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Sample records for predictive control l-mpc

  1. Deadbeat Predictive Controllers

    NASA Technical Reports Server (NTRS)

    Juang, Jer-Nan; Phan, Minh

    1997-01-01

    Several new computational algorithms are presented to compute the deadbeat predictive control law. The first algorithm makes use of a multi-step-ahead output prediction to compute the control law without explicitly calculating the controllability matrix. The system identification must be performed first and then the predictive control law is designed. The second algorithm uses the input and output data directly to compute the feedback law. It combines the system identification and the predictive control law into one formulation. The third algorithm uses an observable-canonical form realization to design the predictive controller. The relationship between all three algorithms is established through the use of the state-space representation. All algorithms are applicable to multi-input, multi-output systems with disturbance inputs. In addition to the feedback terms, feed forward terms may also be added for disturbance inputs if they are measurable. Although the feedforward terms do not influence the stability of the closed-loop feedback law, they enhance the performance of the controlled system.

  2. On identified predictive control

    NASA Technical Reports Server (NTRS)

    Bialasiewicz, Jan T.

    1993-01-01

    Self-tuning control algorithms are potential successors to manually tuned PID controllers traditionally used in process control applications. A very attractive design method for self-tuning controllers, which has been developed over recent years, is the long-range predictive control (LRPC). The success of LRPC is due to its effectiveness with plants of unknown order and dead-time which may be simultaneously nonminimum phase and unstable or have multiple lightly damped poles (as in the case of flexible structures or flexible robot arms). LRPC is a receding horizon strategy and can be, in general terms, summarized as follows. Using assumed long-range (or multi-step) cost function the optimal control law is found in terms of unknown parameters of the predictor model of the process, current input-output sequence, and future reference signal sequence. The common approach is to assume that the input-output process model is known or separately identified and then to find the parameters of the predictor model. Once these are known, the optimal control law determines control signal at the current time t which is applied at the process input and the whole procedure is repeated at the next time instant. Most of the recent research in this field is apparently centered around the LRPC formulation developed by Clarke et al., known as generalized predictive control (GPC). GPC uses ARIMAX/CARIMA model of the process in its input-output formulation. In this paper, the GPC formulation is used but the process predictor model is derived from the state space formulation of the ARIMAX model and is directly identified over the receding horizon, i.e., using current input-output sequence. The underlying technique in the design of identified predictive control (IPC) algorithm is the identification algorithm of observer/Kalman filter Markov parameters developed by Juang et al. at NASA Langley Research Center and successfully applied to identification of flexible structures.

  3. Predicting and Controlling School Violence.

    ERIC Educational Resources Information Center

    Rich, John Martin

    1992-01-01

    Discusses the extent to which violence can be accurately predicted, suggesting interventions, control, and remediation. The educator's role in reducing violence includes dealing with the school, parents, media, and community. Educators need conflict resolution skills for defusing aggression and establishing better relations. (SM)

  4. Inhibitory Control Predicts Grammatical Ability

    PubMed Central

    Ibbotson, Paul; Kearvell-White, Jennifer

    2015-01-01

    We present evidence that individual variation in grammatical ability can be predicted by individual variation in inhibitory control. We tested 81 5-year-olds using two classic tests from linguistics and psychology (Past Tense and the Stroop). Inhibitory control was a better predicator of grammatical ability than either vocabulary or age. Our explanation is that giving the correct response in both tests requires using a common cognitive capacity to inhibit unwanted competition. The implications are that understanding the developmental trajectory of language acquisition can benefit from integrating the developmental trajectory of non-linguistic faculties, such as executive control. PMID:26659926

  5. Adaptive, predictive controller for optimal process control

    SciTech Connect

    Brown, S.K.; Baum, C.C.; Bowling, P.S.; Buescher, K.L.; Hanagandi, V.M.; Hinde, R.F. Jr.; Jones, R.D.; Parkinson, W.J.

    1995-12-01

    One can derive a model for use in a Model Predictive Controller (MPC) from first principles or from experimental data. Until recently, both methods failed for all but the simplest processes. First principles are almost always incomplete and fitting to experimental data fails for dimensions greater than one as well as for non-linear cases. Several authors have suggested the use of a neural network to fit the experimental data to a multi-dimensional and/or non-linear model. Most networks, however, use simple sigmoid functions and backpropagation for fitting. Training of these networks generally requires large amounts of data and, consequently, very long training times. In 1993 we reported on the tuning and optimization of a negative ion source using a special neural network[2]. One of the properties of this network (CNLSnet), a modified radial basis function network, is that it is able to fit data with few basis functions. Another is that its training is linear resulting in guaranteed convergence and rapid training. We found the training to be rapid enough to support real-time control. This work has been extended to incorporate this network into an MPC using the model built by the network for predictive control. This controller has shown some remarkable capabilities in such non-linear applications as continuous stirred exothermic tank reactors and high-purity fractional distillation columns[3]. The controller is able not only to build an appropriate model from operating data but also to thin the network continuously so that the model adapts to changing plant conditions. The controller is discussed as well as its possible use in various of the difficult control problems that face this community.

  6. Broadband Noise Control Using Predictive Techniques

    NASA Technical Reports Server (NTRS)

    Eure, Kenneth W.; Juang, Jer-Nan

    1997-01-01

    Predictive controllers have found applications in a wide range of industrial processes. Two types of such controllers are generalized predictive control and deadbeat control. Recently, deadbeat control has been augmented to include an extended horizon. This modification, named deadbeat predictive control, retains the advantage of guaranteed stability and offers a novel way of control weighting. This paper presents an application of both predictive control techniques to vibration suppression of plate modes. Several system identification routines are presented. Both algorithms are outlined and shown to be useful in the suppression of plate vibrations. Experimental results are given and the algorithms are shown to be applicable to non- minimal phase systems.

  7. Generalized Predictive and Neural Generalized Predictive Control of Aerospace Systems

    NASA Technical Reports Server (NTRS)

    Kelkar, Atul G.

    2000-01-01

    The research work presented in this thesis addresses the problem of robust control of uncertain linear and nonlinear systems using Neural network-based Generalized Predictive Control (NGPC) methodology. A brief overview of predictive control and its comparison with Linear Quadratic (LQ) control is given to emphasize advantages and drawbacks of predictive control methods. It is shown that the Generalized Predictive Control (GPC) methodology overcomes the drawbacks associated with traditional LQ control as well as conventional predictive control methods. It is shown that in spite of the model-based nature of GPC it has good robustness properties being special case of receding horizon control. The conditions for choosing tuning parameters for GPC to ensure closed-loop stability are derived. A neural network-based GPC architecture is proposed for the control of linear and nonlinear uncertain systems. A methodology to account for parametric uncertainty in the system is proposed using on-line training capability of multi-layer neural network. Several simulation examples and results from real-time experiments are given to demonstrate the effectiveness of the proposed methodology.

  8. H ∞ predictive control of networked control systems

    NASA Astrophysics Data System (ADS)

    Xia, Yuanqing; Li, Li; Liu, Guo-Ping; Shi, Peng

    2011-06-01

    This article is concerned with the problem of H ∞ predictive control of networked control system with random network delay. A new control scheme termed networked predictive control is proposed. This scheme mainly consists of the control prediction generator and network delay compensator. While designing the predictor, the control input to the actuator may be different due to networked induced time-delay and data dropout, and two cases are considered depending on the way that the observer obtains the plant control input u t . The necessary and sufficient conditions are given for the closed-loop networked predictive control system to be stochastically stable for different u t and random network delays in controller to actuator channel (CAC) and sensor to controller channel (SCC). A simulation study shows the effectiveness of the proposed scheme.

  9. Predicting and Controlling Complex Networks

    DTIC Science & Technology

    2015-06-22

    networks and control . . . . . . . . . . . . . . . . . . . 7 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically...Ni, Y.-C. Lai, and C. Grebogi, “Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games,” Physical Review E 83...of Physics B 76, 179-183 (2010). 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games Biodiversity is

  10. A Robustly Stabilizing Model Predictive Control Algorithm

    NASA Technical Reports Server (NTRS)

    Ackmece, A. Behcet; Carson, John M., III

    2007-01-01

    A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.

  11. Plasma Stabilization Based on Model Predictive Control

    NASA Astrophysics Data System (ADS)

    Sotnikova, Margarita

    The nonlinear model predictive control algorithms for plasma current and shape stabilization are proposed. Such algorithms are quite suitable for the situations when the plant to be controlled has essentially nonlinear dynamics. Besides that, predictive model based control algorithms allow to take into account a lot of requirements and constraints involved both on the controlled and manipulated variables. The significant drawback of the algorithms is that they require a lot of time to compute control input at each sampling instant. In this paper the model predictive control algorithms are demonstrated by the example of plasma vertical stabilization for ITER-FEAT tokamak. The tuning of parameters of algorithms is performed in order to decrease computational load.

  12. Linear predictive control with state variable constraints

    NASA Astrophysics Data System (ADS)

    Bdirina, K.; Djoudi, D.; Lagoun, M.

    2012-11-01

    While linear model predictive control is popular since the 70s of the past century, the 90s have witnessed a steadily increasing attention from control theoretists as well as control practitioners in the area of model predictive control (MPC). The practical interest is driven by the fact that today's processes need to be operated under tighter performance specifications. At the same time more and more constraints, stemming for example from environmental and safety considerations, need to besatisfied. Often these demands can only be met when process constraints are explicitly considered in the controller. Predictive control with constraints appears to be a well suited approach for this kind of problems. In this paper the basic principle of MPC with constraints is reviewed and some of the theoretical, computational, and implementation aspects of MPC are discussed. Furthermore the MPC with constraints was applied to linear example.

  13. Model predictive control: A new approach

    NASA Astrophysics Data System (ADS)

    Nagy, Endre

    2017-01-01

    New methods are proposed in this paper for solution of the model predictive control problem. Nonlinear state space design techniques are also treated. For nonlinear state prediction (state evolution computation) a new predictor given with an operator is introduced and tested. Settling the model predictive control problem may be obtained through application of the principle "direct stochastic optimum tracking" with a simple algorithm, which can be derived from a previously developed optimization procedure. The final result is obtained through iterations. Two examples show the applicability and advantages of the method.

  14. Fast predictive control of networked energy systems

    NASA Astrophysics Data System (ADS)

    Chuang, Frank Fu-Han

    In this thesis we study the optimal control of networked energy systems. Networked energy systems consist of a collection of energy storage nodes and a network of links and inputs which allow energy to be exchanged, injected, or removed from the nodes. The nodes may exchange energy between each other autonomously or via controlled flows between the nodes. Examples of networked systems include building heating, ventilation, and air conditioning (HVAC) systems and networked battery systems. In the building system example, the nodes of the system are rooms which store thermal energy in the air and other elements which have thermal capacity. The rooms transfer energy autonomously through thermal conduction, convection, and radiation. Thermal energy can be injected into or removed from the rooms via conditioned air or slabs. In the case of a networked battery system, the batteries store electrical energy in their chemical cells. The batteries may be electrically linked so that a controller can move electrical charge from one battery to another. Networked energy systems are typically large-scale (contain many states and inputs), affected by uncertain forecasts and disturbances, and require fast computation on cheap embedded platforms. In this thesis, the optimal control technique we study is model predictive control for networked energy systems. Model predictive or receding horizon control is a time-domain optimization-based control technique which uses predictive models of a system to forecast its behavior and minimize a performance cost subject to system constraints. In this thesis we address two primary issues concerning model predictive control for networked energy systems: robustness to uncertainty in forecasts and reducing the complexity of the large-scale optimization problem for use in embedded platforms. The first half of the thesis deals primarily with the efficient computation of robust controllers for dealing with random and adversarial uncertainties in the

  15. Robust predictive cruise control for commercial vehicles

    NASA Astrophysics Data System (ADS)

    Junell, Jaime; Tumer, Kagan

    2013-10-01

    In this paper we explore learning-based predictive cruise control and the impact of this technology on increasing fuel efficiency for commercial trucks. Traditional cruise control is wasteful when maintaining a constant velocity over rolling hills. Predictive cruise control (PCC) is able to look ahead at future road conditions and solve for a cost-effective course of action. Model- based controllers have been implemented in this field but cannot accommodate many complexities of a dynamic environment which includes changing road and vehicle conditions. In this work, we focus on incorporating a learner into an already successful model- based predictive cruise controller in order to improve its performance. We explore back propagating neural networks to predict future errors then take actions to prevent said errors from occurring. The results show that this approach improves the model based PCC by up to 60% under certain conditions. In addition, we explore the benefits of classifier ensembles to further improve the gains due to intelligent cruise control.

  16. Model predictive formation control of helicopter systems

    NASA Astrophysics Data System (ADS)

    Saffarian, Mehdi

    In this thesis, a robust formation control framework for formation control of a group of helicopters is proposed and designed. The dynamic model of the helicopter has been developed and verified through simulations. The control framework is constructed using two main control schemes for navigation of a helicopter group in three-dimensional (3D) environments. Two schemes are designed to maintain the position of one helicopter with respect to one or two other neighboring members, respectively. The developed parameters can uniquely define the position of the helicopters with respect to each other and can be used for any other aerial and under water vehicles such as airplanes, spacecrafts and submarines. Also, since this approach is modular, it is possible to use it for desired number and form of the group helicopters. Using the defined control parameters, two decentralized controllers are designed based on Nonlinear Model Predictive Control (NMPC) algorithm technique. The framework performance has been tested through simulation of different formation scenarios.

  17. Model predictive control for cooperative control of space robots

    NASA Astrophysics Data System (ADS)

    Kannan, Somasundar; Alamdari, Seyed Amin Sajadi; Dentler, Jan; Olivares-Mendez, Miguel A.; Voos, Holger

    2017-01-01

    The problem of Orbital Manipulation of Passive body is discussed here. Two scenarios including passive object rigidly attached to robotic servicers and passive body attached to servicers through manipulators are discussed. The Model Predictive Control (MPC) technique is briefly presented and successfully tested through simulations on two cases of position control of passive body in the orbit.

  18. Model Predictive Control of Sewer Networks

    NASA Astrophysics Data System (ADS)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.

    2017-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.

  19. Predictive Control of Large Complex Networks

    NASA Astrophysics Data System (ADS)

    Haber, Aleksandar; Motter, Adilson E.

    Networks of coupled dynamical subsystems are increasingly used to represent complex natural and engineered systems. While recent technological developments give us improved means to actively control the dynamics of individual subsystems in various domains, network control remains a challenging problem due to difficulties imposed by intrinsic nonlinearities, control constraints, and the large-scale nature of the systems. In this talk, we will present a model predictive control approach that is effective while accounting for these realistic properties of complex networks. Our method can systematically identify control interventions that steer the trajectory to a desired state, even in the presence of strong nonlinearities and constraints. Numerical tests show that the method is applicable to a variety of networks, ranging from power grids to chemical reaction systems.

  20. Model predictive control of MSMPR crystallizers

    NASA Astrophysics Data System (ADS)

    Moldoványi, Nóra; Lakatos, Béla G.; Szeifert, Ferenc

    2005-02-01

    A multi-input-multi-output (MIMO) control problem of isothermal continuous crystallizers is addressed in order to create an adequate model-based control system. The moment equation model of mixed suspension, mixed product removal (MSMPR) crystallizers that forms a dynamical system is used, the state of which is represented by the vector of six variables: the first four leading moments of the crystal size, solute concentration and solvent concentration. Hence, the time evolution of the system occurs in a bounded region of the six-dimensional phase space. The controlled variables are the mean size of the grain; the crystal size-distribution and the manipulated variables are the input concentration of the solute and the flow rate. The controllability and observability as well as the coupling between the inputs and the outputs was analyzed by simulation using the linearized model. It is shown that the crystallizer is a nonlinear MIMO system with strong coupling between the state variables. Considering the possibilities of the model reduction, a third-order model was found quite adequate for the model estimation in model predictive control (MPC). The mean crystal size and the variance of the size distribution can be nearly separately controlled by the residence time and the inlet solute concentration, respectively. By seeding, the controllability of the crystallizer increases significantly, and the overshoots and the oscillations become smaller. The results of the controlling study have shown that the linear MPC is an adaptable and feasible controller of continuous crystallizers.

  1. Robust model predictive control of Wiener systems

    NASA Astrophysics Data System (ADS)

    Biagiola, S. I.; Figueroa, J. L.

    2011-03-01

    Block-oriented models (BOMs) have shown to be appealing and efficient as nonlinear representations for many applications. They are at the same time valid and simple models in a more extensive region than time-invariant linear models. In this work, Wiener models are considered. They are one of the most diffused BOMs, and their structure consists in a linear dynamics in cascade with a nonlinear static block. Particularly, the problem of control of these systems in the presence of uncertainty is treated. The proposed methodology makes use of a robust identification procedure in order to obtain a robust model to represent the uncertain system. This model is then employed to design a model predictive controller. The mathematical problem involved in the controller design is formulated in the context of the existing linear matrix inequalities (LMI) theory. The main feature of this approach is that it takes advantage of the static nature of the nonlinearity, which allows to solve the control problem by focusing only in the linear dynamics. This formulation results in a simplified design procedure, because the original nonlinear model predictive control (MPC) problem turns into a linear one.

  2. Wind farms production: Control and prediction

    NASA Astrophysics Data System (ADS)

    El-Fouly, Tarek Hussein Mostafa

    Wind energy resources, unlike dispatchable central station generation, produce power dependable on external irregular source and that is the incident wind speed which does not always blow when electricity is needed. This results in the variability, unpredictability, and uncertainty of wind resources. Therefore, the integration of wind facilities to utility electrical grid presents a major challenge to power system operator. Such integration has significant impact on the optimum power flow, transmission congestion, power quality issues, system stability, load dispatch, and economic analysis. Due to the irregular nature of wind power production, accurate prediction represents the major challenge to power system operators. Therefore, in this thesis two novel models are proposed for wind speed and wind power prediction. One proposed model is dedicated to short-term prediction (one-hour ahead) and the other involves medium term prediction (one-day ahead). The accuracy of the proposed models is revealed by comparing their results with the corresponding values of a reference prediction model referred to as the persistent model. Utility grid operation is not only impacted by the uncertainty of the future production of wind farms, but also by the variability of their current production and how the active and reactive power exchange with the grid is controlled. To address this particular task, a control technique for wind turbines, driven by doubly-fed induction generators (DFIGs), is developed to regulate the terminal voltage by equally sharing the generated/absorbed reactive power between the rotor-side and the gridside converters. To highlight the impact of the new developed technique in reducing the power loss in the generator set, an economic analysis is carried out. Moreover, a new aggregated model for wind farms is proposed that accounts for the irregularity of the incident wind distribution throughout the farm layout. Specifically, this model includes the wake effect

  3. Real-time Adaptive Control Using Neural Generalized Predictive Control

    NASA Technical Reports Server (NTRS)

    Haley, Pam; Soloway, Don; Gold, Brian

    1999-01-01

    The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.

  4. Optimal Control of Distributed Energy Resources using Model Predictive Control

    SciTech Connect

    Mayhorn, Ebony T.; Kalsi, Karanjit; Elizondo, Marcelo A.; Zhang, Wei; Lu, Shuai; Samaan, Nader A.; Butler-Purry, Karen

    2012-07-22

    In an isolated power system (rural microgrid), Distributed Energy Resources (DERs) such as renewable energy resources (wind, solar), energy storage and demand response can be used to complement fossil fueled generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation. The problem is formulated as a multi-objective optimization problem with the goals of minimizing fuel costs and changes in power output of diesel generators, minimizing costs associated with low battery life of energy storage and maintaining system frequency at the nominal operating value. Two control modes are considered for controlling the energy storage to compensate either net load variability or wind variability. Model predictive control (MPC) is used to solve the aforementioned problem and the performance is compared to an open-loop look-ahead dispatch problem. Simulation studies using high and low wind profiles, as well as, different MPC prediction horizons demonstrate the efficacy of the closed-loop MPC in compensating for uncertainties in wind and demand.

  5. Cascade generalized predictive control strategy for boiler drum level.

    PubMed

    Xu, Min; Li, Shaoyuan; Cai, Wenjian

    2005-07-01

    This paper proposes a cascade model predictive control scheme for boiler drum level control. By employing generalized predictive control structures for both inner and outer loops, measured and unmeasured disturbances can be effectively rejected, and drum level at constant load is maintained. In addition, nonminimum phase characteristic and system constraints in both loops can be handled effectively by generalized predictive control algorithms. Simulation results are provided to show that cascade generalized predictive control results in better performance than that of well tuned cascade proportional integral differential controllers. The algorithm has also been implemented to control a 75-MW boiler plant, and the results show an improvement over conventional control schemes.

  6. Nonconvex model predictive control for commercial refrigeration

    NASA Astrophysics Data System (ADS)

    Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John

    2013-08-01

    We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.

  7. Predictive Control of Networked Multiagent Systems via Cloud Computing.

    PubMed

    Liu, Guo-Ping

    2017-01-18

    This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

  8. A method to predict circulation control noise

    NASA Astrophysics Data System (ADS)

    Reger, Robert W.

    Underwater vehicles suffer from reduced maneuverability with conventional lifting append-\\ ages due to the low velocity of operation. Circulation control offers a method to increase maneuverability independent of vehicle speed. However, with circulation control comes additional noise sources, which are not well understood. To better understand these noise sources, a modal-based prediction method is developed, potentially offering a quantitative connection between flow structures and far-field noise. This method involves estimation of the velocity field, surface pressure field, and far-field noise, using only non-time-resolved velocity fields and time-resolved probe measurements. Proper orthogonal decomposition, linear stochastic estimation and Kalman smoothing are employed to estimate time-resolved velocity fields. Poisson's equation is used to calculate time-resolved pressure fields from velocity. Curle's analogy is then used to propagate the surface pressure forces to the far field. This method is developed on a direct numerical simulation of a two-dimensional cylinder at a low Reynolds number (150). Since each of the fields to be estimated are also known from the simulation, a means of obtaining the error from using the methodology is provided. The velocity estimation and the simulated velocity match well when the simulated additive measurement noise is low. The pressure field suffers due to a small domain size; however, the surface pressures estimates fare much better. The far-field estimation contains similar frequency content with reduced magnitudes, attributed to the exclusion of the viscous forces in Curle's analogy. In the absence of added noise, the estimation procedure performs quite nicely for this model problem. The method is tested experimentally on a 650,000 chord-Reynolds-number flow over a 2-D, 20% thick, elliptic circulation control airfoil. Slot jet momentum coefficients of 0 and 0.10 are investigated. Particle image velocimetry, unsteady

  9. Predicting Loss-of-Control Boundaries Toward a Piloting Aid

    NASA Technical Reports Server (NTRS)

    Barlow, Jonathan; Stepanyan, Vahram; Krishnakumar, Kalmanje

    2012-01-01

    This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.

  10. Experimental results of a predictive neural network HVAC controller

    SciTech Connect

    Jeannette, E.; Assawamartbunlue, K.; Kreider, J.F.; Curtiss, P.S.

    1998-12-31

    Proportional, integral, and derivative (PID) control is widely used in many HVAC control processes and requires constant attention for optimal control. Artificial neural networks offer the potential for improved control of processes through predictive techniques. This paper introduces and shows experimental results of a predictive neural network (PNN) controller applied to an unstable hot water system in an air-handling unit. Actual laboratory testing of the PNN and PID controllers show favorable results for the PNN controller.

  11. Predictive and Neural Predictive Control of Uncertain Systems

    NASA Technical Reports Server (NTRS)

    Kelkar, Atul G.

    2000-01-01

    Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.

  12. Sequential Prediction for Information Fusion and Control

    DTIC Science & Technology

    2013-10-14

    paradigms , and external feedback mechanisms. Online prediction and targeted collection of information is an emerging paradigm at the intersection of...unknown, environmental dynamics, potentially stemming from an adversary who reacts to sensing actions, active sensing paradigms , and external feedback mech...anisms. Online prediction and targeted collection of information is an emerging paradigm at the inter- section of optimization, machine learning and

  13. Voltage control in pulsed system by predict-ahead control

    DOEpatents

    Payne, A.N.; Watson, J.A.; Sampayan, S.E.

    1994-09-13

    A method and apparatus for predict-ahead pulse-to-pulse voltage control in a pulsed power supply system is disclosed. A DC power supply network is coupled to a resonant charging network via a first switch. The resonant charging network is coupled at a node to a storage capacitor. An output load is coupled to the storage capacitor via a second switch. A de-Q-ing network is coupled to the resonant charging network via a third switch. The trigger for the third switch is a derived function of the initial voltage of the power supply network, the initial voltage of the storage capacitor, and the present voltage of the storage capacitor. A first trigger closes the first switch and charges the capacitor. The third trigger is asserted according to the derived function to close the third switch. When the third switch is closed, the first switch opens and voltage on the node is regulated. The second trigger may be thereafter asserted to discharge the capacitor into the output load. 4 figs.

  14. Voltage control in pulsed system by predict-ahead control

    DOEpatents

    Payne, Anthony N.; Watson, James A.; Sampayan, Stephen E.

    1994-01-01

    A method and apparatus for predict-ahead pulse-to-pulse voltage control in a pulsed power supply system is disclosed. A DC power supply network is coupled to a resonant charging network via a first switch. The resonant charging network is coupled at a node to a storage capacitor. An output load is coupled to the storage capacitor via a second switch. A de-Q-ing network is coupled to the resonant charging network via a third switch. The trigger for the third switch is a derived function of the initial voltage of the power supply network, the initial voltage of the storage capacitor, and the present voltage of the storage capacitor. A first trigger closes the first switch and charges the capacitor. The third trigger is asserted according to the derived function to close the third switch. When the third switch is closed, the first switch opens and voltage on the node is regulated. The second trigger may be thereafter asserted to discharge the capacitor into the output load.

  15. Networked Robust Predictive Control Systems Design with Packet Loss

    NASA Astrophysics Data System (ADS)

    Nguyen, Quang T.; Veselý, Vojtech; Kozáková, Alena; Pakshin, Pavel

    2014-01-01

    The paper addresses problem of designing a robust output feedback model predictive control for uncertain linear systems over networks with packet-loss. The packet-loss process is arbitrary and bounded by the control horizon of model predictive control. Networked predictive control systems with packet loss are modeled as switched linear systems. This enables us to apply the theory of switched systems to establish the stability condition. The stabilizing controller design is based on sufficient robust stability conditions formulated as a solution of bilinear matrix inequality. Finally, a benchmark numerical example-double integrator is given to illustrate the effectiveness of the proposed method.

  16. Consensus and Stability Analysis of Networked Multiagent Predictive Control Systems.

    PubMed

    Liu, Guo-Ping

    2016-03-17

    This paper is concerned with the consensus and stability problem of multiagent control systems via networks with communication delays and data loss. A networked multiagent predictive control scheme is proposed to achieve output consensus and also compensate for the communication delays and data loss actively. The necessary and sufficient conditions of achieving both consensus and stability of the closed-loop networked multiagent control systems are derived. An important result that is obtained is that the consensus and stability of closed-loop networked multiagent predictive control systems are not related to the communication delays and data loss. An example illustrates the performance of the networked multiagent predictive control scheme.

  17. Comparison of predictive control methods for high consumption industrial furnace.

    PubMed

    Stojanovski, Goran; Stankovski, Mile

    2013-01-01

    We describe several predictive control approaches for high consumption industrial furnace control. These furnaces are major consumers in production industries, and reducing their fuel consumption and optimizing the quality of the products is one of the most important engineer tasks. In order to demonstrate the benefits from implementation of the advanced predictive control algorithms, we have compared several major criteria for furnace control. On the basis of the analysis, some important conclusions have been drawn.

  18. Multiplexed Predictive Control of a Large Commercial Turbofan Engine

    NASA Technical Reports Server (NTRS)

    Richter, hanz; Singaraju, Anil; Litt, Jonathan S.

    2008-01-01

    Model predictive control is a strategy well-suited to handle the highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. However, it has thus far been infeasible to implement model predictive control in engine control applications, because of the combination of model complexity and the time allotted for the control update calculation. In this paper, a multiplexed implementation is proposed that dramatically reduces the computational burden of the quadratic programming optimization that must be solved online as part of the model-predictive-control algorithm. Actuator updates are calculated sequentially and cyclically in a multiplexed implementation, as opposed to the simultaneous optimization taking place in conventional model predictive control. Theoretical aspects are discussed based on a nominal model, and actual computational savings are demonstrated using a realistic commercial engine model.

  19. Model Predictive Control for Nonlinear Parabolic Partial Differential Equations

    NASA Astrophysics Data System (ADS)

    Hashimoto, Tomoaki; Yoshioka, Yusuke; Ohtsuka, Toshiyuki

    In this study, the optimal control problem of nonlinear parabolic partial differential equations (PDEs) is investigated. Optimal control of nonlinear PDEs is an open problem with applications that include fluid, thermal, biological, and chemically-reacting systems. Model predictive control with a fast numerical solution method has been well established to solve the optimal control problem of nonlinear systems described by ordinary differential equations. In this study, we develop a design method of the model predictive control for nonlinear systems described by parabolic PDEs. Our approach is a direct infinite dimensional extension of the model predictive control method for finite-dimensional systems. The objective of this paper is to develop an efficient algorithm for numerically solving the model predictive control problem of nonlinear parabolic PDEs. The effectiveness of the proposed method is verified by numerical simulations.

  20. Model-Based Predictive Control of Turbulent Channel Flow

    NASA Astrophysics Data System (ADS)

    Kellogg, Steven M.; Collis, S. Scott

    1999-11-01

    In recent simulations of optimal turbulence control, the time horizon over which the control is determined matches the time horizon over which the flow is advanced. A popular workhorse of the controls community, Model-Based Predictive Control (MBPC), suggests using longer predictive horizons than advancement windows. Including additional time information in the optimization may generate improved controls. When the advancement horizon is smaller than the predictive horizon, part of the optimization and resulting control are discarded. Although this inherent inefficiency may be justified by improved control predictions, it has hampered prior investigations of MBPC for turbulent flow due to the expense associated with optimal control based on Direct Numerical Simulation. The current approach overcomes this by using our optimal control formulation based on Large Eddy Simulation. This presentation summarizes the results of optimal control simulations for turbulent channel flow using various ratios of advancement and predictive horizons. These results provide clues as to the roles of foresight, control history, cost functional, and turbulence structures for optimal control of wall-bounded turbulence.

  1. Prospects for earthquake prediction and control

    USGS Publications Warehouse

    Healy, J.H.; Lee, W.H.K.; Pakiser, L.C.; Raleigh, C.B.; Wood, M.D.

    1972-01-01

    The San Andreas fault is viewed, according to the concepts of seafloor spreading and plate tectonics, as a transform fault that separates the Pacific and North American plates and along which relative movements of 2 to 6 cm/year have been taking place. The resulting strain can be released by creep, by earthquakes of moderate size, or (as near San Francisco and Los Angeles) by great earthquakes. Microearthquakes, as mapped by a dense seismograph network in central California, generally coincide with zones of the San Andreas fault system that are creeping. Microearthquakes are few and scattered in zones where elastic energy is being stored. Changes in the rate of strain, as recorded by tiltmeter arrays, have been observed before several earthquakes of about magnitude 4. Changes in fluid pressure may control timing of seismic activity and make it possible to control natural earthquakes by controlling variations in fluid pressure in fault zones. An experiment in earthquake control is underway at the Rangely oil field in Colorado, where the rates of fluid injection and withdrawal in experimental wells are being controlled. ?? 1972.

  2. Launch ascent guidance by discrete multi-model predictive control

    NASA Astrophysics Data System (ADS)

    Vachon, Alexandre; Desbiens, André; Gagnon, Eric; Bérard, Caroline

    2014-02-01

    This paper studies the application of discrete multi-model predictive control as a trajectory tracking guidance law for a space launcher. Two different algorithms are developed, each one based on a different representation of launcher translation dynamics. These representations are based on an interpolation of the linear approximation of nonlinear pseudo-five degrees of freedom equations of translation around an elliptical Earth. The interpolation gives a linear-time-varying representation and a linear-fractional representation. They are used as the predictive model of multi-model predictive controllers. The controlled variables are the orbital parameters, and constraints on a terminal region for the minimal accepted precision are also included. Use of orbital parameters as the controlled variables allows for a partial definition of the trajectory. Constraints can also be included in multi-model predictive control to reduce the number of unknowns of the problem by defining input shaping constraints. The guidance algorithms are tested in nominal conditions and off-nominal conditions with uncertainties on the thrust. The results are compared to those of a similar formulation with a nonlinear model predictive controller and to a guidance method based on the resolution of a simplified version of the two-point boundary value problem. In nominal conditions, the model predictive controllers are more precise and produce a more optimal trajectory but are longer to compute than the two-point boundary solution. Moreover, in presence of uncertainties, developed algorithms exhibit poor robustness properties. The multi-model predictive control algorithms do not reach the desired orbit while the nonlinear model predictive control algorithm still converges but produces larger maneuvers than the other method.

  3. Model predictive control of P-time event graphs

    NASA Astrophysics Data System (ADS)

    Hamri, H.; Kara, R.; Amari, S.

    2016-12-01

    This paper deals with model predictive control of discrete event systems modelled by P-time event graphs. First, the model is obtained by using the dater evolution model written in the standard algebra. Then, for the control law, we used the finite-horizon model predictive control. For the closed-loop control, we used the infinite-horizon model predictive control (IH-MPC). The latter is an approach that calculates static feedback gains which allows the stability of the closed-loop system while respecting the constraints on the control vector. The problem of IH-MPC is formulated as a linear convex programming subject to a linear matrix inequality problem. Finally, the proposed methodology is applied to a transportation system.

  4. Rate-Based Model Predictive Control of Turbofan Engine Clearance

    NASA Technical Reports Server (NTRS)

    DeCastro, Jonathan A.

    2006-01-01

    An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.

  5. Pilots Rate Augmented Generalized Predictive Control for Reconfiguration

    NASA Technical Reports Server (NTRS)

    Soloway, Don; Haley, Pam

    2004-01-01

    The objective of this paper is to report the results from the research being conducted in reconfigurable fight controls at NASA Ames. A study was conducted with three NASA Dryden test pilots to evaluate two approaches of reconfiguring an aircraft's control system when failures occur in the control surfaces and engine. NASA Ames is investigating both a Neural Generalized Predictive Control scheme and a Neural Network based Dynamic Inverse controller. This paper highlights the Predictive Control scheme where a simple augmentation to reduce zero steady-state error led to the neural network predictor model becoming redundant for the task. Instead of using a neural network predictor model, a nominal single point linear model was used and then augmented with an error corrector. This paper shows that the Generalized Predictive Controller and the Dynamic Inverse Neural Network controller perform equally well at reconfiguration, but with less rate requirements from the actuators. Also presented are the pilot ratings for each controller for various failure scenarios and two samples of the required control actuation during reconfiguration. Finally, the paper concludes by stepping through the Generalized Predictive Control's reconfiguration process for an elevator failure.

  6. Switched linear model predictive controllers for periodic exogenous signals

    NASA Astrophysics Data System (ADS)

    Wang, Liuping; Gawthrop, Peter; Owens, David. H.; Rogers, Eric

    2010-04-01

    This article develops switched linear controllers for periodic exogenous signals using the framework of a continuous-time model predictive control. In this framework, the control signal is generated by an algorithm that uses receding horizon control principle with an on-line optimisation scheme that permits inclusion of operational constraints. Unlike traditional repetitive controllers, applying this method in the form of switched linear controllers ensures bumpless transfer from one controller to another. Simulation studies are included to demonstrate the efficacy of the design with or without hard constraints.

  7. Model and Predictive Control for a Wind Turbine

    NASA Astrophysics Data System (ADS)

    Gilev, B.; Slavchev, J.; Penev, D.; Yonchev, A.

    2011-12-01

    A mathematical model of the system consisting of wind turbine, gear box and asynchronous generator is presented in this work. The model is linearized. Then a controller, which provides a desire mode of frequency stabilization, is developed using the predictive control theory.

  8. Towards feasible and effective predictive wavefront control for adaptive optics

    SciTech Connect

    Poyneer, L A; Veran, J

    2008-06-04

    We have recently proposed Predictive Fourier Control, a computationally efficient and adaptive algorithm for predictive wavefront control that assumes frozen flow turbulence. We summarize refinements to the state-space model that allow operation with arbitrary computational delays and reduce the computational cost of solving for new control. We present initial atmospheric characterization using observations with Gemini North's Altair AO system. These observations, taken over 1 year, indicate that frozen flow is exists, contains substantial power, and is strongly detected 94% of the time.

  9. Optimal Tuning for Disturbance Suppression Mechanism for Model Predictive Control

    NASA Astrophysics Data System (ADS)

    Tange, Yoshio; Nakazawa, Chikashi

    Disturbance suppression is one of most required performances in process control. We recently proposed a new disturbance suppression mechanism applicable for model predictive control in order to enhance disturbance suppression performance for ramp-like disturbances. The proposed method utilized the prediction error of controlled values and generates a disturbance compensation signal by a constant gain feedback. In this paper, we propose an improved version of the disturbance suppression mechanism by applying a low-pass filter and parameter tuning methods by which we can make the mechanism more tolerant to various disturbances such as ramp, step, and other supposable ones. We also show numerical simulation results with an oil distillation tower plant.

  10. Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

    PubMed Central

    Mehrabi, Naser; Sharif Razavian, Reza; Ghannadi, Borna; McPhee, John

    2017-01-01

    This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization. PMID:28133449

  11. Predictive neuro-fuzzy controller for multilink robot manipulator

    NASA Astrophysics Data System (ADS)

    Kaymaz, Emre; Mitra, Sunanda

    1995-10-01

    A generalized controller based on fuzzy clustering and fuzzy generalized predictive control has been developed for nonlinear systems including multilink robot manipulators. The proposed controller is particularly useful when the dynamics of the nonlinear system to be controlled are difficult to yield exact solutions and the system specification can be obtained in terms of crisp input-output pairs. It inherits the advantages of both fuzzy logic and predictive control. The identification of the nonlinear mapping of the system to be controlled is realized by a three- layer feed-forward neural network model employing the input-output data obtained from the system. The speed of convergence of the neural network is improved by the introduction of a fuzzy logic controlled backpropagation learning algorithm. The neural network model is then used as a simulation tool to generate the input-output data for developing the predictive fuzzy logic controller for the chosen nonlinear system. The use of fuzzy clustering facilitates automatic generation of membership relations of the input-output data. Unlike the linguistic fuzzy logic controller which requires approximate knowledge of the shape and the numbers of the membership functions in the input and output universes of the discourse, this integrated neuro-fuzzy approach allows one to find the fuzzy relations and the membership functions more accurately. Furthermore, it is not necessary to tune the controller. For a two-link robot manipulator, the performance of this predictive fuzzy controller is shown to be superior to that of a conventional controller employing an ARMA model of the system in terms of accuracy and consumption of energy.

  12. Improving active space telescope wavefront control using predictive thermal modeling

    NASA Astrophysics Data System (ADS)

    Gersh-Range, Jessica; Perrin, Marshall D.

    2015-01-01

    Active control algorithms for space telescopes are less mature than those for large ground telescopes due to differences in the wavefront control problems. Active wavefront control for space telescopes at L2, such as the James Webb Space Telescope (JWST), requires weighing control costs against the benefits of correcting wavefront perturbations that are a predictable byproduct of the observing schedule, which is known and determined in advance. To improve the control algorithms for these telescopes, we have developed a model that calculates the temperature and wavefront evolution during a hypothetical mission, assuming the dominant wavefront perturbations are due to changes in the spacecraft attitude with respect to the sun. Using this model, we show that the wavefront can be controlled passively by introducing scheduling constraints that limit the allowable attitudes for an observation based on the observation duration and the mean telescope temperature. We also describe the implementation of a predictive controller designed to prevent the wavefront error (WFE) from exceeding a desired threshold. This controller outperforms simpler algorithms even with substantial model error, achieving a lower WFE without requiring significantly more corrections. Consequently, predictive wavefront control based on known spacecraft attitude plans is a promising approach for JWST and other future active space observatories.

  13. Semiconductor CMP Process Control Predicting Degradation Effect of Consumed Materials

    NASA Astrophysics Data System (ADS)

    Tamaki, Kenji; Kaneko, Shun'ichi

    This paper describes a methodology to build a virtual metrology (VM) model for semiconductor chemical mechanical polishing (CMP) process control. The VM model predicts the polishing rate based on equipment-derived data as soon as allowed, and immediately applies the results to advanced process control (APC). The proposed methodology uses Markov chain Monte Carlo (MCMC) methods to build an analytical model with many parameters for individual consumed materials from historical data in small quantities. The mutual interference of two kinds of consumed materials: dresser and pad are modeled in a form of multilevel predictive model. The methodology uses MCMC methods again to identify the multilevel predictive model taking into account the assumed operation of an actual manufacturing line, for instance, using preliminary test result, learning a model parameter online, and being affected by metrology lag as disturbance. The simulation results show the APC with the proposed VM model is low sensitivity to metrology lag and high precision on polishing amount control.

  14. Prediction and control of limit cycling motions in boosting rockets

    NASA Astrophysics Data System (ADS)

    Newman, Brett

    An investigation concerning the prediction and control of observed limit cycling behavior in a boosting rocket is considered. The suspected source of the nonlinear behavior is the presence of Coulomb friction in the nozzle pivot mechanism. A classical sinusoidal describing function analysis is used to accurately recreate and predict the observed oscillatory characteristic. In so doing, insight is offered into the limit cycling mechanism and confidence is gained in the closed-loop system design. Nonlinear simulation results are further used to support and verify the results obtained from describing function theory. Insight into the limit cycling behavior is, in turn, used to adjust control system parameters in order to passively control the oscillatory tendencies. Tradeoffs with the guidance and control system stability/performance are also noted. Finally, active control of the limit cycling behavior, using a novel feedback algorithm to adjust the inherent nozzle sticking-unsticking characteristics, is considered.

  15. Optimized continuous pharmaceutical manufacturing via model-predictive control.

    PubMed

    Rehrl, Jakob; Kruisz, Julia; Sacher, Stephan; Khinast, Johannes; Horn, Martin

    2016-08-20

    This paper demonstrates the application of model-predictive control to a feeding blending unit used in continuous pharmaceutical manufacturing. The goal of this contribution is, on the one hand, to highlight the advantages of the proposed concept compared to conventional PI-controllers, and, on the other hand, to present a step-by-step guide for controller synthesis. The derivation of the required mathematical plant model is given in detail and all the steps required to develop a model-predictive controller are shown. Compared to conventional concepts, the proposed approach allows to conveniently consider constraints (e.g. mass hold-up in the blender) and offers a straightforward, easy to tune controller setup. The concept is implemented in a simulation environment. In order to realize it on a real system, additional aspects (e.g., state estimation, measurement equipment) will have to be investigated.

  16. Predictive Feedback and Feedforward Control for Systems with Unknown Disturbances

    NASA Technical Reports Server (NTRS)

    Juang, Jer-Nan; Eure, Kenneth W.

    1998-01-01

    Predictive feedback control has been successfully used in the regulation of plate vibrations when no reference signal is available for feedforward control. However, if a reference signal is available it may be used to enhance regulation by incorporating a feedforward path in the feedback controller. Such a controller is known as a hybrid controller. This paper presents the theory and implementation of the hybrid controller for general linear systems, in particular for structural vibration induced by acoustic noise. The generalized predictive control is extended to include a feedforward path in the multi-input multi-output case and implemented on a single-input single-output test plant to achieve plate vibration regulation. There are cases in acoustic-induce vibration where the disturbance signal is not available to be used by the hybrid controller, but a disturbance model is available. In this case the disturbance model may be used in the feedback controller to enhance performance. In practice, however, neither the disturbance signal nor the disturbance model is available. This paper presents the theory of identifying and incorporating the noise model into the feedback controller. Implementations are performed on a test plant and regulation improvements over the case where no noise model is used are demonstrated.

  17. Nonlinear Model Predictive Control with Constraint Satisfactions for a Quadcopter

    NASA Astrophysics Data System (ADS)

    Wang, Ye; Ramirez-Jaime, Andres; Xu, Feng; Puig, Vicenç

    2017-01-01

    This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of full dynamics can be transformed into the inequality constraints. Subsequently, the quadcopter can be controlled by an NMPC controller with the updated constraints generated by constraint satisfactions. Finally, the simulation results applied to a quadcopter simulator are provided to show the effectiveness of the proposed strategy.

  18. Predicted torque equilibrium attitude utilization for Space Station attitude control

    NASA Technical Reports Server (NTRS)

    Kumar, Renjith R.; Heck, Michael L.; Robertson, Brent P.

    1990-01-01

    An approximate knowledge of the torque equilibrium attitude (TEA) is shown to improve the performance of a control moment gyroscope (CMG) momentum management/attitude control law for Space Station Freedom. The linearized equations of motion are used in conjunction with a state transformation to obtain a control law which uses full state feedback and the predicted TEA to minimize both attitude excursions and CMG peak and secular momentum. The TEA can be computationally determined either by observing the steady state attitude of a 'controlled' spacecraft using arbitrary initial attitude, or by simulating a fixed attitude spacecraft flying in desired orbit subject to realistic environmental disturbance models.

  19. Neural Generalized Predictive Control: A Newton-Raphson Implementation

    NASA Technical Reports Server (NTRS)

    Soloway, Donald; Haley, Pamela J.

    1997-01-01

    An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant's nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm's implementation are also included.

  20. Fuzzy Predictive Control Strategy in the Application of the Industrial Furnace Temperature Control

    NASA Astrophysics Data System (ADS)

    Dai, Luping; Chen, Xingliang; Chen, Liu; Liu, Xia

    Ceramic kiln with large heat capacity, big lag and nonlinear characteristic, this paper proposes a combining fuzzy control and predictive control of the control algorithm, to enhance the tracking and anti-interference ability of the algorithm. The simulation results show, this method compared with the control of PID has the high steady precision and dynamic characteristic.

  1. The predictive protective control of the heat exchanger

    NASA Astrophysics Data System (ADS)

    Nevriva, Pavel; Filipova, Blanka; Vilimec, Ladislav

    2016-06-01

    The paper deals with the predictive control applied to flexible cogeneration energy system FES. FES was designed and developed by the VITKOVICE POWER ENGINEERING joint-stock company and represents a new solution of decentralized cogeneration energy sources. In FES, the heating medium is flue gas generated by combustion of a solid fuel. The heated medium is power gas, which is a gas mixture of air and water steam. Power gas is superheated in the main heat exchanger and led to gas turbines. To protect the main heat exchanger against damage by overheating, the novel predictive protective control based on the mathematical model of exchanger was developed. The paper describes the principle, the design and the simulation of the predictive protective method applied to main heat exchanger of FES.

  2. Prediction of active control of subsonic centrifugal compressor rotating stall

    NASA Technical Reports Server (NTRS)

    Lawless, Patrick B.; Fleeter, Sanford

    1993-01-01

    A mathematical model is developed to predict the suppression of rotating stall in a centrifugal compressor with a vaned diffuser. This model is based on the employment of a control vortical waveform generated upstream of the impeller inlet to damp weak potential disturbances that are the early stages of rotating stall. The control system is analyzed by matching the perturbation pressure in the compressor inlet and exit flow fields with a model for the unsteady behavior of the compressor. The model was effective at predicting the stalling behavior of the Purdue Low Speed Centrifugal Compressor for two distinctly different stall patterns. Predictions made for the effect of a controlled inlet vorticity wave on the stability of the compressor show that for minimum control wave magnitudes, on the order of the total inlet disturbance magnitude, significant damping of the instability can be achieved. For control waves of sufficient amplitude, the control phase angle appears to be the most important factor in maintaining a stable condition in the compressor.

  3. Composite predictive flight control for airbreathing hypersonic vehicles

    NASA Astrophysics Data System (ADS)

    Yang, Jun; Zhao, Zhenhua; Li, Shihua; Zheng, Wei Xing

    2014-09-01

    The robust optimised tracking control problem for a generic airbreathing hypersonic vehicle (AHV) subject to nonvanishing mismatched disturbances/uncertainties is investigated in this paper. A baseline nonlinear model predictive control (MPC) method is firstly introduced for optimised tracking control of the nominal dynamics. A nonlinear-disturbance-observer-based control law is then developed for robustness enhancement in the presence of both external disturbances and uncertainties. Compared with the existing robust tracking control methods for AHVs, the proposed composite nonlinear MPC method obtains not only promising robustness and disturbance rejection performance but also optimised nominal tracking control performance. The merits of the proposed method are validated by implementing simulation studies on the AHV system.

  4. Adaptive model predictive process control using neural networks

    DOEpatents

    Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.

    1997-01-01

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.

  5. Adaptive model predictive process control using neural networks

    DOEpatents

    Buescher, K.L.; Baum, C.C.; Jones, R.D.

    1997-08-19

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

  6. Implementation of model predictive control on a hydrothermal oxidation reactor

    SciTech Connect

    Muske, K.R.; Dell`Orco, P.C.; Le, L.A.; Flesner, R.L.

    1998-12-31

    This paper describes the model-based control algorithm developed for a hydrothermal oxidation reactor at the Pantex Department of Energy facility in Amarillo, Texas. The combination of base hydrolysis and hydrothermal oxidation is used for the disposal of PBX 9404 high explosive at Pantex. The reactor oxidizes the organic compounds in the hydrolysate solutions obtained from the base hydrolysis process. The objective of the model predictive controller is to minimize the total aqueous nitrogen compounds in the effluent of the reactor. The controller also maintains a desired excess oxygen concentration in the reactor effluent to ensure the complete destruction of the organic carbon compounds in the hydrolysate.

  7. Health-aware Model Predictive Control of Pasteurization Plant

    NASA Astrophysics Data System (ADS)

    Karimi Pour, Fatemeh; Puig, Vicenç; Ocampo-Martinez, Carlos

    2017-01-01

    In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.

  8. Nonlinear Dynamic Inversion Baseline Control Law: Architecture and Performance Predictions

    NASA Technical Reports Server (NTRS)

    Miller, Christopher J.

    2011-01-01

    A model reference dynamic inversion control law has been developed to provide a baseline control law for research into adaptive elements and other advanced flight control law components. This controller has been implemented and tested in a hardware-in-the-loop simulation; the simulation results show excellent handling qualities throughout the limited flight envelope. A simple angular momentum formulation was chosen because it can be included in the stability proofs for many basic adaptive theories, such as model reference adaptive control. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as basic as possible to simplify the addition of the adaptive elements. Those design choices are explained, along with their predicted impact on the handling qualities.

  9. Decentralized robust nonlinear model predictive controller for unmanned aerial systems

    NASA Astrophysics Data System (ADS)

    Garcia Garreton, Gonzalo A.

    The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1. A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2. A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3. An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4. A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible.

  10. Tuning Proportional-Integral controllers to approximate simplified predictive control performance.

    PubMed

    Mansour, S E

    2009-10-01

    An exact equivalence between PI (Proportional-Integral) and two-parameter SPC (Simplified Predictive Control) is developed to provide identical control of first order linear plants. A relationship between the PI control parameters and the SPC control parameters is described. This relationship that allows the same control in the case of first order linear plants is also found to provide tuning formulas that yield PI control which approximates SPC performance in the case of second order linear plants with widely separated Eigenvalues. Finally, an extension of the PI control algorithm to include future errors provides another exact PI-SPC equivalence for networked control of first order plants.

  11. A nonlinear regression model-based predictive control algorithm.

    PubMed

    Dubay, R; Abu-Ayyad, M; Hernandez, J M

    2009-04-01

    This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.

  12. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants

    SciTech Connect

    B. Wayne Bequette; Priyadarshi Mahapatra

    2010-08-31

    The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.

  13. Predictive onboard flow control for packet switching satellites

    NASA Technical Reports Server (NTRS)

    Bobinsky, Eric A.

    1992-01-01

    We outline two alternate approaches to predicting the onset of congestion in a packet switching satellite, and argue that predictive, rather than reactive, flow control is necessary for the efficient operation of such a system. The first method discussed is based on standard, statistical techniques which are used to periodically calculate a probability of near-term congestion based on arrival rate statistics. If this probability exceeds a present threshold, the satellite would transmit a rate-reduction signal to all active ground stations. The second method discussed would utilize a neural network to periodically predict the occurrence of buffer overflow based on input data which would include, in addition to arrival rates, the distributions of packet lengths, source addresses, and destination addresses.

  14. Model predictive control of a wind turbine modelled in Simpack

    NASA Astrophysics Data System (ADS)

    Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.

    2014-06-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to

  15. Application of infinite model predictive control methodology to other advanced controllers.

    PubMed

    Abu-Ayyad, M; Dubay, R; Hernandez, J M

    2009-01-01

    This paper presents an application of most recent developed predictive control algorithm an infinite model predictive control (IMPC) to other advanced control schemes. The IMPC strategy was derived for systems with different degrees of nonlinearity on the process gain and time constant. Also, it was shown that IMPC structure uses nonlinear open-loop modeling which is conducted while closed-loop control is executed every sampling instant. The main objective of this work is to demonstrate that the methodology of IMPC can be applied to other advanced control strategies making the methodology generic. The IMPC strategy was implemented on several advanced controllers such as PI controller using Smith-Predictor, Dahlin controller, simplified predictive control (SPC), dynamic matrix control (DMC), and shifted dynamic matrix (m-DMC). Experimental work using these approaches combined with IMPC was conducted on both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems and compared with the original forms of these advanced controllers. Computer simulations were performed on nonlinear plants demonstrating that the IMPC strategy can be readily implemented on other advanced control schemes providing improved control performance. Practical work included real-time control applications on a DC motor, plastic injection molding machine and a MIMO three zone thermal system.

  16. Flutter prediction for a wing with active aileron control

    NASA Technical Reports Server (NTRS)

    Penning, K.; Sandlin, D. R.

    1983-01-01

    A method for predicting the vibrational stability of an aircraft with an analog active aileron flutter suppression system (FSS) is expained. Active aileron refers to the use of an active control system connected to the aileron to damp vibrations. Wing vibrations are sensed by accelerometers and the information is used to deflect the aileron. Aerodynamic force caused by the aileron deflection oppose wing vibrations and effectively add additional damping to the system.

  17. Model predictive control for tracking randomly varying references

    NASA Astrophysics Data System (ADS)

    Falugi, Paola

    2015-04-01

    This paper proposes a model predictive control scheme for tracking a-priori unknown references varying in a wide range and analyses its performance. It is usual to assume that the reference eventually converges to a constant in which case convergence to zero of the tracking error can be established. In this note we remove this simplifying assumption and characterise the set to which the tracking error converges and the associated region of convergence.

  18. Prediction and control of chaotic processes using nonlinear adaptive networks

    SciTech Connect

    Jones, R.D.; Barnes, C.W.; Flake, G.W.; Lee, K.; Lewis, P.S.; O'Rouke, M.K.; Qian, S.

    1990-01-01

    We present the theory of nonlinear adaptive networks and discuss a few applications. In particular, we review the theory of feedforward backpropagation networks. We then present the theory of the Connectionist Normalized Linear Spline network in both its feedforward and iterated modes. Also, we briefly discuss the theory of stochastic cellular automata. We then discuss applications to chaotic time series, tidal prediction in Venice lagoon, finite differencing, sonar transient detection, control of nonlinear processes, control of a negative ion source, balancing a double inverted pendulum and design advice for free electron lasers and laser fusion targets.

  19. Punishment sensitivity predicts the impact of punishment on cognitive control.

    PubMed

    Braem, Senne; Duthoo, Wout; Notebaert, Wim

    2013-01-01

    Cognitive control theories predict enhanced conflict adaptation after punishment. However, no such effect was found in previous work. In the present study, we demonstrate in a flanker task how behavioural adjustments following punishment signals are highly dependent on punishment sensitivity (as measured by the Behavioural Inhibition System (BIS) scale): Whereas low punishment-sensitive participants do show increased conflict adaptation after punishment, high punishment-sensitive participants show no such modulation. Interestingly, participants with a high punishment-sensitivity showed an overall reaction time increase after punishments. Our results stress the role of individual differences in explaining motivational modulations of cognitive control.

  20. Experimental Investigations of Generalized Predictive Control for Tiltrotor Stability Augmentation

    NASA Technical Reports Server (NTRS)

    Nixon, Mark W.; Langston, Chester W.; Singleton, Jeffrey D.; Piatak, David J.; Kvaternik, Raymond G.; Bennett, Richard L.; Brown, Ross K.

    2001-01-01

    A team of researchers from the Army Research Laboratory, NASA Langley Research Center (LaRC), and Bell Helicopter-Textron, Inc. have completed hover-cell and wind-tunnel testing of a 1/5-size aeroelastically-scaled tiltrotor model using a new active control system for stability augmentation. The active system is based on a generalized predictive control (GPC) algorithm originally developed at NASA LaRC in 1997 for un-known disturbance rejection. Results of these investigations show that GPC combined with an active swashplate can significantly augment the damping and stability of tiltrotors in both hover and high-speed flight.

  1. Applying new optimization algorithms to more predictive control

    SciTech Connect

    Wright, S.J.

    1996-03-01

    The connections between optimization and control theory have been explored by many researchers and optimization algorithms have been applied with success to optimal control. The rapid pace of developments in model predictive control has given rise to a host of new problems to which optimization has yet to be applied. Concurrently, developments in optimization, and especially in interior-point methods, have produced a new set of algorithms that may be especially helpful in this context. In this paper, we reexamine the relatively simple problem of control of linear processes subject to quadratic objectives and general linear constraints. We show how new algorithms for quadratic programming can be applied efficiently to this problem. The approach extends to several more general problems in straightforward ways.

  2. Application of linear gauss pseudospectral method in model predictive control

    NASA Astrophysics Data System (ADS)

    Yang, Liang; Zhou, Hao; Chen, Wanchun

    2014-03-01

    This paper presents a model predictive control(MPC) method aimed at solving the nonlinear optimal control problem with hard terminal constraints and quadratic performance index. The method combines the philosophies of the nonlinear approximation model predictive control, linear quadrature optimal control and Gauss Pseudospectral method. The current control is obtained by successively solving linear algebraic equations transferred from the original problem via linearization and the Gauss Pseudospectral method. It is not only of high computational efficiency since it does not need to solve nonlinear programming problem, but also of high accuracy though there are a few discrete points. Therefore, this method is suitable for on-board applications. A design of terminal impact with a specified direction is carried out to evaluate the performance of this method. Augmented PN guidance law in the three-dimensional coordinate system is applied to produce the initial guess. And various cases for target with straight-line movements are employed to demonstrate the applicability in different impact angles. Moreover, performance of the proposed method is also assessed by comparison with other guidance laws. Simulation results indicate that this method is not only of high computational efficiency and accuracy, but also applicable in the framework of guidance design.

  3. Controlled test for predictive power of Lyapunov exponents: Their inability to predict epileptic seizures

    NASA Astrophysics Data System (ADS)

    Lai, Ying-Cheng; Harrison, Mary Ann F.; Frei, Mark G.; Osorio, Ivan

    2004-09-01

    Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy.

  4. Control of nonlinear processes by using linear model predictive control algorithms.

    PubMed

    Gu, Bingfeng; Gupta, Yash P

    2008-04-01

    Most chemical processes are inherently nonlinear. However, because of their simplicity, linear control algorithms have been used for the control of nonlinear processes. In this study, the use of the dynamic matrix control algorithm and a simplified model predictive control algorithm for control of a bench-scale pH neutralization process is investigated. The nonlinearity is handled by dividing the operating region into sub-regions and by switching the controller model as the process moves from one sub-region to another. A simple modification for model predictive control algorithms is presented to handle the switching. The simulation and experimental results show that the modification can provide a significant improvement in the control of nonlinear processes.

  5. An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands

    PubMed Central

    Jiang, Jiefeng; Beck, Jeffrey; Heller, Katherine; Egner, Tobias

    2015-01-01

    The anterior cingulate and lateral prefrontal cortices have been implicated in implementing context-appropriate attentional control, but the learning mechanisms underlying our ability to flexibly adapt the control settings to changing environments remain poorly understood. Here we show that human adjustments to varying control demands are captured by a reinforcement learner with a flexible, volatility-driven learning rate. Using model-based functional magnetic resonance imaging, we demonstrate that volatility of control demand is estimated by the anterior insula, which in turn optimizes the prediction of forthcoming demand in the caudate nucleus. The caudate's prediction of control demand subsequently guides the implementation of proactive and reactive attentional control in dorsal anterior cingulate and dorsolateral prefrontal cortices. These data enhance our understanding of the neuro-computational mechanisms of adaptive behaviour by connecting the classic cingulate-prefrontal cognitive control network to a subcortical control-learning mechanism that infers future demands by flexibly integrating remote and recent past experiences. PMID:26391305

  6. Constrained model predictive control, state estimation and coordination

    NASA Astrophysics Data System (ADS)

    Yan, Jun

    In this dissertation, we study the interaction between the control performance and the quality of the state estimation in a constrained Model Predictive Control (MPC) framework for systems with stochastic disturbances. This consists of three parts: (i) the development of a constrained MPC formulation that adapts to the quality of the state estimation via constraints; (ii) the application of such a control law in a multi-vehicle formation coordinated control problem in which each vehicle operates subject to a no-collision constraint posed by others' imperfect prediction computed from finite bit-rate, communicated data; (iii) the design of the predictors and the communication resource assignment problem that satisfy the performance requirement from Part (ii). Model Predictive Control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. However, if the state constraints were handled in the same certainty-equivalence fashion, the resulting control law could drive the real state to violate the constraints frequently. Part (i) focuses on exploring the inclusion of state estimates into the constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. In Part (ii), we consider applying constrained MPC as a local control law in a coordinated control problem of a group of distributed autonomous systems. Interactions between the systems are captured via constraints. First, we inspect the application of constrained MPC to a completely deterministic case. Formation stability theorems are derived for the subsystems and conditions on the local constraint set are derived in order to

  7. A model predictive control approach for the Italian LBE XADS

    NASA Astrophysics Data System (ADS)

    Cammi, Antonio; Casella, Francesco; Luzzi, Lelio; Milano, Alessandro; Ricotti, Marco E.

    2008-06-01

    In this paper, model predictive control (MPC) is applied to the Italian 80 MW th experimental accelerator driven system (XADS), referring to a simple, non-linear model for the dynamic simulation of the plant, which has been developed and described in a previous work [A. Cammi, L. Luzzi, A.A. Porta, M.E. Ricotti, Prog. Nucl. Energ. 48 (2006) 578], in order to describe the interactions among the different subsystems: i.e., the accelerator-core coupling, the lead bismuth eutectic (LBE) primary system, the secondary system with diathermic oil and air coolers batteries, which reject the thermal power to the environment. Hereinafter, a model predictive controller is proposed, with the objective to minimize the difference between the average temperature of the diathermic oil and its reference value, while also minimizing the variations of the control input, which is the air coolers mass flow rate. The dynamic response of the LBE-XADS has been evaluated with reference to a reduction of 20% in the reactor power from nominal load conditions: this transient is very demanding for the overall plant, nevertheless the obtained results indicate the effectiveness of the proposed controller.

  8. Prediction and control of neural responses to pulsatile electrical stimulation.

    PubMed

    Campbell, Luke J; Sly, David James; O'Leary, Stephen John

    2012-04-01

    This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s(-1). A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s(-1). Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.

  9. Prediction and control of neural responses to pulsatile electrical stimulation

    NASA Astrophysics Data System (ADS)

    Campbell, Luke J.; Sly, David James; O'Leary, Stephen John

    2012-04-01

    This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s-1. A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s-1. Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.

  10. Error correction, sensory prediction, and adaptation in motor control.

    PubMed

    Shadmehr, Reza; Smith, Maurice A; Krakauer, John W

    2010-01-01

    Motor control is the study of how organisms make accurate goal-directed movements. Here we consider two problems that the motor system must solve in order to achieve such control. The first problem is that sensory feedback is noisy and delayed, which can make movements inaccurate and unstable. The second problem is that the relationship between a motor command and the movement it produces is variable, as the body and the environment can both change. A solution is to build adaptive internal models of the body and the world. The predictions of these internal models, called forward models because they transform motor commands into sensory consequences, can be used to both produce a lifetime of calibrated movements, and to improve the ability of the sensory system to estimate the state of the body and the world around it. Forward models are only useful if they produce unbiased predictions. Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors.

  11. Model predictive control power management strategies for HEVs: A review

    NASA Astrophysics Data System (ADS)

    Huang, Yanjun; Wang, Hong; Khajepour, Amir; He, Hongwen; Ji, Jie

    2017-02-01

    This paper presents a comprehensive review of power management strategy (PMS) utilized in hybrid electric vehicles (HEVs) with an emphasis on model predictive control (MPC) based strategies for the first time. Research on MPC-based power management systems for HEVs has intensified recently due to its many inherent merits. The categories of the existing PMSs are identified from the latest literature, and a brief study of each type is conducted. Then, the MPC approach is introduced and its advantages are discussed. Based on the acquisition method of driver behavior used for state prediction and the dynamic model used, the MPC is classified and elaborated. Factors that affect the performance of the MPC are put forward, including prediction accuracy, design parameters, and solvers. Finally, several important issues in the application of MPC-based power management strategies and latest developing trends are discussed. This paper not only provides a comprehensive analysis of MPC-based power management strategies for HEVs but also puts forward the future and emphasis of future study, which will promote the development of energy management controller with high performance and low cost for HEVs.

  12. Structural Acoustic Prediction and Interior Noise Control Technology

    NASA Technical Reports Server (NTRS)

    Mathur, G. P.; Chin, C. L.; Simpson, M. A.; Lee, J. T.; Palumbo, Daniel L. (Technical Monitor)

    2001-01-01

    This report documents the results of Task 14, "Structural Acoustic Prediction and Interior Noise Control Technology". The task was to evaluate the performance of tuned foam elements (termed Smart Foam) both analytically and experimentally. Results taken from a three-dimensional finite element model of an active, tuned foam element are presented. Measurements of sound absorption and sound transmission loss were taken using the model. These results agree well with published data. Experimental performance data were taken in Boeing's Interior Noise Test Facility where 12 smart foam elements were applied to a 757 sidewall. Several configurations were tested. Noise reductions of 5-10 dB were achieved over the 200-800 Hz bandwidth of the controller. Accelerometers mounted on the panel provided a good reference for the controller. Configurations with far-field error microphones outperformed near-field cases.

  13. Energy-efficient container handling using hybrid model predictive control

    NASA Astrophysics Data System (ADS)

    Xin, Jianbin; Negenborn, Rudy R.; Lodewijks, Gabriel

    2015-11-01

    The performance of container terminals needs to be improved to adapt the growth of containers while maintaining sustainability. This paper provides a methodology for determining the trajectory of three key interacting machines for carrying out the so-called bay handling task, involving transporting containers between a vessel and the stacking area in an automated container terminal. The behaviours of the interacting machines are modelled as a collection of interconnected hybrid systems. Hybrid model predictive control (MPC) is proposed to achieve optimal performance, balancing the handling capacity and energy consumption. The underlying control problem is hereby formulated as a mixed-integer linear programming problem. Simulation studies illustrate that a higher penalty on energy consumption indeed leads to improved sustainability using less energy. Moreover, simulations illustrate how the proposed energy-efficient hybrid MPC controller performs under different types of uncertainties.

  14. Self-Tuning of Design Variables for Generalized Predictive Control

    NASA Technical Reports Server (NTRS)

    Lin, Chaung; Juang, Jer-Nan

    2000-01-01

    Three techniques are introduced to determine the order and control weighting for the design of a generalized predictive controller. These techniques are based on the application of fuzzy logic, genetic algorithms, and simulated annealing to conduct an optimal search on specific performance indexes or objective functions. Fuzzy logic is found to be feasible for real-time and on-line implementation due to its smooth and quick convergence. On the other hand, genetic algorithms and simulated annealing are applicable for initial estimation of the model order and control weighting, and final fine-tuning within a small region of the solution space, Several numerical simulations for a multiple-input and multiple-output system are given to illustrate the techniques developed in this paper.

  15. Design of nonlinear PID controller and nonlinear model predictive controller for a continuous stirred tank reactor.

    PubMed

    Prakash, J; Srinivasan, K

    2009-07-01

    In this paper, the authors have represented the nonlinear system as a family of local linear state space models, local PID controllers have been designed on the basis of linear models, and the weighted sum of the output from the local PID controllers (Nonlinear PID controller) has been used to control the nonlinear process. Further, Nonlinear Model Predictive Controller using the family of local linear state space models (F-NMPC) has been developed. The effectiveness of the proposed control schemes has been demonstrated on a CSTR process, which exhibits dynamic nonlinearity.

  16. Predictive control and estimation algorithms for the NASA/JPL 70-meter antennas

    NASA Technical Reports Server (NTRS)

    Gawronski, W.

    1991-01-01

    A modified output prediction procedure and a new controller design is presented based on the predictive control law. Also, a new predictive estimator is developed to complement the controller and to enhance system performance. The predictive controller is designed and applied to the tracking control of the Deep Space Network 70 m antennas. Simulation results show significant improvement in tracking performance over the linear quadratic controller and estimator presently in use.

  17. Programmable logic controller implementation of an auto-tuned predictive control based on minimal plant information.

    PubMed

    Valencia-Palomo, G; Rossiter, J A

    2011-01-01

    This paper makes two key contributions. First, it tackles the issue of the availability of constrained predictive control for low-level control loops. Hence, it describes how the constrained control algorithm is embedded in an industrial programmable logic controller (PLC) using the IEC 61131-3 programming standard. Second, there is a definition and implementation of a novel auto-tuned predictive controller; the key novelty is that the modelling is based on relatively crude but pragmatic plant information. Laboratory experiment tests were carried out in two bench-scale laboratory systems to prove the effectiveness of the combined algorithm and hardware solution. For completeness, the results are compared with a commercial proportional-integral-derivative (PID) controller (also embedded in the PLC) using the most up to date auto-tuning rules.

  18. Model Predictive Control for the Operation of Building Cooling Systems

    SciTech Connect

    Ma, Yudong; Borrelli, Francesco; Hencey, Brandon; Coffey, Brian; Bengea, Sorin; Haves, Philip

    2010-06-29

    A model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reduction in the central plant electricity cost and improvement of its efficiency.

  19. Tuning the Model Predictive Control of a Crude Distillation Unit.

    PubMed

    Yamashita, André Shigueo; Zanin, Antonio Carlos; Odloak, Darci

    2016-01-01

    Tuning the parameters of the Model Predictive Control (MPC) of an industrial Crude Distillation Unit (CDU) is considered here. A realistic scenario is depicted where the inputs of the CDU system have optimizing targets, which are provided by the Real Time Optimization layer of the control structure. It is considered the nominal case, in which both the CDU model and the MPC model are the same. The process outputs are controlled inside zones instead of at fixed set points. Then, the tuning procedure has to define the weights that penalize the output error with respect to the control zone, the weights that penalize the deviation of the inputs from their targets, as well as the weights that penalize the input moves. A tuning approach based on multi-objective optimization is proposed and applied to the MPC of the CDU system. The performance of the controller tuned with the proposed approach is compared through simulation with the results of an existing approach also based on multi-objective optimization. The simulation results are similar, but the proposed approach has a computational load significantly lower than the existing method. The tuning effort is also much lower than in the conventional practical approaches that are usually based on ad-hoc procedures.

  20. Preview Scheduled Model Predictive Control For Horizontal Axis Wind Turbines

    NASA Astrophysics Data System (ADS)

    Laks, Jason H.

    This research investigates the use of model predictive control (MPC) in application to wind turbine operation from start-up to cut-out. The studies conducted are focused on the design of an MPC controller for a 650˜KW, three-bladed horizontal axis turbine that is in operation at the National Renewable Energy Laboratory's National Wind Technology Center outside of Golden, Colorado. This turbine is at the small end of utility scale turbines, but it provides advanced instrumentation and control capabilities, and there is a good probability that the approach developed in simulation for this thesis, will be field tested on the actual turbine. A contribution of this thesis is a method to combine the use of preview measurements with MPC while also providing regulation of turbine speed and cyclic blade loading. A common MPC technique provides integral-like control to achieve offset-free operation. At the same time in wind turbine applications, multiple studies have developed "feed-forward" controls based on applying a gain to an estimate of the wind speed changes obtained from an observer incorporating a disturbance model. These approaches are based on a technique that can be referred to as disturbance accommodating control (DAC). In this thesis, it is shown that offset-free tracking MPC is equivalent to a DAC approach when the disturbance gain is computed to satisfy a regulator equation. Although the MPC literature has recognized that this approach provides "structurally stable" disturbance rejection and tracking, this step is not typically divorced from the MPC computations repeated each sample hit. The DAC formulation is conceptually simpler, and essentially uncouples regulation considerations from MPC related issues. This thesis provides a self contained proof that the DAC formulation (an observer-controller and appropriate disturbance gain) provides structurally stable regulation.

  1. Predictive models of procedural human supervisory control behavior

    NASA Astrophysics Data System (ADS)

    Boussemart, Yves

    Human supervisory control systems are characterized by the computer-mediated nature of the interactions between one or more operators and a given task. Nuclear power plants, air traffic management and unmanned vehicles operations are examples of such systems. In this context, the role of the operators is typically highly proceduralized due to the time and mission-critical nature of the tasks. Therefore, the ability to continuously monitor operator behavior so as to detect and predict anomalous situations is a critical safeguard for proper system operation. In particular, such models can help support the decision J]l8king process of a supervisor of a team of operators by providing alerts when likely anomalous behaviors are detected By exploiting the operator behavioral patterns which are typically reinforced through standard operating procedures, this thesis proposes a methodology that uses statistical learning techniques in order to detect and predict anomalous operator conditions. More specifically, the proposed methodology relies on hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) to generate predictive models of unmanned vehicle systems operators. Through the exploration of the resulting HMMs in two distinct single operator scenarios, the methodology presented in this thesis is validated and shown to provide models capable of reliably predicting operator behavior. In addition, the use of HSMMs on the same data scenarios provides the temporal component of the predictions missing from the HMMs. The final step of this work is to examine how the proposed methodology scales to more complex scenarios involving teams of operators. Adopting a holistic team modeling approach, both HMMs and HSMMs are learned based on two team-based data sets. The results show that the HSMMs can provide valuable timing information in the single operator case, whereas HMMs tend to be more robust to increased team complexity. In addition, this thesis discusses the

  2. Striatal prediction errors support dynamic control of declarative memory decisions

    PubMed Central

    Scimeca, Jason M.; Katzman, Perri L.; Badre, David

    2016-01-01

    Adaptive memory requires context-dependent control over how information is retrieved, evaluated and used to guide action, yet the signals that drive adjustments to memory decisions remain unknown. Here we show that prediction errors (PEs) coded by the striatum support control over memory decisions. Human participants completed a recognition memory test that incorporated biased feedback to influence participants' recognition criterion. Using model-based fMRI, we find that PEs—the deviation between the outcome and expected value of a memory decision—correlate with striatal activity and predict individuals' final criterion. Importantly, the striatal PEs are scaled relative to memory strength rather than the expected trial outcome. Follow-up experiments show that the learned recognition criterion transfers to free recall, and targeting biased feedback to experimentally manipulate the magnitude of PEs influences criterion consistent with PEs scaled relative to memory strength. This provides convergent evidence that declarative memory decisions can be regulated via striatally mediated reinforcement learning signals. PMID:27713407

  3. Predictive current control of permanent magnet synchronous motor based on linear active disturbance rejection control

    NASA Astrophysics Data System (ADS)

    Li, Kunpeng

    2017-01-01

    The compatibility problem between rapidity and overshooting in the traditional predictive current control structure is inevitable and difficult to solve by reason of using PI controller. A novel predictive current control (PCC) algorithm for permanent magnet synchronous motor (PMSM) based on linear active disturbance rejection control (LADRC) is presented in this paper. In order to displace PI controller, the LADRC strategy which consisted of linear state error feedback (LSEF) control algorithm and linear extended state observer (LESO), is designed based on the mathematic model of PMSM. The purpose of LSEF is to make sure fast response to load mutation and system uncertainties, and LESO is designed to estimate the uncertain disturbances. The principal structures of the proposed system are speed outer loop based on LADRC and current inner loop based on predictive current control. Especially, the instruction value of qaxis current in inner loop is derived from the control quantity which is designed in speed outer loop. The simulation is carried out in Matlab/Simulink software, and the results illustrate that the dynamic and static performances of proposed system are satisfied. Moreover the robust against model parameters mismatch is enhanced obviously.

  4. Humans are sensitive to attention control when predicting others’ actions

    PubMed Central

    Pesquita, Ana; Chapman, Craig S.; Enns, James T.

    2016-01-01

    Studies of social perception report acute human sensitivity to where another’s attention is aimed. Here we ask whether humans are also sensitive to how the other’s attention is deployed. Observers viewed videos of actors reaching to targets without knowing that those actors were sometimes choosing to reach to one of the targets (endogenous control) and sometimes being directed to reach to one of the targets (exogenous control). Experiments 1 and 2 showed that observers could respond more rapidly when actors chose where to reach, yet were at chance when guessing whether the reach was chosen or directed. This implicit sensitivity to attention control held when either actor’s faces or limbs were masked (experiment 3) and when only the earliest actor’s movements were visible (experiment 4). Individual differences in sensitivity to choice correlated with an independent measure of social aptitude. We conclude that humans are sensitive to attention control through an implicit kinematic process linked to empathy. The findings support the hypothesis that social cognition involves the predictive modeling of others’ attentional states. PMID:27436897

  5. Design and Performance Analysis of Incremental Networked Predictive Control Systems.

    PubMed

    Pang, Zhong-Hua; Liu, Guo-Ping; Zhou, Donghua

    2016-06-01

    This paper is concerned with the design and performance analysis of networked control systems with network-induced delay, packet disorder, and packet dropout. Based on the incremental form of the plant input-output model and an incremental error feedback control strategy, an incremental networked predictive control (INPC) scheme is proposed to actively compensate for the round-trip time delay resulting from the above communication constraints. The output tracking performance and closed-loop stability of the resulting INPC system are considered for two cases: 1) plant-model match case and 2) plant-model mismatch case. For the former case, the INPC system can achieve the same output tracking performance and closed-loop stability as those of the corresponding local control system. For the latter case, a sufficient condition for the stability of the closed-loop INPC system is derived using the switched system theory. Furthermore, for both cases, the INPC system can achieve a zero steady-state output tracking error for step commands. Finally, both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method.

  6. Study on Noise Prediction Model and Control Schemes for Substation

    PubMed Central

    Gao, Yang; Liu, Songtao

    2014-01-01

    With the government's emphasis on environmental issues of power transmission and transformation project, noise pollution has become a prominent problem now. The noise from the working transformer, reactor, and other electrical equipment in the substation will bring negative effect to the ambient environment. This paper focuses on using acoustic software for the simulation and calculation method to control substation noise. According to the characteristics of the substation noise and the techniques of noise reduction, a substation's acoustic field model was established with the SoundPLAN software to predict the scope of substation noise. On this basis, 4 reasonable noise control schemes were advanced to provide some helpful references for noise control during the new substation's design and construction process. And the feasibility and application effect of these control schemes can be verified by using the method of simulation modeling. The simulation results show that the substation always has the problem of excessive noise at boundary under the conventional measures. The excess noise can be efficiently reduced by taking the corresponding noise reduction methods. PMID:24672356

  7. An investigation into active vibration isolation based on predictive control: Part I: Energy source control

    NASA Astrophysics Data System (ADS)

    Fei, H. Z.; Zheng, G. T.; Liu, Z. G.

    2006-09-01

    We report the results of a recent study for the active vibration isolation with whole-spacecraft vibration isolation as an application background into which three parts are divided: (i) energy source control, (ii) nonlinearity and time delay, (iii) implementation and experiment. This paper is the first in this three-part series report, which presents theoretical and experimental investigations into pressure tracking system for energy source control of the isolator. Considering the special environment of the rocket and expected characteristics of actuators, where the isolator will be arranged between the rocket and the spacecraft, pneumatic actuator is proposed to realize the active isolation control. In order to improve the dynamic characteristics of the pneumatic isolator, a cascade control algorithm with double loop structure and predictive control algorithm for pressure tracking control of the inner loop are proposed. In the current paper, a pressure tracking control system using model predictive control (MPC) is studied first. A pneumatic model around pressure work point is built firstly by simplifying the flow equation of valve's orifices and pressure differential equation of the chambers. With this model, an MPC algorithm in the state space is developed, and problems including control parameter choice and command horizon generator are discussed in detail. In addition, by adding model error correction loop and velocity compensation feedback, effects of model uncertainty and volume variation of chambers are reduced greatly. Thus with this design, the real-time pressure tracking can be guaranteed, and so that the active control system can work at higher frequency range.

  8. Model predictive control with constraints for a nonlinear adaptive cruise control vehicle model in transition manoeuvres

    NASA Astrophysics Data System (ADS)

    Ali, Zeeshan; Popov, Atanas A.; Charles, Guy

    2013-06-01

    A vehicle following control law, based on the model predictive control method, to perform transition manoeuvres (TMs) for a nonlinear adaptive cruise control (ACC) vehicle is presented in this paper. The TM controller ultimately establishes a steady-state following distance behind a preceding vehicle to avoid collision, keeping account of acceleration limits, safe distance, and state constraints. The vehicle dynamics model is for continuous-time domain and captures the real dynamics of the sub-vehicle models for steady-state and transient operations. The ACC vehicle can execute the TM successfully and achieves a steady-state in the presence of complex dynamics within the constraint boundaries.

  9. Two-Step Design Method of Engine Control System Based on Generalized Predictive Control

    NASA Astrophysics Data System (ADS)

    Hashimoto, Seiji; Okuda, Hiroyuki; Okada, Yasushi; Adachi, Shuichi; Niwa, Shinji; Kajitani, Mitsunobu

    Conservation of the environment has become critical to the automotive industry. Recently, requirements for on-board diagnostic and engine control systems have been strictly enforced. In the present paper, in order to meet the requirements for a low-emissions vehicle, a novel construction method of the air-fuel ratio (A/F) control system is proposed. The construction method of the system is divided into two steps. The first step is to design the A/F control system for the engine based on an open loop design. The second step is to design the A/F control system for the catalyst system. The design method is based on the generalized predictive control in order to satisfy the robustness to open loop control as well as model uncertainty. The effectiveness of the proposed A/F control system is verified through experiments using full-scale products.

  10. Inlet Flow Control and Prediction Technologies for Embedded Propulsion Systems

    NASA Technical Reports Server (NTRS)

    McMillan, Michelle L.; Gissen, Abe; Vukasinovic, Bojan; Lakebrink, Matthew T.; Glezer, Ari; Mani, Mori; Mace, James

    2010-01-01

    Fail-safe inlet flow control may enable high-speed cruise efficiency, low noise signature, and reduced fuel-burn goals for hybrid wing-body aircraft. The objectives of this program are to develop flow control and prediction methodologies for boundary-layer ingesting (BLI) inlets used in these aircraft. This report covers the second of a three year program. The approach integrates experiments and numerical simulations. Both passive and active flow-control devices were tested in a small-scale wind tunnel. Hybrid actuation approaches, combining a passive microvane and active synthetic jet, were tested in various geometric arrangements. Detailed flow measurements were taken to provide insight into the flow physics. Results of the numerical simulations were correlated against experimental data. The sensitivity of results to grid resolution and turbulence models was examined. Aerodynamic benefits from microvanes and microramps were assessed when installed in an offset BLI inlet. Benefits were quantified in terms of recovery and distortion changes. Microvanes were more effective than microramps at improving recovery and distortion.

  11. Inlet Flow Control and Prediction Technologies for Embedded Propulsion Systems

    NASA Technical Reports Server (NTRS)

    McMillan, Michelle L.; Mackie, Scott A.; Gissen, Abe; Vukasinovic, Bojan; Lakebrink, Matthew T.; Glezer, Ari; Mani, Mori; Mace, James L.

    2011-01-01

    Fail-safe, hybrid, flow control (HFC) is a promising technology for meeting high-speed cruise efficiency, low-noise signature, and reduced fuel-burn goals for future, Hybrid-Wing-Body (HWB) aircraft with embedded engines. This report details the development of HFC technology that enables improved inlet performance in HWB vehicles with highly integrated inlets and embedded engines without adversely affecting vehicle performance. In addition, new test techniques for evaluating Boundary-Layer-Ingesting (BLI)-inlet flow-control technologies developed and demonstrated through this program are documented, including the ability to generate a BLI-like inlet-entrance flow in a direct-connect, wind-tunnel facility, as well as, the use of D-optimal, statistically designed experiments to optimize test efficiency and enable interpretation of results. Validated improvements in numerical analysis tools and methods accomplished through this program are also documented, including Reynolds-Averaged Navier-Stokes CFD simulations of steady-state flow physics for baseline, BLI-inlet diffuser flow, as well as, that created by flow-control devices. Finally, numerical methods were employed in a ground-breaking attempt to directly simulate dynamic distortion. The advances in inlet technologies and prediction tools will help to meet and exceed "N+2" project goals for future HWB aircraft.

  12. Prediction-based association control scheme in dense femtocell networks

    PubMed Central

    Pham, Ngoc-Thai; Huynh, Thong; Hwang, Won-Joo; You, Ilsun; Choo, Kim-Kwang Raymond

    2017-01-01

    The deployment of large number of femtocell base stations allows us to extend the coverage and efficiently utilize resources in a low cost manner. However, the small cell size of femtocell networks can result in frequent handovers to the mobile user, and consequently throughput degradation. Thus, in this paper, we propose predictive association control schemes to improve the system’s effective throughput. Our design focuses on reducing handover frequency without impacting on throughput. The proposed schemes determine handover decisions that contribute most to the network throughput and are proper for distributed implementations. The simulation results show significant gains compared with existing methods in terms of handover frequency and network throughput perspective. PMID:28328992

  13. Redesigned Predictive Event-Triggered Controller for Networked Control System With Delays.

    PubMed

    Wu, Di; Sun, Xi-Ming; Wen, Changyun; Wang, Wei

    2016-10-01

    Event-triggered control (ETC) is a control strategy which can effectively reduce communication traffic in control networks. In the case where communication resources are scarce, ETC plays an important role in updating and communicating data. When network-induced delays are involved, two unsynchronized phenomena will appear if the existing ETC strategy, designed for networked control systems (NCSs) free of delays, is adopted. This paper deals with the ETC problem for NCS with delays existing in both sensor-to-controller and controller-to-actuator channels. A new predictive ETC strategy is proposed to solve both unsynchronized problems. It is shown that the stability of the resulting closed-loop system can be guaranteed under such an ETC strategy. Finally, both simulation studies and experimental tests are carried out to illustrate the proposed technique and verify its effectiveness.

  14. Predictive wavefront control for Adaptive Optics with arbitrary control loop delays

    SciTech Connect

    Poyneer, L A; Veran, J

    2007-10-30

    We present a modification of the closed-loop state space model for AO control which allows delays that are a non-integer multiple of the system frame rate. We derive the new forms of the Predictive Fourier Control Kalman filters for arbitrary delays and show that they are linear combinations of the whole-frame delay terms. This structure of the controller is independent of the delay. System stability margins and residual error variance both transition gracefully between integer-frame delays.

  15. Predictive active disturbance rejection control for processes with time delay.

    PubMed

    Zheng, Qinling; Gao, Zhiqiang

    2014-07-01

    Active disturbance rejection control (ADRC) has been shown to be an effective tool in dealing with real world problems of dynamic uncertainties, disturbances, nonlinearities, etc. This paper addresses its existing limitations with plants that have a large transport delay. In particular, to overcome the delay, the extended state observer (ESO) in ADRC is modified to form a predictive ADRC, leading to significant improvements in the transient response and stability characteristics, as shown in extensive simulation studies and hardware-in-the-loop tests, as well as in the frequency response analysis. In this research, it is assumed that the amount of delay is approximately known, as is the approximated model of the plant. Even with such uncharacteristic assumptions for ADRC, the proposed method still exhibits significant improvements in both performance and robustness over the existing methods such as the dead-time compensator based on disturbance observer and the Filtered Smith Predictor, in the context of some well-known problems of chemical reactor and boiler control problems.

  16. Factors Predicting Atypical Development of Nighttime Bladder Control

    PubMed Central

    Sullivan, Sarah; Heron, Jon

    2015-01-01

    ABSTRACT: Objective: To derive latent classes (longitudinal “phenotypes”) of frequency of bedwetting from 4 to 9 years and to examine their association with developmental delay, parental history of bedwetting, length of gestation and birth weight. Method: The authors used data from 8,769 children from the UK Avon Longitudinal Study of Parents and Children cohort. Mothers provided repeated reports on their child's frequency of bedwetting from 4 to 9 years. The authors used longitudinal latent class analysis to derive latent classes of bedwetting and examined their association with sex, developmental level at 18 months, parental history of wetting, birth weight, and gestational length. Results: The authors identified 5 latent classes: (1) “normative”—low probability of bedwetting; (2) “infrequent delayed”—delayed attainment of nighttime bladder control with bedwetting control with bedwetting ≥ twice a week; (4) “infrequent persistent”—persistent bedwetting < twice a week; and (5) “frequent persistent”—persistent bedwetting ≥ twice a week. Male gender (odds ratio = 3.20 [95% confidence interval = 2.36–4.34]), developmental delay, for example, delayed social skills (1.33 [1.11–1.58]), and maternal history of wetting (3.91 [2.60–5.88]) were associated with an increase in the odds of bedwetting at 4 to 9 years. There was little evidence that low birth weight and shorter gestation period were associated with bedwetting. Conclusion: The authors described patterns of development of nighttime bladder control and found evidence for factors that predict continuation of bedwetting at school age. Increased knowledge of risk factors for bedwetting is needed to identify children at risk of future problems attaining and maintaining continence. PMID:26468941

  17. A Disturbance Rejection for Model Predictive Control Using a Multivariable Disturbance Observer

    NASA Astrophysics Data System (ADS)

    Tange, Yoshio; Matsui, Tetsuro; Matsumoto, Koji; Nishida, Hideyuki

    Model predictive control has been widely used in industrial applications. And more efficient and more precise control is being required to meet growing demands such as energy savings and fewer emissions in industrial plants. In this paper, we focus on step response model based predictive control, which is one of most applied predictive control methods, and propose a new disturbance rejection method to overcome control performance degradation caused by unmeasured ramp-like disturbances.

  18. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    USGS Publications Warehouse

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  19. Predictive powertrain control using powertrain history and GPS data

    DOEpatents

    Weslati, Feisel; Krupadanam, Ashish A

    2015-03-03

    A method and powertrain apparatus that predicts a route of travel for a vehicle and uses historical powertrain loads and speeds for the predicted route of travel to optimize at least one powertrain operation for the vehicle.

  20. EPR oxygen images predict tumor control by a 50 percent tumor control radiation dose

    PubMed Central

    Elas, Martyna; Magwood, Jessica M.; Butler, Brandi; Li, Chanel; Wardak, Rona; Barth, Eugene D.; Epel, Boris; Rubinstein, Samuel; Pelizzari, Charles A.; Weichselbaum, Ralph R.; Halpern, Howard J.

    2013-01-01

    Clinical trials to ameliorate hypoxia as a strategy to relieve the radiation resistance it causes have prompted a need to assay the precise extent and location of hypoxia in tumors. Electron Paramagnetic Resonance oxygen imaging (EPR O2 imaging) provides a non-invasive means to address this need. To obtain a preclinical proof of principle that EPR O2 images could predict radiation control, we treated mouse tumors at or near doses required to achieve 50 percent control (TCD50). Mice with FSa fibrosarcoma or MCa4 carcinoma were subjected to EPR O2 imaging and immediately radiated to a TCD50 or TCD50 ±10 Gy.. Statistical analysis was permitted by collection of ~ 1300 tumor pO2 image voxels, including the fraction of tumor voxels with pO2 less than 10 mm Hg (HF10). Tumors were followed for 90 days (FSa) or 120 days (MCa4) to determine local control or failure. HF10 obtained from EPR images showed statistically significant differences between tumors that were controlled by the TCD50 and those that were not controlled for both FSa and MCa4. Kaplan-Meier analysis of both types of tumors showed ~90% of mildly hypoxic tumors were controlled (HF10<10%), and only 37% (FSA) and 23% (MCa4) tumors controlled if hypoxic. EPR pO2 image voxel distributions in these ~0.5 ml tumors provide a prediction of radiation curability independent of radiation dose. These data confirm the significance of EPR pO2 hypoxic fractions. The ~90% control of low HF10 tumors argue that ½ ml subvolumes of tumors may be more sensitive to radiation and may need less radiation for high tumor control rates. PMID:23861469

  1. Predictable SCR co-benefits for mercury control

    SciTech Connect

    Pritchard, S.

    2009-01-15

    A test program, performed in cooperation with Dominion Power and the Babcock and Wilcox Co., was executed at Dominion Power's Mount Storm power plant in Grant County, W. Va. The program was focused on both the selective catalytic reduction (SCR) catalyst capability to oxide mercury as well as the scrubber's capability to capture and retain the oxidized mercury. This article focuses on the SCR catalyst performance aspects. The Mount Storm site consists of three units totaling approximately 1,660 MW. All units are equipped with SCR systems for NOx control. A full-scale test to evaluate the effect of the SCR was performed on Unit 2, a 550 MWT-fired boiler firing a medium sulfur bituminous coal. This test program demonstrated that the presence of an SCR catalyst can significantly affect the mercury speciation profile. Observation showed that in the absence of an SCR catalyst, the extent of oxidation of element a mercury at the inlet of the flue gas desulfurization system was about 64%. The presence of a Cornertech SCR catalyst improved this oxidation to levels greater than 95% almost all of which was captured by the downstream wet FGD system. Cornertech's proprietary SCR Hg oxidation model was used to accurately predict the field results. 1 ref., 2 figs., 1 tab.

  2. Exploratory Studies in Generalized Predictive Control for Active Aeroelastic Control of Tiltrotor Aircraft

    NASA Technical Reports Server (NTRS)

    Kvaternik, Raymond G.; Juang, Jer-Nan; Bennett, Richard L.

    2000-01-01

    The Aeroelasticity Branch at NASA Langley Research Center has a long and substantive history of tiltrotor aeroelastic research. That research has included a broad range of experimental investigations in the Langley Transonic Dynamics Tunnel (TDT) using a variety of scale models and the development of essential analyses. Since 1994, the tiltrotor research program has been using a 1/5-scale, semispan aeroelastic model of the V-22 designed and built by Bell Helicopter Textron Inc. (BHTI) in 1981. That model has been refurbished to form a tiltrotor research testbed called the Wing and Rotor Aeroelastic Test System (WRATS) for use in the TDT. In collaboration with BHTI, studies under the current tiltrotor research program are focused on aeroelastic technology areas having the potential for enhancing the commercial and military viability of tiltrotor aircraft. Among the areas being addressed, considerable emphasis is being directed to the evaluation of modern adaptive multi-input multi- output (MIMO) control techniques for active stability augmentation and vibration control of tiltrotor aircraft. As part of this investigation, a predictive control technique known as Generalized Predictive Control (GPC) is being studied to assess its potential for actively controlling the swashplate of tiltrotor aircraft to enhance aeroelastic stability in both helicopter and airplane modes of flight. This paper summarizes the exploratory numerical and experimental studies that were conducted as part of that investigation.

  3. Gain scheduled continuous-time model predictive controller with experimental validation on AC machine

    NASA Astrophysics Data System (ADS)

    Wang, Liuping; Gan, Lu

    2013-08-01

    Linear controllers with gain scheduling have been successfully used in the control of nonlinear systems for the past several decades. This paper proposes the design of gain scheduled continuous-time model predictive controller with constraints. Using induction machine as an illustrative example, the paper will show the four steps involved in the design of a gain scheduled predictive controller: (i) linearisation of a nonlinear plant according to operating conditions; (ii) the design of linear predictive controllers for the family of linear models; (iii) gain scheduled predictive control law that will optimise a multiple model objective function with constraints, which will also ensure smooth transitions (i.e. bumpless transfer) between the predictive controllers; (iv) experimental validation of the gain scheduled predictive control system with constraints.

  4. Performance and robustness of hybrid model predictive control for controllable dampers in building models

    NASA Astrophysics Data System (ADS)

    Johnson, Erik A.; Elhaddad, Wael M.; Wojtkiewicz, Steven F.

    2016-04-01

    A variety of strategies have been developed over the past few decades to determine controllable damping device forces to mitigate the response of structures and mechanical systems to natural hazards and other excitations. These "smart" damping devices produce forces through passive means but have properties that can be controlled in real time, based on sensor measurements of response across the structure, to dramatically reduce structural motion by exploiting more than the local "information" that is available to purely passive devices. A common strategy is to design optimal damping forces using active control approaches and then try to reproduce those forces with the smart damper. However, these design forces, for some structures and performance objectives, may achieve high performance by selectively adding energy, which cannot be replicated by a controllable damping device, causing the smart damper performance to fall far short of what an active system would provide. The authors have recently demonstrated that a model predictive control strategy using hybrid system models, which utilize both continuous and binary states (the latter to capture the switching behavior between dissipative and non-dissipative forces), can provide reductions in structural response on the order of 50% relative to the conventional clipped-optimal design strategy. This paper explores the robustness of this newly proposed control strategy through evaluating controllable damper performance when the structure model differs from the nominal one used to design the damping strategy. Results from the application to a two-degree-of-freedom structure model confirms the robustness of the proposed strategy.

  5. Predictive Poincaré control: A control theory for chaotic systems

    NASA Astrophysics Data System (ADS)

    Schweizer, Jörg; Kennedy, Michael Peter

    1995-11-01

    One of the most interesting features of chaotic systems is the large number of unstable orbits embedded in a chaotic attractor. In this work, we propose a global chaos-control technique called predictive Poincaré control (PPC) that permits stabilization of a predefined solution, using only small control pulses. We prove this result for a large class of n-dimensional chaotic systems. The predefined solution can be a periodic or nonperiodic oscillation, expressed by a periodic or nonperiodic symbolic sequence [S. Hayes, C. Grebogi, and E. Ott, Phys. Rev. Lett. 70, 3031 (1993)]. We apply the general PPC scheme to the well known Lorenz model and study its robustness with respect to parasitic effects.

  6. Analysis, prediction and control of radio frequency interference with respect to DSN

    NASA Technical Reports Server (NTRS)

    Degroot, N. F.

    1982-01-01

    Susceptibility modeling, prediction of radio frequency interference from satellites, operational radio frequency interference control, and international regulations are considered. The existing satellite interference prediction program DSIP2 is emphasized. A summary status evaluation and recommendations for future work are given.

  7. Application of Sampling Based Model Predictive Control to an Autonomous Underwater Vehicle

    DTIC Science & Technology

    2010-07-01

    55 Application of Sampling Based Model Predictive Control to an Autonomous Underwater Vehicle Unmanned Underwater Vehicles (UUVs) can be utilized...the vehicle can feasibly traverse. As a result, Sampling- Based Model Predictive Control (SBMPC) is proposed to simultaneously generate control...inputs and system trajectories for an autonomous underwater vehicle (AUV). The algorithm combines the benefits of sampling- based motion planning with

  8. Predictive motor control of sensory dynamics in auditory active sensing.

    PubMed

    Morillon, Benjamin; Hackett, Troy A; Kajikawa, Yoshinao; Schroeder, Charles E

    2015-04-01

    Neuronal oscillations present potential physiological substrates for brain operations that require temporal prediction. We review this idea in the context of auditory perception. Using speech as an exemplar, we illustrate how hierarchically organized oscillations can be used to parse and encode complex input streams. We then consider the motor system as a major source of rhythms (temporal priors) in auditory processing, that act in concert with attention to sharpen sensory representations and link them across areas. We discuss the circuits that could mediate this audio-motor interaction, notably the potential role of the somatosensory system. Finally, we reposition temporal predictions in the context of internal models, discussing how they interact with feature-based or spatial predictions. We argue that complementary predictions interact synergistically according to the organizational principles of each sensory system, forming multidimensional filters crucial to perception.

  9. Predictive Techniques for Spacecraft Cabin Air Quality Control

    NASA Technical Reports Server (NTRS)

    Perry, J. L.; Cromes, Scott D. (Technical Monitor)

    2001-01-01

    As assembly of the International Space Station (ISS) proceeds, predictive techniques are used to determine the best approach for handling a variety of cabin air quality challenges. These techniques use equipment offgassing data collected from each ISS module before flight to characterize the trace chemical contaminant load. Combined with crew metabolic loads, these data serve as input to a predictive model for assessing the capability of the onboard atmosphere revitalization systems to handle the overall trace contaminant load as station assembly progresses. The techniques for predicting in-flight air quality are summarized along with results from early ISS mission analyses. Results from groundbased analyses of in-flight air quality samples are compared to the predictions to demonstrate the technique's relative conservatism.

  10. Stochastic Prediction and Feedback Control of Router Queue Size in a Virtual Network Environment

    DTIC Science & Technology

    2014-09-18

    STOCHASTIC PREDICTION AND FEEDBACK CONTROL OF ROUTER QUEUE SIZE IN A VIRTUAL NETWORK ENVIRONMENT THESIS Muflih Alqahtani, First...AFIT-ENG-T-14-S-10 STOCHASTIC PREDICTION AND FEEDBACK CONTROL OF ROUTER QUEUE SIZE IN A VIRTUAL NETWORK ENVIRONMENT THESIS Presented to the...UNLIMITED AFIT-ENG-T-14-S-10 STOCHASTIC PREDICTION AND FEEDBACK CONTROL OF ROUTER QUEUE SIZE IN A VIRTUAL NETWORK ENVIRONMENT Muflih Alqahtani

  11. Control of Warm Compression Stations Using Model Predictive Control: Simulation and Experimental Results

    NASA Astrophysics Data System (ADS)

    Bonne, F.; Alamir, M.; Bonnay, P.

    2017-02-01

    This paper deals with multivariable constrained model predictive control for Warm Compression Stations (WCS). WCSs are subject to numerous constraints (limits on pressures, actuators) that need to be satisfied using appropriate algorithms. The strategy is to replace all the PID loops controlling the WCS with an optimally designed model-based multivariable loop. This new strategy leads to high stability and fast disturbance rejection such as those induced by a turbine or a compressor stop, a key-aspect in the case of large scale cryogenic refrigeration. The proposed control scheme can be used to achieve precise control of pressures in normal operation or to avoid reaching stopping criteria (such as excessive pressures) under high disturbances (such as a pulsed heat load expected to take place in future fusion reactors, expected in the cryogenic cooling systems of the International Thermonuclear Experimental Reactor ITER or the Japan Torus-60 Super Advanced fusion experiment JT-60SA). The paper details the simulator used to validate this new control scheme and the associated simulation results on the SBTs WCS. This work is partially supported through the French National Research Agency (ANR), task agreement ANR-13-SEED-0005.

  12. Prediction Models are Basis for Rational Air Quality Control

    ERIC Educational Resources Information Center

    Daniels, Anders; Bach, Wilfrid

    1973-01-01

    An air quality control scheme employing meteorological diffusion, time averaging and frequency, and cost-benefit models is discussed. The methods outlined provide a constant feedback system for air quality control. Flow charts and maps are included. (BL)

  13. Prediction of forces and moments for flight vehicle control effectors. Part 1: Validation of methods for predicting hypersonic vehicle controls forces and moments

    NASA Technical Reports Server (NTRS)

    Maughmer, Mark D.; Ozoroski, L.; Ozoroski, T.; Straussfogel, D.

    1990-01-01

    Many types of hypersonic aircraft configurations are currently being studied for feasibility of future development. Since the control of the hypersonic configurations throughout the speed range has a major impact on acceptable designs, it must be considered in the conceptual design stage. The ability of the aerodynamic analysis methods contained in an industry standard conceptual design system, APAS II, to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds is considered. Predicted control forces and moments generated by various control effectors are compared with previously published wind tunnel and flight test data for three configurations: the North American X-15, the Space Shuttle Orbiter, and a hypersonic research airplane concept. Qualitative summaries of the results are given for each longitudinal force and moment and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage. Results for most lateral/directional control derivatives are acceptable for conceptual design purposes; however, predictions at supersonic Mach numbers for the change in yawing moment due to aileron deflection and the change in rolling moment due to rudder deflection are found to be unacceptable. Including shielding effects in the analysis is shown to have little effect on lift and pitching moment predictions while improving drag predictions.

  14. Maternal Illusory Control Predicts Socialization Strategies and Toddler Compliance.

    ERIC Educational Resources Information Center

    Donovan, Wilberta L.; Leavitt, Lewis A.; Walsh, Reghan O.

    2000-01-01

    Examined the relation between mothers' perception of their capacity for controlling infant crying and a later measure of compliance with parent requests by toddlers. Found that toddlers of mothers in the low and high illusion of control (overestimating of maternal control) groups were more likely to be highly defiant than were toddlers of mothers…

  15. Dynamical Epidemic Suppression Using Stochastic Prediction and Control

    DTIC Science & Technology

    2004-10-28

    reduce the rate of input of susceptibles. By using the PDF flux, we are able to distinguish regions used in other chaos control schemes that are...use this information in a control algo- stochastic chaos control methods that account specifically for rithm to prevent bursting dynamics (that is, to

  16. Hurricane prediction and control: impact of large computers.

    PubMed

    Hammond, A L

    1973-08-17

    This is the third is a continuing series of articles on natural disasters, their prediction and mnodification, and progress in understanding the physical bases of these phenomena. Two earlier articles (Science, 25 May, p. 851, and 1 June, p. 940) reported advances in earthquake prediction. Hurricanes are the subject here. Generally less devastating than major earthquakes-although a single hurricane in 1970 killed an estimated 200,000 persons in Bangladesh-these storms are still the most destructive of all atmospheric phenomena. A recent report of the National Academy of Sciences (see box) recommends that efforts to modify hurricanes and other severe storms become a national goal.

  17. Adaptive Data-based Predictive Control for Short Take-off and Landing (STOL) Aircraft

    NASA Technical Reports Server (NTRS)

    Barlow, Jonathan Spencer; Acosta, Diana Michelle; Phan, Minh Q.

    2010-01-01

    Data-based Predictive Control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. The characteristics of adaptive data-based predictive control are particularly appropriate for the control of nonlinear and time-varying systems, such as Short Take-off and Landing (STOL) aircraft. STOL is a capability of interest to NASA because conceptual Cruise Efficient Short Take-off and Landing (CESTOL) transport aircraft offer the ability to reduce congestion in the terminal area by utilizing existing shorter runways at airports, as well as to lower community noise by flying steep approach and climb-out patterns that reduce the noise footprint of the aircraft. In this study, adaptive data-based predictive control is implemented as an integrated flight-propulsion controller for the outer-loop control of a CESTOL-type aircraft. Results show that the controller successfully tracks velocity while attempting to maintain a constant flight path angle, using longitudinal command, thrust and flap setting as the control inputs.

  18. Design of a new PID controller using predictive functional control optimization for chamber pressure in a coke furnace.

    PubMed

    Zhang, Jianming

    2017-03-01

    An improved proportional-integral-derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers.

  19. Predictive Display Design for a Two-Axis Control Task.

    DTIC Science & Technology

    1982-08-01

    elementary processes (Sheridan and Rouse 1971, Rouse 1973, Van Heusden 1977). The problem is particularly acute in flight systems and missile guidance...26. VAN HEUSDEN , A R (1977) Models for human prediction of process variables. In Proceedings of Symposium on Human Operators and Simulation

  20. Gaining Control and Predictability of Software-Intensive Systems Development and Sustainment

    DTIC Science & Technology

    2015-02-04

    this would be a major design driver for the software architect (Naegle & Petross, 2007). Primary Software Acquisition Problem Areas Addressed The...control and produces significantly more predictability in the program management realm. The research conclusions and recommendations are designed to...provide more control and predictability to software-intensive systems development. Due to the TOC and architectural design focus, system sustainability

  1. Inhibitory Control Predicts Language Switching Performance in Trilingual Speech Production

    ERIC Educational Resources Information Center

    Linck, Jared A.; Schwieter, John W.; Sunderman, Gretchen

    2012-01-01

    This study investigated the role of domain-general inhibitory control in trilingual speech production. Taking an individual differences approach, we examined the relationship between performance on a non-linguistic measure of inhibitory control (the Simon task) and a multilingual language switching task for a group of fifty-six native English (L1)…

  2. Predicting Changes in Older Adults' Interpersonal Control Strivings

    ERIC Educational Resources Information Center

    Sorkin, Dara H.; Rook, Karen S.; Heckhausen, Jutta; Billimek, John

    2009-01-01

    People vary in the importance they ascribe to, and efforts they invest in, maintaining positive relationships with others. Research has linked such variation in interpersonal control strivings to the quality of social exchanges experienced, but little work has examined the predictors of interpersonal control strivings. Given the importance of…

  3. The Interplay of Maternal Sensitivity and Gentle Control When Predicting Children's Subsequent Academic Functioning: Evidence of Mediation by Effortful Control

    ERIC Educational Resources Information Center

    Kopystynska, Olena; Spinrad, Tracy L.; Seay, Danielle M.; Eisenberg, Nancy

    2016-01-01

    The goal of this work was to examine the complex interrelation of mothers' early gentle control and sensitivity in predicting children's effortful control (EC) and academic functioning. Maternal gentle control, maternal sensitivity, and children's EC were measured when children were 18, 30, and 42 months of age (T1, T2, and T3, respectively), and…

  4. A Roll, Fin, and Fin Controller Prediction Computer Program.

    DTIC Science & Technology

    1980-06-01

    Reference 1, and specific details of this improve- ment will be published in a future report currently under preparation by Cox. *A complete listing of...effects. CONCLUDING RMARKS This report provides a user’s guide to FINCON, a roll, fin, fin con- troller prediction computer program. No attempt to...180. FLOATIMUOIINU) ROLL I# OA14PUINUI a OUCIIV,1 ROLL 19 IF ( ITEPATE .EQ.0) O T3 98’ ROLL 106 Ise NTIY 0 ROLL lot To 0.0 ROLL lit s0 NTRY - NTYRY I

  5. Noise prediction and control of Pudong International Airport expansion project.

    PubMed

    Lei, Bin; Yang, Xin; Yang, Jianguo

    2009-04-01

    The Environmental Impact Assessment (EIA) process of the third runway building project of Pudong International Airport is briefly introduced in the paper. The basic principle, the features, and the operation steps of newly imported FAA's Integrated Noise Model (INM) are discussed for evaluating the aircraft noise impacts. The prediction of the aircraft noise and the countermeasures for the noise mitigation are developed, which includes the reasonable runway location, the optimized land use, the selection of low noise aircrafts, the Fly Quit Program, the relocation of sensitive receptors and the noise insulation of sensitive buildings. Finally, the expansion project is justified and its feasibility is confirmed.

  6. Investigation of energy management strategies for photovoltaic systems - A predictive control algorithm

    NASA Technical Reports Server (NTRS)

    Cull, R. C.; Eltimsahy, A. H.

    1983-01-01

    The present investigation is concerned with the formulation of energy management strategies for stand-alone photovoltaic (PV) systems, taking into account a basic control algorithm for a possible predictive, (and adaptive) controller. The control system controls the flow of energy in the system according to the amount of energy available, and predicts the appropriate control set-points based on the energy (insolation) available by using an appropriate system model. Aspects of adaptation to the conditions of the system are also considered. Attention is given to a statistical analysis technique, the analysis inputs, the analysis procedure, and details regarding the basic control algorithm.

  7. Predictive control of SOFC based on a GA-RBF neural network model

    NASA Astrophysics Data System (ADS)

    Wu, Xiao-Juan; Zhu, Xin-Jian; Cao, Guang-Yi; Tu, Heng-Yong

    Transients in a load have a significant impact on the performance and durability of a solid oxide fuel cell (SOFC) system. One of the main reasons is that the fuel utilization changes drastically due to the load change. Therefore, in order to guarantee the fuel utilization to operate within a safe range, a nonlinear model predictive control (MPC) method is proposed to control the stack terminal voltage as a proper constant in this paper. The nonlinear predictive controller is based on an improved radial basis function (RBF) neural network identification model. During the process of modeling, the genetic algorithm (GA) is used to optimize the parameters of RBF neural networks. And then a nonlinear predictive control algorithm is applied to track the voltage of the SOFC. Compared with the constant fuel utilization control method, the simulation results show that the nonlinear predictive control algorithm based on the GA-RBF model performs much better.

  8. Happiness as a motivator: positive affect predicts primary control striving for career and educational goals.

    PubMed

    Haase, Claudia M; Poulin, Michael J; Heckhausen, Jutta

    2012-08-01

    What motivates individuals to invest time and effort and overcome obstacles (i.e., strive for primary control) when pursuing important goals? We propose that positive affect predicts primary control striving for career and educational goals, and we explore the mediating role of control beliefs. In Study 1, positive affect predicted primary control striving for career goals in a two-wave longitudinal study of a U.S. sample. In Study 2, positive affect predicted primary control striving for career and educational goals and objective career outcomes in a six-wave longitudinal study of a German sample. Control beliefs partially mediated the longitudinal associations with primary control striving. Thus, when individuals experience positive affect, they become more motivated to invest time and effort, and overcome obstacles when pursuing their goals, in part because they believe they have more control over attaining their goals.

  9. Meditation-induced states predict attentional control over time.

    PubMed

    Colzato, Lorenza S; Sellaro, Roberta; Samara, Iliana; Baas, Matthijs; Hommel, Bernhard

    2015-12-01

    Meditation is becoming an increasingly popular topic for scientific research and various effects of extensive meditation practice (ranging from weeks to several years) on cognitive processes have been demonstrated. Here we show that extensive practice may not be necessary to achieve those effects. Healthy adult non-meditators underwent a brief single session of either focused attention meditation (FAM), which is assumed to increase top-down control, or open monitoring meditation (OMM), which is assumed to weaken top-down control, before performing an Attentional Blink (AB) task - which assesses the efficiency of allocating attention over time. The size of the AB was considerably smaller after OMM than after FAM, which suggests that engaging in meditation immediately creates a cognitive-control state that has a specific impact on how people allocate their attention over time.

  10. Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking

    DTIC Science & Technology

    2015-07-01

    feedback control to generate desired lateral and angular velocities to compensate for vehicle slip rates. Finally, they use the robot’s inverse dynamics to...Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking Chris J. Ostafew Institute for Aerospace Studies...paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithm to achieve high-performance path tracking in challenging off-road

  11. Adaptive and predictive control of a simulated robot arm.

    PubMed

    Tolu, Silvia; Vanegas, Mauricio; Garrido, Jesús A; Luque, Niceto R; Ros, Eduardo

    2013-06-01

    In this work, a basic cerebellar neural layer and a machine learning engine are embedded in a recurrent loop which avoids dealing with the motor error or distal error problem. The presented approach learns the motor control based on available sensor error estimates (position, velocity, and acceleration) without explicitly knowing the motor errors. The paper focuses on how to decompose the input into different components in order to facilitate the learning process using an automatic incremental learning model (locally weighted projection regression (LWPR) algorithm). LWPR incrementally learns the forward model of the robot arm and provides the cerebellar module with optimal pre-processed signals. We present a recurrent adaptive control architecture in which an adaptive feedback (AF) controller guarantees a precise, compliant, and stable control during the manipulation of objects. Therefore, this approach efficiently integrates a bio-inspired module (cerebellar circuitry) with a machine learning component (LWPR). The cerebellar-LWPR synergy makes the robot adaptable to changing conditions. We evaluate how this scheme scales for robot-arms of a high number of degrees of freedom (DOFs) using a simulated model of a robot arm of the new generation of light weight robots (LWRs).

  12. Integration of Predictive Routing Information with Dynamic Traffic Signal Control

    DTIC Science & Technology

    1994-05-01

    vehicles without the on-board guidance aid (Harris, S., Rabone , A., et.al., 1992). The simulation developed was called ROute GUidance Simulation (ROGUS...Florida. Harris, S., Rabone , A., et.al. 1992. ROGUS: A Simulation of Dynamic Route Guidance Systems. Traffic Engineering and Control(33)327-329

  13. Cognitive Control Predicts Academic Achievement in Kindergarten Children

    ERIC Educational Resources Information Center

    Coldren, Jeffrey T.

    2013-01-01

    Children's ability to shift behavior in response to changing environmental demands is critical for successful intellectual functioning. While the processes underlying the development of cognitive control have been thoroughly investigated, its functioning in an ecologically relevant setting such as school is less well understood. Given the alarming…

  14. New technologies in predicting, preventing and controlling emerging infectious diseases.

    PubMed

    Christaki, Eirini

    2015-01-01

    Surveillance of emerging infectious diseases is vital for the early identification of public health threats. Emergence of novel infections is linked to human factors such as population density, travel and trade and ecological factors like climate change and agricultural practices. A wealth of new technologies is becoming increasingly available for the rapid molecular identification of pathogens but also for the more accurate monitoring of infectious disease activity. Web-based surveillance tools and epidemic intelligence methods, used by all major public health institutions, are intended to facilitate risk assessment and timely outbreak detection. In this review, we present new methods for regional and global infectious disease surveillance and advances in epidemic modeling aimed to predict and prevent future infectious diseases threats.

  15. New technologies in predicting, preventing and controlling emerging infectious diseases

    PubMed Central

    Christaki, Eirini

    2015-01-01

    Surveillance of emerging infectious diseases is vital for the early identification of public health threats. Emergence of novel infections is linked to human factors such as population density, travel and trade and ecological factors like climate change and agricultural practices. A wealth of new technologies is becoming increasingly available for the rapid molecular identification of pathogens but also for the more accurate monitoring of infectious disease activity. Web-based surveillance tools and epidemic intelligence methods, used by all major public health institutions, are intended to facilitate risk assessment and timely outbreak detection. In this review, we present new methods for regional and global infectious disease surveillance and advances in epidemic modeling aimed to predict and prevent future infectious diseases threats. PMID:26068569

  16. Prediction and control of slender-wing rock

    NASA Technical Reports Server (NTRS)

    Kandil, Osama A.; Salman, Ahmed A.

    1992-01-01

    The unsteady Euler equations and the Euler equations of rigid-body dynamics, both written in the moving frame of reference, are sequentially solved to simulate the limit-cycle rock motion of slender delta wings. The governing equations of the fluid flow and the dynamics of the present multidisciplinary problem are solved using an implicit, approximately-factored, central-difference-like, finite-volume scheme and a four-stage Runge-Kutta scheme, respectively. For the control of wing-rock motion, leading-edge flaps are forced to oscillate anti-symmetrically at prescribed frequency and amplitude, which are tuned in order to suppress the rock motion. Since the computational grid deforms due to the leading-edge flaps motion, the grid is dynamically deformed using the Navier-displacement equations. Computational applications cover locally-conical and three-dimensional solutions for the wing-rock simulation and its control.

  17. Motor prediction in Brain-Computer Interfaces for controlling mobile robots.

    PubMed

    Geng, Tao; Gan, John Q

    2008-01-01

    EEG-based Brain-Computer Interface (BCI) can be regarded as a new channel for motor control except that it does not involve muscles. Normal neuromuscular motor control has two fundamental components: (1) to control the body, and (2) to predict the consequences of the control command, which is called motor prediction. In this study, after training with a specially designed BCI paradigm based on motor imagery, two subjects learnt to predict the time course of some features of the EEG signals. It is shown that, with this newly-obtained motor prediction skill, subjects can use motor imagery of feet to directly control a mobile robot to avoid obstacles and reach a small target in a time-critical scenario.

  18. A Novel Method to Predict Circulation Control Noise

    DTIC Science & Technology

    2016-03-17

    OASPL computed from 1 to 16 kHz. . . . . . . . 162 5 LIST OF FIGURES 1.1 A sample circulation control airfoil...Doppler effect included and (c),(d) Doppler effect removed. . .......... . . 82 3.32 (a) Spectral and (b) time series data comparison between Curle ’s...analogy employing the DNS full forces and DNS pressure at r = 75, () = 80°. 83 3.33 (a) Spectral and (b) time series data comparison between Curle’s

  19. A computerized test of self-control predicts classroom behavior.

    PubMed

    Hoerger, Marguerite L; Mace, F Charles

    2006-01-01

    We assessed choices on a computerized test of self-control (CTSC) for a group of children with features of attention deficit hyperactivity disorder (ADHD) and a group of controls. Thirty boys participated in the study. Fifteen of the children had been rated by their parents as hyperactive and inattentive, and 15 were age- and gender-matched controls in the same classroom. The children were observed in the classroom for three consecutive mornings, and data were collected on their activity levels and attention. The CTSC consisted of two tasks. In the delay condition, children chose to receive three rewards after a delay of 60 s or one reward immediately. In the task-difficulty condition, the children chose to complete a difficult math problem and receive three rewards or complete an easier problem for one reward. The children with ADHD features made more impulsive choices than their peers during both conditions, and these choices correlated with measures of their activity and attention in the classroom.

  20. Asynchronous update based networked predictive control system using a novel proactive compensation strategy.

    PubMed

    Duan, Yingyao; Zuo, Xin; Liu, Jianwei

    2016-01-01

    Networked predictive control system (NPCS) has been proposed to address random delays and data dropouts in networked control systems (NCSs). A remaining challenge of this approach is that the controller has uncertain information about the actual control inputs, which leads to the predicted control input errors. The main contribution of this paper is to develop an explicit mechanism running in the distributed network nodes asynchronously, which enables the controller node to keep informed of the states of the actuator node without a priori knowledge about the network. Based on this mechanism, a novel proactive compensation strategy is proposed to develop asynchronous update based networked predictive control system (AUBNPCS). The stability criterion of AUBNPCS is derived analytically. A simulation experiment based on Truetime demonstrates the effectiveness of the scheme.

  1. Nonlinear model identification and adaptive model predictive control using neural networks.

    PubMed

    Akpan, Vincent A; Hassapis, George D

    2011-04-01

    This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time.

  2. Predictive optimal control of sewer networks using CORAL tool: application to Riera Blanca catchment in Barcelona.

    PubMed

    Puig, V; Cembrano, G; Romera, J; Quevedo, J; Aznar, B; Ramón, G; Cabot, J

    2009-01-01

    This paper deals with the global control of the Riera Blanca catchment in the Barcelona sewer network using a predictive optimal control approach. This catchment has been modelled using a conceptual modelling approach based on decomposing the catchments in subcatchments and representing them as virtual tanks. This conceptual modelling approach allows real-time model calibration and control of the sewer network. The global control problem of the Riera Blanca catchment is solved using a optimal/predictive control algorithm. To implement the predictive optimal control of the Riera Blanca catchment, a software tool named CORAL is used. The on-line control is simulated by interfacing CORAL with a high fidelity simulator of sewer networks (MOUSE). CORAL interchanges readings from the limnimeters and gate commands with MOUSE as if it was connected with the real SCADA system. Finally, the global control results obtained using the predictive optimal control are presented and compared against the results obtained using current local control system. The results obtained using the global control are very satisfactory compared to those obtained using the local control.

  3. The Minimal Control Principle Predicts Strategy Shifts in the Abstract Decision Making Task

    ERIC Educational Resources Information Center

    Taatgen, Niels A.

    2011-01-01

    The minimal control principle (Taatgen, 2007) predicts that people strive for problem-solving strategies that require as few internal control states as possible. In an experiment with the Abstract Decision Making task (ADM task; Joslyn & Hunt, 1998) the reward structure was manipulated to make either a low-control strategy or a high-strategy…

  4. A predictive controller based on transient simulations for controlling a power plant

    NASA Astrophysics Data System (ADS)

    Svingen, B.

    2016-11-01

    A predictive governor based on an embedded, online transient simulation was commissioned at Tonstad power plant in Norway in December 2014. This governor controls each individual turbine governor by feeding them modified setpoints. Tonstad power plant consists of 4 × 160 MW + 1 × 320 MW high head Francis turbines. With a yearly production of 3888 GWh, it is the largest in Norway. The plant is a typical high head Norwegian plant with very long tunnels and correspondingly active dynamic behaviour. This new governor system continuously simulates the entire plant, and appropriate actions are taken automatically by special algorithms. The simulations are based on the method of characteristics (MOC). The governing system has been in full operational mode since December 19 2014. The testing period also included special acceptance tests to be able to deliver FRR, both on the Nordic grid and on DC cable to Denmark. Although in full operational mode, this system is still a prototype under constant development. It shows a new way of using transient analysis that may become increasingly important in the future with added power from un-regulated sources such as wind, solar and bio.

  5. Model analysis of remotely controlled rendezvous and docking with display prediction

    NASA Technical Reports Server (NTRS)

    Milgram, P.; Wewerinke, P. H.

    1986-01-01

    Manual control of rendezvous and docking (RVD) of two spacecraft in low earth orbit by a remote human operator is discussed. Experimental evidence has shown that control performance degradation for large transmission delays (between spacecraft and operations control center) can be substantially improved by the introduction of predictor displays. An intial Optimal Control Model (OCM) analysis of RVD translational and rotational perturbation control was performed, with emphasis placed on the predictive capabilities of the combined Kalman estimator/optimal predictor with respect to control performance, for a range of time delays, motor noise levels and tracking axes. OCM predictions are then used as a reference for comparing tracking performance with a simple predictor display, as well as with no display prediction at all. Use is made here of an imperfect internal model formulation, whereby it is assumed that the human operator has no knowledge of the system transmission delay.

  6. Jet Engine Noise Generation, Prediction and Control. Chapter 86

    NASA Technical Reports Server (NTRS)

    Huff, Dennis L.; Envia, Edmane

    2004-01-01

    . An example of this type of engine is shown in Figure IC, which is a schematic of the Honeywell T55 engine that powers the CH-47 Chinook helicopter. Since the noise from the propellers or helicopter rotors is usually dominant for turbo-shaft engines, less attention has been paid to these engines in so far as community noise considerations are concerned. This chapter will concentrate mostly on turbofan engine noise and will highlight common methods for their noise prediction and reduction.

  7. On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid

    NASA Astrophysics Data System (ADS)

    Velarde, P.; Valverde, L.; Maestre, J. M.; Ocampo-Martinez, C.; Bordons, C.

    2017-03-01

    In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.

  8. Prediction and Control of Vortex Dominated and Vortex-wake Flows

    NASA Technical Reports Server (NTRS)

    Kandil, Osama

    1996-01-01

    This report describes the activities and accomplishments under this research grant, including a list of publications and dissertations, produced in the field of prediction and control of vortex dominated and vortex wake flows.

  9. Well-being and control in older persons: the prediction of well-being from control measures.

    PubMed

    Smits, C H; Deeg, D J; Bosscher, R J

    1995-01-01

    The interrelation of six facets of control and their ability to predict well-being in older persons were studied in an age and gender stratified community sample aged fifty-five to eighty-nine. An extended conceptual framework of control facets is introduced including "established" facets, such as mastery, self-efficacy and internal health locus of control and "new" control facets such as neuroticism, social inadequacy, and sense of coherence. An interview and a postal questionnaire included measures of the control facets and the Affect Balance Scale. Correlations between control measures were mostly modest. Negative affect was predicted by neuroticism and sense of coherence. Tendencies of independent association of mastery with global well-being and of social inadequacy with positive affect were established.

  10. Auxiliary particle filter-model predictive control of the vacuum arc remelting process

    NASA Astrophysics Data System (ADS)

    Lopez, F.; Beaman, J.; Williamson, R.

    2016-07-01

    Solidification control is required for the suppression of segregation defects in vacuum arc remelting of superalloys. In recent years, process controllers for the VAR process have been proposed based on linear models, which are known to be inaccurate in highly-dynamic conditions, e.g. start-up, hot-top and melt rate perturbations. A novel controller is proposed using auxiliary particle filter-model predictive control based on a nonlinear stochastic model. The auxiliary particle filter approximates the probability of the state, which is fed to a model predictive controller that returns an optimal control signal. For simplicity, the estimation and control problems are solved using Sequential Monte Carlo (SMC) methods. The validity of this approach is verified for a 430 mm (17 in) diameter Alloy 718 electrode melted into a 510 mm (20 in) diameter ingot. Simulation shows a more accurate and smoother performance than the one obtained with an earlier version of the controller.

  11. The role of action prediction and inhibitory control for joint action coordination in toddlers.

    PubMed

    Meyer, M; Bekkering, H; Haartsen, R; Stapel, J C; Hunnius, S

    2015-11-01

    From early in life, young children eagerly engage in social interactions. Yet, they still have difficulties in performing well-coordinated joint actions with others. Adult literature suggests that two processes are important for smooth joint action coordination: action prediction and inhibitory control. The aim of the current study was to disentangle the potential role of these processes in the early development of joint action coordination. Using a simple turn-taking game, we assessed 2½-year-old toddlers' joint action coordination, focusing on timing variability and turn-taking accuracy. In two additional tasks, we examined their action prediction capabilities with an eye-tracking paradigm and examined their inhibitory control capabilities with a classic executive functioning task (gift delay task). We found that individual differences in action prediction and inhibitory action control were distinctly related to the two aspects of joint action coordination. Toddlers who showed more precision in their action predictions were less variable in their action timing during the joint play. Furthermore, toddlers who showed more inhibitory control in an individual context were more accurate in their turn-taking performance during the joint action. On the other hand, no relation between timing variability and inhibitory control or between turn-taking accuracy and action prediction was found. The current results highlight the distinct role of action prediction and inhibitory action control for the quality of joint action coordination in toddlers. Underlying neurocognitive mechanisms and implications for processes involved in joint action coordination in general are discussed.

  12. Serum C-reactive protein in asthma and its ability in predicting asthma control, a case-control study

    PubMed Central

    Monadi, Mahmoud; Firouzjahi, Alireza; Hosseini, Amin; Javadian, Yahya; Sharbatdaran, Majid; Heidari, Behzad

    2016-01-01

    Background: Increased serum high sensitive C-reactive protein (hs-CRP) in asthma and its association with disease severity has been investigated in many studies. This study aimed to determine serum hs-CRP status in asthma versus healthy controls and to examine its ability in predicting asthma control. Methods: Serum CRP was measured by ELISA method using a high sensitive CRP kit. Severity of asthma was determined using Asthma Control Test. Spearman and chi square tests were used for association and correlation respectively. The predictive ability was determined by receiver operating characteristics (ROC) analysis. Accuracy was determined by determination of area under the ROC curve (AUC). Results: A total of 120 patients and 115 controls were studied. Median serum hs-CRP in asthma was higher than control (P=0.001. In well controlled asthma the hs-CRP decreased significantly compared with poorly controlled (P=0.024) but still was higher than control (P=0.017). Serum hs-CRP at cutoff level of 1.45 mg/L differentiated the patients and controls with accuracy of 63.5 % (AUC= 0.635±0.037, P=0.001). Serum hs-CRP ≤ 2.15 mg/L predicted well controlled asthma with accuracy of 62.5% (AUC= 0.625±0.056, p=0.025). After adjusting for age, sex, weight and smoking, there was an independent association between serum hs-CRP >1.45 mg/L and asthma by adjusted OR=2.49, p=0.018). Conclusion: These findings indicate that serum hs-CRP in asthma is higher than healthy control and increases with severity of asthma and decreases with. Thus, serum hs-CRP measurement can be helpful in predicting asthma control and treatment response. PMID:26958331

  13. Study on model current predictive control method of PV grid- connected inverters systems with voltage sag

    NASA Astrophysics Data System (ADS)

    Jin, N.; Yang, F.; Shang, S. Y.; Tao, T.; Liu, J. S.

    2016-08-01

    According to the limitations of the LVRT technology of traditional photovoltaic inverter existed, this paper proposes a low voltage ride through (LVRT) control method based on model current predictive control (MCPC). This method can effectively improve the photovoltaic inverter output characteristics and response speed. The MCPC method of photovoltaic grid-connected inverter designed, the sum of the absolute value of the predictive current and the given current error is adopted as the cost function with the model predictive control method. According to the MCPC, the optimal space voltage vector is selected. Photovoltaic inverter has achieved automatically switches of priority active or reactive power control of two control modes according to the different operating states, which effectively improve the inverter capability of LVRT. The simulation and experimental results proves that the proposed method is correct and effective.

  14. The Impact of Trajectory Prediction Uncertainty on Air Traffic Controller Performance and Acceptability

    NASA Technical Reports Server (NTRS)

    Mercer, Joey S.; Bienert, Nancy; Gomez, Ashley; Hunt, Sarah; Kraut, Joshua; Martin, Lynne; Morey, Susan; Green, Steven M.; Prevot, Thomas; Wu, Minghong G.

    2013-01-01

    A Human-In-The-Loop air traffic control simulation investigated the impact of uncertainties in trajectory predictions on NextGen Trajectory-Based Operations concepts, seeking to understand when the automation would become unacceptable to controllers or when performance targets could no longer be met. Retired air traffic controllers staffed two en route transition sectors, delivering arrival traffic to the northwest corner-post of Atlanta approach control under time-based metering operations. Using trajectory-based decision-support tools, the participants worked the traffic under varying levels of wind forecast error and aircraft performance model error, impacting the ground automations ability to make accurate predictions. Results suggest that the controllers were able to maintain high levels of performance, despite even the highest levels of trajectory prediction errors.

  15. Is It Really Self-Control? Examining the Predictive Power of the Delay of Gratification Task

    PubMed Central

    Duckworth, Angela L.; Tsukayama, Eli; Kirby, Teri A.

    2013-01-01

    This investigation tests whether the predictive power of the delay of gratification task (colloquially known as the “marshmallow test”) derives from its assessment of self-control or of theoretically unrelated traits. Among 56 school-age children in Study 1, delay time was associated with concurrent teacher ratings of self-control and Big Five conscientiousness—but not with other personality traits, intelligence, or reward-related impulses. Likewise, among 966 preschool children in Study 2, delay time was consistently associated with concurrent parent and caregiver ratings of self-control but not with reward-related impulses. While delay time in Study 2 was also related to concurrently measured intelligence, predictive relations with academic, health, and social outcomes in adolescence were more consistently explained by ratings of effortful control. Collectively, these findings suggest that delay task performance may be influenced by extraneous traits, but its predictive power derives primarily from its assessment of self-control. PMID:23813422

  16. Is it really self-control? Examining the predictive power of the delay of gratification task.

    PubMed

    Duckworth, Angela L; Tsukayama, Eli; Kirby, Teri A

    2013-07-01

    This investigation tests whether the predictive power of the delay of gratification task (colloquially known as the "marshmallow test") derives from its assessment of self-control or of theoretically unrelated traits. Among 56 school-age children in Study 1, delay time was associated with concurrent teacher ratings of self-control and Big Five conscientiousness-but not with other personality traits, intelligence, or reward-related impulses. Likewise, among 966 preschool children in Study 2, delay time was consistently associated with concurrent parent and caregiver ratings of self-control but not with reward-related impulses. While delay time in Study 2 was also related to concurrently measured intelligence, predictive relations with academic, health, and social outcomes in adolescence were more consistently explained by ratings of effortful control. Collectively, these findings suggest that delay task performance may be influenced by extraneous traits, but its predictive power derives primarily from its assessment of self-control.

  17. Quantitatively predictable control of Drosophila transcriptional enhancers in vivo with engineered transcription factors.

    PubMed

    Crocker, Justin; Ilsley, Garth R; Stern, David L

    2016-03-01

    Genes are regulated by transcription factors that bind to regions of genomic DNA called enhancers. Considerable effort is focused on identifying transcription factor binding sites, with the goal of predicting gene expression from DNA sequence. Despite this effort, general, predictive models of enhancer function are currently lacking. Here we combine quantitative models of enhancer function with manipulations using engineered transcription factors to examine the extent to which enhancer function can be controlled in a quantitatively predictable manner. Our models, which incorporate few free parameters, can accurately predict the contributions of ectopic transcription factor inputs. These models allow the predictable 'tuning' of enhancers, providing a framework for the quantitative control of enhancers with engineered transcription factors.

  18. Predictive control strategies for wind turbine system based on permanent magnet synchronous generator.

    PubMed

    Maaoui-Ben Hassine, Ikram; Naouar, Mohamed Wissem; Mrabet-Bellaaj, Najiba

    2016-05-01

    In this paper, Model Predictive Control and Dead-beat predictive control strategies are proposed for the control of a PMSG based wind energy system. The proposed MPC considers the model of the converter-based system to forecast the possible future behavior of the controlled variables. It allows selecting the voltage vector to be applied that leads to a minimum error by minimizing a predefined cost function. The main features of the MPC are low current THD and robustness against parameters variations. The Dead-beat predictive control is based on the system model to compute the optimum voltage vector that ensures zero-steady state error. The optimum voltage vector is then applied through Space Vector Modulation (SVM) technique. The main advantages of the Dead-beat predictive control are low current THD and constant switching frequency. The proposed control techniques are presented and detailed for the control of back-to-back converter in a wind turbine system based on PMSG. Simulation results (under Matlab-Simulink software environment tool) and experimental results (under developed prototyping platform) are presented in order to show the performances of the considered control strategies.

  19. Model predictive control system and method for integrated gasification combined cycle power generation

    DOEpatents

    Kumar, Aditya; Shi, Ruijie; Kumar, Rajeeva; Dokucu, Mustafa

    2013-04-09

    Control system and method for controlling an integrated gasification combined cycle (IGCC) plant are provided. The system may include a controller coupled to a dynamic model of the plant to process a prediction of plant performance and determine a control strategy for the IGCC plant over a time horizon subject to plant constraints. The control strategy may include control functionality to meet a tracking objective and control functionality to meet an optimization objective. The control strategy may be configured to prioritize the tracking objective over the optimization objective based on a coordinate transformation, such as an orthogonal or quasi-orthogonal projection. A plurality of plant control knobs may be set in accordance with the control strategy to generate a sequence of coordinated multivariable control inputs to meet the tracking objective and the optimization objective subject to the prioritization resulting from the coordinate transformation.

  20. Controllability Modulates the Neural Response to Predictable but not Unpredictable Threat in Humans

    PubMed Central

    Wood, Kimberly H.; Wheelock, Muriah D.; Shumen, Joshua R.; Bowen, Kenton H.; Ver Hoef, Lawrence W.; Knight, David C.

    2015-01-01

    Stress resilience is mediated, in part, by our ability to predict and control threats within our environment. Therefore, determining the neural mechanisms that regulate the emotional response to predictable and controllable threat may provide important new insight into the processes that mediate resilience to emotional dysfunction and guide the future development of interventions for anxiety disorders. To better understand the effect of predictability and controllability on threat-related brain activity in humans, two groups of healthy volunteers participated in a yoked Pavlovian fear conditioning study during functional magnetic resonance imaging (fMRI). Threat predictability was manipulated by presenting an aversive unconditioned stimulus (UCS) that was either preceded by a conditioned stimulus (i.e., predictable) or by presenting the UCS alone (i.e., unpredictable). Similar to animal model research that has employed yoked fear conditioning procedures, one group (Controllable Condition; CC), but not the other group (Uncontrollable Condition; UC) was able to terminate the UCS. The fMRI signal response within the dorsolateral prefrontal cortex (PFC), dorsomedial PFC, ventromedial PFC, and posterior cingulate was diminished during predictable compared to unpredictable threat (i.e., UCS). In addition, threat-related activity within the ventromedial PFC and bilateral hippocampus was diminished only to threats that were both predictable and controllable. These findings provide insight into how threat predictability and controllability affects the activity of brain regions (i.e., ventromedial PFC and hippocampus) involved in emotion regulation, and may have important implications for better understanding neural processes that mediate emotional resilience to stress. PMID:26149610

  1. Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model

    NASA Astrophysics Data System (ADS)

    Liu, Kailong; Li, Kang; Zhang, Cheng

    2017-04-01

    Battery temperature is a primary factor affecting the battery performance, and suitable battery temperature control in particular internal temperature control can not only guarantee battery safety but also improve its efficiency. This is however challenging as current controller designs for battery charging have no mechanisms to incorporate such information. This paper proposes a novel battery charging control strategy which applies the constrained generalized predictive control (GPC) to charge a LiFePO4 battery based on a newly developed coupled thermoelectric model. The control target primarily aims to maintain the battery cell internal temperature within a desirable range while delivering fast charging. To achieve this, the coupled thermoelectric model is firstly introduced to capture the battery behaviours in particular SOC and internal temperature which are not directly measurable in practice. Then a controlled auto-regressive integrated moving average (CARIMA) model whose parameters are identified by the recursive least squares (RLS) algorithm is developed as an online self-tuning predictive model for a GPC controller. Then the constrained generalized predictive controller is developed to control the charging current. Experiment results confirm the effectiveness of the proposed control strategy. Further, the best region of heat dissipation rate and proper internal temperature set-points are also investigated and analysed.

  2. Feasibility study on a perceived fatigue prediction dependent power control for an electrically assisted bicycle.

    PubMed

    Kiryu, T; Minagawa, H

    2013-01-01

    Several types of electric motor assists have been developed, as a result, it is important to control muscular fatigue on-site in terms of health promotion and motor rehabilitation. Predicting the perceived fatigue by several biosignal-related variables with the multiple regression model and polynomial approximation, we try to propose a self control design for the electrically assisted bicycle (EAB). We also determine the meaningful muscles during pedaling by muscle synergies in relation to the motion maturity. In field experiments, prediction of ongoing perceived physical fatigue could have the potential of suitable control of EAB.

  3. Model predictive control of a combined heat and power plant using local linear models

    SciTech Connect

    Kikstra, J.F.; Roffel, B.; Schoen, P.

    1998-10-01

    Model predictive control has been applied to control of a combined heat and power plant. One of the main features of this plant is that it exhibits nonlinear process behavior due to large throughput swings. In this application, the operating window of the plant has been divided into a number of smaller windows in which the nonlinear process behavior has been approximated by linear behavior. For each operating window, linear step weight models were developed from a detailed nonlinear first principles model, and the model prediction is calculated based on interpolation between these linear models. The model output at each operating point can then be calculated from four basic linear models, and the required control action can subsequently be calculated with the standard model predictive control approach using quadratic programming.

  4. Artificial neural network implementation of a near-ideal error prediction controller

    NASA Technical Reports Server (NTRS)

    Mcvey, Eugene S.; Taylor, Lynore Denise

    1992-01-01

    A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error

  5. Sense of control predicts depressive and anxious symptoms across the transition to parenthood.

    PubMed

    Keeton, Courtney Pierce; Perry-Jenkins, Maureen; Sayer, Aline G

    2008-04-01

    In this study, the authors examined the relationship between sense of control and depressive and anxious symptoms for mothers and fathers during the 1st year of parenthood. Participants were 153 dual-earner, working-class couples who were recruited during the 3rd trimester of pregnancy at prenatal education courses. Data were collected 1 month antenatally and 1, 4, 6, and 12 months postnatally. Sense of control was decomposed into 2 distinct parts: an enduring component and a malleable component that changes with context. Consistent with a cognitive theory of emotional problems, results demonstrated that a sense of control served a protective function for mental health outcomes. A higher sense of enduring control predicted lower levels of psychological distress for new parents, and increases in control over time predicted decreases in depression and anxiety. Findings hold implications for interventions with expectant parents, such as expanding prenatal education courses to include strategies for enhancing and maintaining a sense of personal control.

  6. Nonlinear Predictive Control of Wind Energy Conversion System Using Dfig with Aerodynamic Torque Observer

    NASA Astrophysics Data System (ADS)

    Kamel, Ouari; Mohand, Ouhrouche; Toufik, Rekioua; Taib, Nabil

    2015-01-01

    In order to improvement of the performances for wind energy conversions systems (WECS), an advanced control techniques must be used. In this paper, as an alternative to conventional PI-type control methods, a nonlinear predictive control (NPC) approach is developed for DFIG-based wind turbine. To enhance the robustness of the controller, a disturbance observer is designed to estimate the aerodynamic torque which is considered as an unknown perturbation. An explicitly analytical form of the optimal predictive controller is given consequently on-line optimization is not necessary The DFIG is fed through the rotor windings by a back-to-back converter controlled by Pulse Width Modulation (PWM), where the stator winding is directly connected to the grid. The presented simulation results show a good performance in trajectory tracking of the proposed strategy and rejection of disturbances is successfully achieved.

  7. Model predictive control based on reduced order models applied to belt conveyor system.

    PubMed

    Chen, Wei; Li, Xin

    2016-11-01

    In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system.

  8. Operational flood control of a low-lying delta system using large time step Model Predictive Control

    NASA Astrophysics Data System (ADS)

    Tian, Xin; van Overloop, Peter-Jules; Negenborn, Rudy R.; van de Giesen, Nick

    2015-01-01

    The safety of low-lying deltas is threatened not only by riverine flooding but by storm-induced coastal flooding as well. For the purpose of flood control, these deltas are mostly protected in a man-made environment, where dikes, dams and other adjustable infrastructures, such as gates, barriers and pumps are widely constructed. Instead of always reinforcing and heightening these structures, it is worth considering making the most of the existing infrastructure to reduce the damage and manage the delta in an operational and overall way. In this study, an advanced real-time control approach, Model Predictive Control, is proposed to operate these structures in the Dutch delta system (the Rhine-Meuse delta). The application covers non-linearity in the dynamic behavior of the water system and the structures. To deal with the non-linearity, a linearization scheme is applied which directly uses the gate height instead of the structure flow as the control variable. Given the fact that MPC needs to compute control actions in real-time, we address issues regarding computational time. A new large time step scheme is proposed in order to save computation time, in which different control variables can have different control time steps. Simulation experiments demonstrate that Model Predictive Control with the large time step setting is able to control a delta system better and much more efficiently than the conventional operational schemes.

  9. PLIO: a generic tool for real-time operational predictive optimal control of water networks.

    PubMed

    Cembrano, G; Quevedo, J; Puig, V; Pérez, R; Figueras, J; Verdejo, J M; Escaler, I; Ramón, G; Barnet, G; Rodríguez, P; Casas, M

    2011-01-01

    This paper presents a generic tool, named PLIO, that allows to implement the real-time operational control of water networks. Control strategies are generated using predictive optimal control techniques. This tool allows the flow management in a large water supply and distribution system including reservoirs, open-flow channels for water transport, water treatment plants, pressurized water pipe networks, tanks, flow/pressure control elements and a telemetry/telecontrol system. Predictive optimal control is used to generate flow control strategies from the sources to the consumer areas to meet future demands with appropriate pressure levels, optimizing operational goals such as network safety volumes and flow control stability. PLIO allows to build the network model graphically and then to automatically generate the model equations used by the predictive optimal controller. Additionally, PLIO can work off-line (in simulation) and on-line (in real-time mode). The case study of Santiago-Chile is presented to exemplify the control results obtained using PLIO off-line (in simulation).

  10. Multiple model predictive control for a hybrid proton exchange membrane fuel cell system

    NASA Astrophysics Data System (ADS)

    Chen, Qihong; Gao, Lijun; Dougal, Roger A.; Quan, Shuhai

    This paper presents a hierarchical predictive control strategy to optimize both power utilization and oxygen control simultaneously for a hybrid proton exchange membrane fuel cell/ultracapacitor system. The control employs fuzzy clustering-based modeling, constrained model predictive control, and adaptive switching among multiple models. The strategy has three major advantages. First, by employing multiple piecewise linear models of the nonlinear system, we are able to use linear models in the model predictive control, which significantly simplifies implementation and can handle multiple constraints. Second, the control algorithm is able to perform global optimization for both the power allocation and oxygen control. As a result, we can achieve the optimization from the entire system viewpoint, and a good tradeoff between transient performance of the fuel cell and the ultracapacitor can be obtained. Third, models of the hybrid system are identified using real-world data from the hybrid fuel cell system, and models are updated online. Therefore, the modeling mismatch is minimized and high control accuracy is achieved. Study results demonstrate that the control strategy is able to appropriately split power between fuel cell and ultracapacitor, avoid oxygen starvation, and so enhance the transient performance and extend the operating life of the hybrid system.

  11. Predictive models of glucose control: roles for glucose-sensing neurones.

    PubMed

    Kosse, C; Gonzalez, A; Burdakov, D

    2015-01-01

    The brain can be viewed as a sophisticated control module for stabilizing blood glucose. A review of classical behavioural evidence indicates that central circuits add predictive (feedforward/anticipatory) control to the reactive (feedback/compensatory) control by peripheral organs. The brain/cephalic control is constructed and engaged, via associative learning, by sensory cues predicting energy intake or expenditure (e.g. sight, smell, taste, sound). This allows rapidly measurable sensory information (rather than slowly generated internal feedback signals, e.g. digested nutrients) to control food selection, glucose supply for fight-or-flight responses or preparedness for digestion/absorption. Predictive control is therefore useful for preventing large glucose fluctuations. We review emerging roles in predictive control of two classes of widely projecting hypothalamic neurones, orexin/hypocretin (ORX) and melanin-concentrating hormone (MCH) cells. Evidence is cited that ORX neurones (i) are activated by sensory cues (e.g. taste, sound), (ii) drive hepatic production, and muscle uptake, of glucose, via sympathetic nerves, (iii) stimulate wakefulness and exploration via global brain projections and (iv) are glucose-inhibited. MCH neurones are (i) glucose-excited, (ii) innervate learning and reward centres to promote synaptic plasticity, learning and memory and (iii) are critical for learning associations useful for predictive control (e.g. using taste to predict nutrient value of food). This evidence is unified into a model for predictive glucose control. During associative learning, inputs from some glucose-excited neurones may promote connections between the 'fast' senses and reward circuits, constructing neural shortcuts for efficient action selection. In turn, glucose-inhibited neurones may engage locomotion/exploration and coordinate the required fuel supply. Feedback inhibition of the latter neurones by glucose would ensure that glucose fluxes they stimulate

  12. Small Body GN&C Research Report: A Robust Model Predictive Control Algorithm with Guaranteed Resolvability

    NASA Technical Reports Server (NTRS)

    Acikmese, Behcet A.; Carson, John M., III

    2005-01-01

    A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees the resolvability of the associated finite-horizon optimal control problem in a receding-horizon implementation. The control consists of two components; (i) feedforward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives, and derivatives in polytopes. An illustrative numerical example is also provided.

  13. A robust model predictive control algorithm for uncertain nonlinear systems that guarantees resolvability

    NASA Technical Reports Server (NTRS)

    Acikmese, Ahmet Behcet; Carson, John M., III

    2006-01-01

    A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees resolvability. With resolvability, initial feasibility of the finite-horizon optimal control problem implies future feasibility in a receding-horizon framework. The control consists of two components; (i) feed-forward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives and derivatives in polytopes. An illustrative numerical example is also provided.

  14. Application of time-variant predictive control to modelling driver steering skill

    NASA Astrophysics Data System (ADS)

    Keen, Steven D.; Cole, David J.

    2011-04-01

    The paper addresses the need for improved mathematical models of human steering control. A multiple-model structure for a driver's internal model of a nonlinear vehicle is proposed. The multiple-model structure potentially offers a straightforward way to represent a range of driver expertise. The internal model is combined with a model predictive steering controller. The controller generates a steering command through the minimisation of a cost function involving vehicle path error. A study of the controller performance during an aggressive, nonlinear steering manoeuvre is provided. Analysis of the controller performance reveals a reduction in the closed-loop controller bandwidth with increasing tyre saturation and fixed controller gains. A parameter study demonstrates that increasing the multiple-model density, increasing the weights on the path error, and increasing the controller knowledge range all improved the path following accuracy of the controller.

  15. Control, Filtering and Prediction for Phased Arrays in Directed Energy Systems

    DTIC Science & Technology

    2016-04-30

    AFRL-RD-PS- AFRL-RD-PS- TR-2016-0029 TR-2016-0029 CONTROL, FILTERING AND PREDICTION FOR PHASED ARRAYS IN DIRECTED ENERGY SYSTEMS Steve Gibson...UNLIMITED. AIR FORCE RESEARCH LABORATORY Directed Energy Directorate 3550 Aberdeen Ave SE AIR FORCE MATERIEL COMMAND KIRTLAND AIR FORCE BASE, NM...Filtering and Prediction for Phased Arrays in Directed Energy Systems 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER

  16. Implicit theories about willpower predict the activation of a rest goal following self-control exertion.

    PubMed

    Job, Veronika; Bernecker, Katharina; Miketta, Stefanie; Friese, Malte

    2015-10-01

    Past research indicates that peoples' implicit theories about the nature of willpower moderate the ego-depletion effect. Only people who believe or were led to believe that willpower is a limited resource (limited-resource theory) showed lower self-control performance after an initial demanding task. As of yet, the underlying processes explaining this moderating effect by theories about willpower remain unknown. Here, we propose that the exertion of self-control activates the goal to preserve and replenish mental resources (rest goal) in people with a limited-resource theory. Five studies tested this hypothesis. In Study 1, individual differences in implicit theories about willpower predicted increased accessibility of a rest goal after self-control exertion. Furthermore, measured (Study 2) and manipulated (Study 3) willpower theories predicted an increased preference for rest-conducive objects. Finally, Studies 4 and 5 provide evidence that theories about willpower predict actual resting behavior: In Study 4, participants who held a limited-resource theory took a longer break following self-control exertion than participants with a nonlimited-resource theory. Longer resting time predicted decreased rest goal accessibility afterward. In Study 5, participants with an induced limited-resource theory sat longer on chairs in an ostensible product-testing task when they had engaged in a task requiring self-control beforehand. This research provides consistent support for a motivational shift toward rest after self-control exertion in people holding a limited-resource theory about willpower.

  17. Predictive-model-based dynamic coordination control strategy for power-split hybrid electric bus

    NASA Astrophysics Data System (ADS)

    Zeng, Xiaohua; Yang, Nannan; Wang, Junnian; Song, Dafeng; Zhang, Nong; Shang, Mingli; Liu, Jianxin

    2015-08-01

    Parameter-matching methods and optimal control strategies of the top-selling hybrid electric vehicle (HEV), namely, power-split HEV, are widely studied. In particular, extant research on control strategy focuses on the steady-state energy management strategy to obtain better fuel economy. However, given that multi-power sources are highly coupled in power-split HEVs and influence one another during mode shifting, conducting research on dynamic coordination control strategy (DCCS) to achieve riding comfort is also important. This paper proposes a predictive-model-based DCCS. First, the dynamic model of the objective power-split HEV is built and the mode shifting process is analyzed based on the developed model to determine the reason for the system shock generated. Engine torque estimation algorithm is then designed according to the principle of the nonlinear observer, and the prediction model of the degree of shock is established based on the theory of model predictive control. Finally, the DCCS with adaptation for a complex driving cycle is realized by combining the feedback control and the predictive model. The presented DCCS is validated on the co-simulation platform of AMESim and Simulink. Results show that the shock during mode shifting is well controlled, thereby improving riding comfort.

  18. Nonlinear modeling and predictive functional control of Hammerstein system with application to the turntable servo system

    NASA Astrophysics Data System (ADS)

    Zhang, Qian; Wang, Qunjing; Li, Guoli

    2016-05-01

    This article deals with the identification of nonlinear model and Nonlinear Predictive Functional Controller (NPFC) design based on the Hammerstein structure for the turntable servo system. As a mechanism with multi-mass rotational system, nonlinearities significantly influence the system operation, especially when the turntable is in the states of zero-crossing distortion or rapid acceleration/deceleration, etc. The field data from identification experiments are processed by Comprehensive Learning Particle Swarm Optimization (CLPSO). As a result, Hammerstein model can be derived to describe the input-output relationship globally, considering all the linear and nonlinear factors of the turntable system. Cross validation results demonstrate good correspondence between the real equipment and the identified model. In the second part of this manuscript, a nonlinear control strategy based on the genetic algorithm and predictive control is developed. The global nonlinear predictive controller is carried out by two steps: (i) build the linear predictive functional controller with state space equations for the linear subsystem of Hammerstein model, and (ii) optimize the global control variable by minimizing the cost function through genetic algorithm. On the basis of distinguish model for turntable and the effectiveness of NPFC, the good performance of tracking ability is achieved in the simulation results.

  19. Emotional attentional control predicts changes in diurnal cortisol secretion following exposure to a prolonged psychosocial stressor.

    PubMed

    Lenaert, Bert; Barry, Tom J; Schruers, Koen; Vervliet, Bram; Hermans, Dirk

    2016-01-01

    Hypothalamic-pituitary-adrenal (HPA) axis irregularities have been associated with several psychological disorders. Hence, the identification of individual difference variables that predict variations in HPA-axis activity represents an important challenge for psychiatric research. We investigated whether self-reported attentional control in emotionally demanding situations prospectively predicted changes in diurnal salivary cortisol secretion following exposure to a prolonged psychosocial stressor. Low ability to voluntarily control attention has previously been associated with anxiety and depressive symptomatology. Attentional control was assessed using the Emotional Attentional Control Scale. In students who were preparing for academic examination, salivary cortisol was assessed before (time 1) and after (time 2) examination. Results showed that lower levels of self-reported emotional attentional control at time 1 (N=90) predicted higher absolute diurnal cortisol secretion and a slower decline in cortisol throughout the day at time 2 (N=71). Difficulty controlling attention during emotional experiences may lead to chronic HPA-axis hyperactivity after prolonged exposure to stress. These results indicate that screening for individual differences may foster prediction of HPA-axis disturbances, paving the way for targeted disorder prevention.

  20. Model Predictive Control application for real time operation of controlled structures for the Water Authority Noorderzijlvest, The Netherlands

    NASA Astrophysics Data System (ADS)

    van Heeringen, Klaas-Jan; Gooijer, Jan; Knot, Floris; Talsma, Jan

    2015-04-01

    In the Netherlands, flood protection has always been a key issue to protect settlements against storm surges and riverine floods. Whereas flood protection traditionally focused on structural measures, nowadays the availability of meteorological and hydrological forecasts enable the application of more advanced real-time control techniques for operating the existing hydraulic infrastructure in an anticipatory and more efficient way. Model Predictive Control (MPC) is a powerful technique to derive optimal control variables with the help of model based predictions evaluated against a control objective. In a project for the regional water authority Noorderzijlvest in the north of the Netherlands, it has been shown that MPC can increase the safety level of the system during flood events by an anticipatory pre-release of water. Furthermore, energy costs of pumps can be reduced by making tactical use of the water storage and shifting pump activities during normal operating conditions to off-peak hours. In this way cheap energy is used in combination of gravity flow through gates during low tide periods. MPC has now been implemented for daily operational use of the whole water system of the water authority Noorderzijlvest. The system developed to a real time decision support system which not only supports the daily operation but is able to directly implement the optimal control settings at the structures. We explain how we set-up and calibrated a prediction model (RTC-Tools) that is accurate and fast enough for optimization purposes, and how we integrated it in the operational flood early warning system (Delft-FEWS). Beside the prediction model, the weights and the factors of the objective function are an important element of MPC, since they shape the control objective. We developed special features in Delft-FEWS to allow the operators to adjust the objective function in order to meet changing requirements and to evaluate different control strategies.

  1. Multi input single output model predictive control of non-linear bio-polymerization process

    SciTech Connect

    Arumugasamy, Senthil Kumar; Ahmad, Z.

    2015-05-15

    This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state space model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.

  2. Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

    NASA Technical Reports Server (NTRS)

    He, Yuning

    2015-01-01

    Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.

  3. A predictive control framework for optimal energy extraction of wind farms

    NASA Astrophysics Data System (ADS)

    Vali, M.; van Wingerden, J. W.; Boersma, S.; Petrović, V.; Kühn, M.

    2016-09-01

    This paper proposes an adjoint-based model predictive control for optimal energy extraction of wind farms. It employs the axial induction factor of wind turbines to influence their aerodynamic interactions through the wake. The performance index is defined here as the total power production of the wind farm over a finite prediction horizon. A medium-fidelity wind farm model is utilized to predict the inflow propagation in advance. The adjoint method is employed to solve the formulated optimization problem in a cost effective way and the first part of the optimal solution is implemented over the control horizon. This procedure is repeated at the next controller sample time providing the feedback into the optimization. The effectiveness and some key features of the proposed approach are studied for a two turbine test case through simulations.

  4. Validation of engineering methods for predicting hypersonic vehicle controls forces and moments

    NASA Technical Reports Server (NTRS)

    Maughmer, M.; Straussfogel, D.; Long, L.; Ozoroski, L.

    1991-01-01

    This work examines the ability of the aerodynamic analysis methods contained in an industry standard conceptual design code, the Aerodynamic Preliminary Analysis System (APAS II), to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds. Predicted control forces and moments generated by various control effectors are compared with previously published wind-tunnel and flight-test data for three vehicles: the North American X-15, a hypersonic research airplane concept, and the Space Shuttle Orbiter. Qualitative summaries of the results are given for each force and moment coefficient and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage.

  5. On Application of Model Predictive Control to Power Converter with Switching

    NASA Astrophysics Data System (ADS)

    Zanma, Tadanao; Fukuta, Junichi; Doki, Shinji; Ishida, Muneaki; Okuma, Shigeru; Matsumoto, Takashi; Nishimori, Eiji

    This paper concerns a DC-DC converter control. In DC-DC converters, there exist both continuous components such as inductance, conductance and resistance and discrete ones, IGBT and MOSFET as semiconductor switching elements. Such a system can be regarded as a hybrid dynamical system. Thus, this paper presents a dc-dc control technique based on the model predictive control. Specifically, a case in which the load of the dc-dc converter changes from active to sleep is considered. In the case, a control method which makes the output voltage follow to the reference quickly in transition, and the switching frequency be constant in steady state. In addition, in applying the model predictive control to power electronics circuits, the switching characteristic of the device and the restriction condition for protection are also considered. The effectiveness of the proposed method is illustrated by comparing a conventional method through some simulation results.

  6. LMI-Based Generation of Feedback Laws for a Robust Model Predictive Control Algorithm

    NASA Technical Reports Server (NTRS)

    Acikmese, Behcet; Carson, John M., III

    2007-01-01

    This technical note provides a mathematical proof of Corollary 1 from the paper 'A Nonlinear Model Predictive Control Algorithm with Proven Robustness and Resolvability' that appeared in the 2006 Proceedings of the American Control Conference. The proof was omitted for brevity in the publication. The paper was based on algorithms developed for the FY2005 R&TD (Research and Technology Development) project for Small-body Guidance, Navigation, and Control [2].The framework established by the Corollary is for a robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems that guarantees the resolvability of the associated nite-horizon optimal control problem in a receding-horizon implementation. Additional details of the framework are available in the publication.

  7. Model predictive control of non-linear systems over networks with data quantization and packet loss.

    PubMed

    Yu, Jimin; Nan, Liangsheng; Tang, Xiaoming; Wang, Ping

    2015-11-01

    This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur. The non-linear controlled plant in the networked control system (NCS) is represented by a Tagaki-Sugeno (T-S) model. The sensed data and control signal are quantized in both links and described as sector bound uncertainties by applying sector bound approach. Then, the quantized data are transmitted in the communication networks and may suffer from the effect of packet losses, which are modeled as Bernoulli process. A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.

  8. Brain mechanisms for predictive control by switching internal models: implications for higher-order cognitive functions.

    PubMed

    Imamizu, Hiroshi; Kawato, Mitsuo

    2009-07-01

    Humans can guide their actions toward the realization of their intentions. Flexible, rapid and precise realization of intentions and goals relies on the brain learning to control its actions on external objects and to predict the consequences of this control. Neural mechanisms that mimic the input-output properties of our own body and other objects can be used to support prediction and control, and such mechanisms are called internal models. We first summarize functional neuroimaging, behavioral and computational studies of the brain mechanisms related to acquisition, modular organization, and the predictive switching of internal models mainly for tool use. These mechanisms support predictive control and flexible switching of intentional actions. We then review recent studies demonstrating that internal models are crucial for the execution of not only immediate actions but also higher-order cognitive functions, including optimization of behaviors toward long-term goals, social interactions based on prediction of others' actions and mental states, and language processing. These studies suggest that a concept of internal models can consistently explain the neural mechanisms and computational principles needed for fundamental sensorimotor functions as well as higher-order cognitive functions.

  9. Real time simulation of nonlinear generalized predictive control for wind energy conversion system with nonlinear observer.

    PubMed

    Ouari, Kamel; Rekioua, Toufik; Ouhrouche, Mohand

    2014-01-01

    In order to make a wind power generation truly cost-effective and reliable, an advanced control techniques must be used. In this paper, we develop a new control strategy, using nonlinear generalized predictive control (NGPC) approach, for DFIG-based wind turbine. The proposed control law is based on two points: NGPC-based torque-current control loop generating the rotor reference voltage and NGPC-based speed control loop that provides the torque reference. In order to enhance the robustness of the controller, a disturbance observer is designed to estimate the aerodynamic torque which is considered as an unknown perturbation. Finally, a real-time simulation is carried out to illustrate the performance of the proposed controller.

  10. Predicting Human Error in Air Traffic Control Decision Support Tools and Free Flight Concepts

    NASA Technical Reports Server (NTRS)

    Mogford, Richard; Kopardekar, Parimal

    2001-01-01

    The document is a set of briefing slides summarizing the work the Advanced Air Transportation Technologies (AATT) Project is doing on predicting air traffic controller and airline pilot human error when using new decision support software tools and when involved in testing new air traffic control concepts. Previous work in this area is reviewed as well as research being done jointly with the FAA. Plans for error prediction work in the AATT Project are discussed. The audience is human factors researchers and aviation psychologists from government and industry.

  11. Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control

    PubMed Central

    Deshpande, Sunil; Nandola, Naresh N.; Rivera, Daniel E.; Younger, Jarred W.

    2014-01-01

    The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health. PMID:25506132

  12. Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control.

    PubMed

    Deshpande, Sunil; Nandola, Naresh N; Rivera, Daniel E; Younger, Jarred W

    2014-12-01

    The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.

  13. Environmental controls on the phenology of moths: predicting plasticity and constraint under climate change.

    PubMed

    Valtonen, Anu; Ayres, Matthew P; Roininen, Heikki; Pöyry, Juha; Leinonen, Reima

    2011-01-01

    Ecological systems have naturally high interannual variance in phenology. Component species have presumably evolved to maintain appropriate phenologies under historical climates, but cases of inappropriate phenology can be expected with climate change. Understanding controls on phenology permits predictions of ecological responses to climate change. We studied phenological control systems in Lepidoptera by analyzing flight times recorded at a network of sites in Finland. We evaluated the strength and form of controls from temperature and photoperiod, and tested for geographic variation within species. Temperature controls on phenology were evident in 51% of 112 study species and for a third of those thermal controls appear to be modified by photoperiodic cues. For 24% of the total, photoperiod by itself emerged as the most likely control system. Species with thermal control alone should be most immediately responsive in phenology to climate warming, but variably so depending upon the minimum temperature at which appreciable development occurs and the thermal responsiveness of development rate. Photoperiodic modification of thermal controls constrains phenotypic responses in phenologies to climate change, but can evolve to permit local adaptation. Our results suggest that climate change will alter the phenological structure of the Finnish Lepidoptera community in ways that are predictable with knowledge of the proximate physiological controls. Understanding how phenological controls in Lepidoptera compare to that of their host plants and enemies could permit general inferences regarding climatic effects on mid- to high-latitude ecosystems.

  14. Offset-Free Model Predictive Control of Open Water Channel Based on Moving Horizon Estimation

    NASA Astrophysics Data System (ADS)

    Ekin Aydin, Boran; Rutten, Martine

    2016-04-01

    Model predictive control (MPC) is a powerful control option which is increasingly used by operational water managers for managing water systems. The explicit consideration of constraints and multi-objective management are important features of MPC. However, due to the water loss in open water systems by seepage, leakage and evaporation a mismatch between the model and the real system will be created. These mismatch affects the performance of MPC and creates an offset from the reference set point of the water level. We present model predictive control based on moving horizon estimation (MHE-MPC) to achieve offset free control of water level for open water canals. MHE-MPC uses the past predictions of the model and the past measurements of the system to estimate unknown disturbances and the offset in the controlled water level is systematically removed. We numerically tested MHE-MPC on an accurate hydro-dynamic model of the laboratory canal UPC-PAC located in Barcelona. In addition, we also used well known disturbance modeling offset free control scheme for the same test case. Simulation experiments on a single canal reach show that MHE-MPC outperforms disturbance modeling offset free control scheme.

  15. Toddler Inhibitory Control, Bold Response to Novelty, and Positive Affect Predict Externalizing Symptoms in Kindergarten

    PubMed Central

    Buss, Kristin A.; Kiel, Elizabeth J.; Morales, Santiago; Robinson, Emily

    2013-01-01

    Poor inhibitory control and bold-approach have been found to predict the development of externalizing behavior problems in young children. Less research has examined how positive affect may influence the development of externalizing behavior in the context of low inhibitory control and high approach. We used a multimethod approach to examine how observed toddler inhibitory control, bold-approach, and positive affect predicted externalizing outcomes (observed, adult- and self-reported) in additive and interactive ways at the beginning of kindergarten. 24-month-olds (N = 110) participated in a laboratory visit and 84 were followed up in kindergarten for externalizing behaviors. Overall, children who were low in inhibitory control, high in bold-approach, and low in positive affect at 24-months of age were at greater risk for externalizing behaviors during kindergarten. PMID:25018589

  16. Nonlinear recurrent neural network predictive control for energy distribution of a fuel cell powered robot.

    PubMed

    Chen, Qihong; Long, Rong; Quan, Shuhai; Zhang, Liyan

    2014-01-01

    This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell.

  17. Design of a robust model predictive controller with reduced computational complexity.

    PubMed

    Razi, M; Haeri, M

    2014-11-01

    The practicality of robust model predictive control of systems with model uncertainties depends on the time consumed for solving a defined optimization problem. This paper presents a method for the computational complexity reduction in a robust model predictive control. First a scaled state vector is defined such that the objective function contours in the defined optimization problem become vertical or horizontal ellipses or circles, and then the control input is determined at each sampling time as a state feedback that minimizes the infinite horizon objective function by solving some linear matrix inequalities. The simulation results show that the number of iterations to solve the problem at each sampling interval is reduced while the control performance does not alter noticeably.

  18. Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot

    PubMed Central

    Chen, Qihong; Long, Rong; Quan, Shuhai

    2014-01-01

    This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell. PMID:24707206

  19. Experimental quadrotor flight performance using computationally efficient and recursively feasible linear model predictive control

    NASA Astrophysics Data System (ADS)

    Jaffery, Mujtaba H.; Shead, Leo; Forshaw, Jason L.; Lappas, Vaios J.

    2013-12-01

    A new linear model predictive control (MPC) algorithm in a state-space framework is presented based on the fusion of two past MPC control laws: steady-state optimal MPC (SSOMPC) and Laguerre optimal MPC (LOMPC). The new controller, SSLOMPC, is demonstrated to have improved feasibility, tracking performance and computation time than its predecessors. This is verified in both simulation and practical experimentation on a quadrotor unmanned air vehicle in an indoor motion-capture testbed. The performance of the control law is experimentally compared with proportional-integral-derivative (PID) and linear quadratic regulator (LQR) controllers in an unconstrained square manoeuvre. The use of soft control output and hard control input constraints is also examined in single and dual constrained manoeuvres.

  20. A new methane control and prediction software suite for longwall mines

    NASA Astrophysics Data System (ADS)

    Dougherty, Heather N.; Özgen Karacan, C.

    2011-09-01

    This paper presents technical and application aspects of a new software suite, MCP (Methane Control and Prediction), developed for addressing some of the methane and methane control issues in longwall coal mines. The software suite consists of dynamic link library (DLL) extensions to MS-Access TM, written in C++. In order to create the DLLs, various statistical, mathematical approaches, prediction and classification artificial neural network (ANN) methods were used. The current version of MCP suite (version 1.3) discussed in this paper has four separate modules that (a) predict the dynamic elastic properties of coal-measure rocks, (b) predict ventilation emissions from longwall mines, (c) determine the type of degasification system that needs to be utilized for given situations and (d) assess the production performance of gob gas ventholes that are used to extract methane from longwall gobs. These modules can be used with the data from basic logs, mining, longwall panel, productivity, and coal bed characteristics. The applications of these modules separately or in combination for methane capture and control related problems will help improve the safety of mines. The software suite's version 1.3 is discussed in this paper. Currently, it's new version 2.0 is available and can be downloaded from http://www.cdc.gov/niosh/mining/products/product180.htm free of charge. The models discussed in this paper can be found under "ancillary models" and under "methane prediction models" for specific U.S. conditions in the new version.

  1. Controlling Bimetallic Nanostructures by the Microemulsion Method with Subnanometer Resolution Using a Prediction Model.

    PubMed

    Buceta, David; Tojo, Concha; Vukmirovic, Miomir B; Deepak, Francis Leonard; López-Quintela, M Arturo

    2015-07-14

    We present a theoretical model to predict the atomic structure of Au/Pt nanoparticles synthesized in microemulsions. Excellent concordance with the experimental results shows that the structure of the nanoparticles can be controlled at subnanometer resolution simply by changing the reactant concentration. The results of this study not only offer a better understanding of the complex mechanisms governing reactions in microemulsions, but open up a simple new way to synthesize bimetallic nanoparticles with ad hoc controlled nanostructures.

  2. T-S fuzzy model predictive speed control of electrical vehicles.

    PubMed

    Khooban, Mohammad Hassan; Vafamand, Navid; Niknam, Taher

    2016-09-01

    This paper proposes a novel nonlinear model predictive controller (MPC) in terms of linear matrix inequalities (LMIs). The proposed MPC is based on Takagi-Sugeno (TS) fuzzy model, a non-parallel distributed compensation (non-PDC) fuzzy controller and a non-quadratic Lyapunov function (NQLF). Utilizing the non-PDC controller together with the Lyapunov theorem guarantees the stabilization issue of this MPC. In this approach, at each sampling time a quadratic cost function with an infinite prediction and control horizon is minimized such that constraints on the control input Euclidean norm are satisfied. To show the merits of the proposed approach, a nonlinear electric vehicle (EV) system with parameter uncertainty is considered as a case study. Indeed, the main goal of this study is to force the speed of EV to track a desired value. The experimental data, a new European driving cycle (NEDC), is used in order to examine the performance of the proposed controller. First, the equivalent TS model of the original nonlinear system is derived. After that, in order to evaluate the proficiency of the proposed controller, the achieved results of the proposed approach are compared with those of the conventional MPC controller and the optimal Fuzzy PI controller (OFPI), which are the latest research on the problem in hand.

  3. Adaptive control of the packet transmission period with solar energy harvesting prediction in wireless sensor networks.

    PubMed

    Kwon, Kideok; Yang, Jihoon; Yoo, Younghwan

    2015-04-24

    A number of research works has studied packet scheduling policies in energy scavenging wireless sensor networks, based on the predicted amount of harvested energy. Most of them aim to achieve energy neutrality, which means that an embedded system can operate perpetually while meeting application requirements. Unlike other renewable energy sources, solar energy has the feature of distinct periodicity in the amount of harvested energy over a day. Using this feature, this paper proposes a packet transmission control policy that can enhance the network performance while keeping sensor nodes alive. Furthermore, this paper suggests a novel solar energy prediction method that exploits the relation between cloudiness and solar radiation. The experimental results and analyses show that the proposed packet transmission policy outperforms others in terms of the deadline miss rate and data throughput. Furthermore, the proposed solar energy prediction method can predict more accurately than others by 6.92%.

  4. A web-based noise control prediction model for rooms using the method of images

    NASA Astrophysics Data System (ADS)

    Dance, Stephen

    2002-11-01

    Previous simple models could only predict sound levels in untreated rooms. Now, using the method of images, it has become possible to accurately predict the sound level in fitted industrial rooms from any computer on the Internet. Thus, a powerful tool in an acoustician's armory is available to all, while requiring only the minimal amount of input data to construct the model. This is only achievable if the scope of the model is reduced to one or two acoustic parameters. Now, two common noise control techniques have been implemented into the image source model: acoustic barriers and absorptive patches. Predictions using the model with and without noise control techniques will be demonstrated, so the advantages can be clearly seen in typical industrial rooms. The models are now available on the web, running directly inside Netscape or Internet Explorer.

  5. Effective variable switching point predictive current control for ac low-voltage drives

    NASA Astrophysics Data System (ADS)

    Stolze, Peter; Karamanakos, Petros; Kennel, Ralph; Manias, Stefanos; Endisch, Christian

    2015-07-01

    This paper presents an effective model predictive current control scheme for induction machines driven by a three-level neutral point clamped inverter, called variable switching point predictive current control. Despite the fact that direct, enumeration-based model predictive control (MPC) strategies are very popular in the field of power electronics due to their numerous advantages such as design simplicity and straightforward implementation procedure, they carry two major drawbacks. These are the increased computational effort and the high ripples on the controlled variables, resulting in a limited applicability of such methods. The high ripples occur because in direct MPC algorithms the actuating variable can only be changed at the beginning of a sampling interval. A possible remedy for this would be to change the applied control input within the sampling interval, and thus to apply it for a shorter time than one sample. However, since such a solution would lead to an additional overhead which is crucial especially for multilevel inverters, a heuristic preselection of the optimal control action is adopted to keep the computational complexity at bay. Experimental results are provided to verify the potential advantages of the proposed strategy.

  6. An efficient artificial bee colony algorithm with application to nonlinear predictive control

    NASA Astrophysics Data System (ADS)

    Ait Sahed, Oussama; Kara, Kamel; Benyoucef, Abousoufyane; Laid Hadjili, Mohamed

    2016-05-01

    In this paper a constrained nonlinear predictive control algorithm, that uses the artificial bee colony (ABC) algorithm to solve the optimization problem, is proposed. The main objective is to derive a simple and efficient control algorithm that can solve the nonlinear constrained optimization problem with minimal computational time. Indeed, a modified version, enhancing the exploring and the exploitation capabilities, of the ABC algorithm is proposed and used to design a nonlinear constrained predictive controller. This version allows addressing the premature and the slow convergence drawbacks of the standard ABC algorithm, using a modified search equation, a well-known organized distribution mechanism for the initial population and a new equation for the limit parameter. A convergence statistical analysis of the proposed algorithm, using some well-known benchmark functions is presented and compared with several other variants of the ABC algorithm. To demonstrate the efficiency of the proposed algorithm in solving engineering problems, the constrained nonlinear predictive control of the model of a Multi-Input Multi-Output industrial boiler is considered. The control performances of the proposed ABC algorithm-based controller are also compared to those obtained using some variants of the ABC algorithms.

  7. Lateral prefrontal cortex activity during cognitive control of emotion predicts response to social stress in schizophrenia.

    PubMed

    Tully, Laura M; Lincoln, Sarah Hope; Hooker, Christine I

    2014-01-01

    LPFC dysfunction is a well-established neural impairment in schizophrenia and is associated with worse symptoms. However, how LPFC activation influences symptoms is unclear. Previous findings in healthy individuals demonstrate that lateral prefrontal cortex (LPFC) activation during cognitive control of emotional information predicts mood and behavior in response to interpersonal conflict, thus impairments in these processes may contribute to symptom exacerbation in schizophrenia. We investigated whether schizophrenia participants show LPFC deficits during cognitive control of emotional information, and whether these LPFC deficits prospectively predict changes in mood and symptoms following real-world interpersonal conflict. During fMRI, 23 individuals with schizophrenia or schizoaffective disorder and 24 healthy controls completed the Multi-Source Interference Task superimposed on neutral and negative pictures. Afterwards, schizophrenia participants completed a 21-day online daily-diary in which they rated the extent to which they experienced mood and schizophrenia-spectrum symptoms, as well as the occurrence and response to interpersonal conflict. Schizophrenia participants had lower dorsal LPFC activity (BA9) during cognitive control of task-irrelevant negative emotional information. Within schizophrenia participants, DLPFC activity during cognitive control of emotional information predicted changes in positive and negative mood on days following highly distressing interpersonal conflicts. Results have implications for understanding the specific role of LPFC in response to social stress in schizophrenia, and suggest that treatments targeting LPFC-mediated cognitive control of emotion could promote adaptive response to social stress in schizophrenia.

  8. Model of Predictive Control of a Direct-Fire Projectile Equipped With Canards

    DTIC Science & Technology

    2005-03-01

    matrices, which are sent to the MPC routine. The MPC routine calculates the optimal control sequence over the length of the update interval. When... 0767 . 19. Burchett, B.; Peterson, A.; Costello, M. Prediction of Swerving Motion of a Dual-Spin Projectile With Lateral Pulsejets in Atmospheric

  9. Predictive Value of Morphological Features in Patients with Autism versus Normal Controls

    ERIC Educational Resources Information Center

    Ozgen, H.; Hellemann, G. S.; de Jonge, M. V.; Beemer, F. A.; van Engeland, H.

    2013-01-01

    We investigated the predictive power of morphological features in 224 autistic patients and 224 matched-pairs controls. To assess the relationship between the morphological features and autism, we used the receiver operator curves (ROC). In addition, we used recursive partitioning (RP) to determine a specific pattern of abnormalities that is…

  10. The Ability of Psychological Flexibility and Job Control to Predict Learning, Job Performance, and Mental Health

    ERIC Educational Resources Information Center

    Bond, Frank W.; Flaxman, Paul E.

    2006-01-01

    This longitudinal study tested the degree to which an individual characteristic, psychological flexibility, and a work organization variable, job control, predicted ability to learn new skills at work, job performance, and mental health, amongst call center workers in the United Kingdom (N = 448). As hypothesized, results indicated that job…

  11. Development of advanced stability theory suction prediction techniques for laminar flow control. [on swept wings

    NASA Technical Reports Server (NTRS)

    Srokowski, A. J.

    1978-01-01

    The problem of obtaining accurate estimates of suction requirements on swept laminar flow control wings was discussed. A fast accurate computer code developed to predict suction requirements by integrating disturbance amplification rates was described. Assumptions and approximations used in the present computer code are examined in light of flow conditions on the swept wing which may limit their validity.

  12. Application of Output Predictive Algorithmic Control to a Terrain Following Aircraft System.

    DTIC Science & Technology

    1982-03-01

    Preface I would like to thank my thesis advisor/ Dr. J. Gary Reid, and my thesis committee consisting of Capt. James silverthorn , Dr. John...June 1979, pp 387-392. 3.5 Reid, J. G., Chaffin, D. E., Silverthorn J. T. Output Predictive Algorithmic Control: Precision Tracking With

  13. How Minimal Grade Goals and Self-Control Capacity Interact in Predicting Test Grades

    ERIC Educational Resources Information Center

    Bertrams, Alex

    2012-01-01

    The present research examined the prediction of school students' grades in an upcoming math test via their minimal grade goals (i.e., the minimum grade in an upcoming test one would be satisfied with). Due to its significance for initiating and maintaining goal-directed behavior, self-control capacity was expected to moderate the relation between…

  14. Predictive control of hollow-fiber bioreactors for the production of monoclonal antibodies.

    PubMed

    Dowd, J E; Weber, I; Rodriguez, B; Piret, J M; Kwok, K E

    1999-05-20

    The selection of medium feed rates for perfusion bioreactors represents a challenge for process optimization, particularly in bioreactors that are sampled infrequently. When the present and immediate future of a bioprocess can be adequately described, predictive control can minimize deviations from set points in a manner that can maximize process consistency. Predictive control of perfusion hollow-fiber bioreactors was investigated in a series of hybridoma cell cultures that compared operator control to computer estimation of feed rates. Adaptive software routines were developed to estimate the current and predict the future glucose uptake and lactate production of the bioprocess at each sampling interval. The current and future glucose uptake rates were used to select the perfusion feed rate in a designed response to deviations from the set point values. The routines presented a graphical user interface through which the operator was able to view the up-to-date culture performance and assess the model description of the immediate future culture performance. In addition, fewer samples were taken in the computer-estimated cultures, reducing labor and analytical expense. The use of these predictive controller routines and the graphical user interface decreased the glucose and lactate concentration variances up to sevenfold, and antibody yields increased by 10% to 43%.

  15. Parenting and Child "DRD4" Genotype Interact to Predict Children's Early Emerging Effortful Control

    ERIC Educational Resources Information Center

    Smith, Heather J.; Sheikh, Haroon I.; Dyson, Margaret W.; Olino, Thomas M.; Laptook, Rebecca S.; Durbin, C. Emily; Hayden, Elizabeth P.; Singh, Shiva M.; Klein, Daniel N.

    2012-01-01

    Effortful control (EC), or the trait-like capacity to regulate dominant responses, has important implications for children's development. Although genetic factors and parenting likely influence EC, few studies have examined whether they interact to predict its development. This study examined whether the "DRD4" exon III variable number tandem…

  16. Demonstration of leapfrogging for implementing nonlinear model predictive control on a heat exchanger.

    PubMed

    Sridhar, Upasana Manimegalai; Govindarajan, Anand; Rhinehart, R Russell

    2016-01-01

    This work reveals the applicability of a relatively new optimization technique, Leapfrogging, for both nonlinear regression modeling and a methodology for nonlinear model-predictive control. Both are relatively simple, yet effective. The application on a nonlinear, pilot-scale, shell-and-tube heat exchanger reveals practicability of the techniques.

  17. Direct data-based model predictive control with applications to structures, robotic swarms, and aircraft

    NASA Astrophysics Data System (ADS)

    Barlow, Jonathan S.

    A direct method to design data-based model predictive controllers is presented. The design method uses system identification techniques to identify model predictive controller gains directly from a set of excitation input and disturbance corrupted output. The design is direct in that the controller gains can be designed directly from input and disturbance corrupted output data without an intermediate identification step. The direct design is simpler than previous two-step designs and reduces computation time for the design of the controller. The direct design also enables an adaptive implementation capable of identifying controller gains online. The direct data-based controllers can be used for vibration suppression, disturbance rejection, tracking and is applied to structures, robot swarms and aircraft. For the cases of vibration suppression and disturbance rejection, the data-based controller has the advantage that any disturbances present in the design data are automatically rejected without needing to know the details of the disturbances. For the case of robot swarms, extensions are made for formation control and obstacle avoidance, and the controller can be implemented as a decentralized controller in real time and in parallel on individual vehicles with communication limited to past input and past output data. A formulation for improving the robustness of the controller to parametric variations is also developed. Finally, the adaptive implementation is shown to be useful for the control of linear time-varying systems and has been successfully implemented to control a linear time-varying model of a Cruise Efficient Short Take-Off and Landing (CESTOL) type aircraft.

  18. Adaptive cruise control with stop&go function using the state-dependent nonlinear model predictive control approach.

    PubMed

    Shakouri, Payman; Ordys, Andrzej; Askari, Mohamad R

    2012-09-01

    In the design of adaptive cruise control (ACC) system two separate control loops - an outer loop to maintain the safe distance from the vehicle traveling in front and an inner loop to control the brake pedal and throttle opening position - are commonly used. In this paper a different approach is proposed in which a single control loop is utilized. The objective of the distance tracking is incorporated into the single nonlinear model predictive control (NMPC) by extending the original linear time invariant (LTI) models obtained by linearizing the nonlinear dynamic model of the vehicle. This is achieved by introducing the additional states corresponding to the relative distance between leading and following vehicles, and also the velocity of the leading vehicle. Control of the brake and throttle position is implemented by taking the state-dependent approach. The model demonstrates to be more effective in tracking the speed and distance by eliminating the necessity of switching between the two controllers. It also offers smooth variation in brake and throttle controlling signal which subsequently results in a more uniform acceleration of the vehicle. The results of proposed method are compared with other ACC systems using two separate control loops. Furthermore, an ACC simulation results using a stop&go scenario are shown, demonstrating a better fulfillment of the design requirements.

  19. Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers

    PubMed Central

    2013-01-01

    Background Identification of malaria vector breeding sites can enhance control activities. Although associations between malaria vector breeding sites and topography are well recognized, practical models that predict breeding sites from topographic information are lacking. We used topographic variables derived from remotely sensed Digital Elevation Models (DEMs) to model the breeding sites of malaria vectors. We further compared the predictive strength of two different DEMs and evaluated the predictability of various habitat types inhabited by Anopheles larvae. Methods Using GIS techniques, topographic variables were extracted from two DEMs: 1) Shuttle Radar Topography Mission 3 (SRTM3, 90-m resolution) and 2) the Advanced Spaceborne Thermal Emission Reflection Radiometer Global DEM (ASTER, 30-m resolution). We used data on breeding sites from an extensive field survey conducted on an island in western Kenya in 2006. Topographic variables were extracted for 826 breeding sites and for 4520 negative points that were randomly assigned. Logistic regression modelling was applied to characterize topographic features of the malaria vector breeding sites and predict their locations. Model accuracy was evaluated using the area under the receiver operating characteristics curve (AUC). Results All topographic variables derived from both DEMs were significantly correlated with breeding habitats except for the aspect of SRTM. The magnitude and direction of correlation for each variable were similar in the two DEMs. Multivariate models for SRTM and ASTER showed similar levels of fit indicated by Akaike information criterion (3959.3 and 3972.7, respectively), though the former was slightly better than the latter. The accuracy of prediction indicated by AUC was also similar in SRTM (0.758) and ASTER (0.755) in the training site. In the testing site, both SRTM and ASTER models showed higher AUC in the testing sites than in the training site (0.829 and 0.799, respectively). The

  20. Nonlinear model predictive control of SOFC based on a Hammerstein model

    NASA Astrophysics Data System (ADS)

    Huo, Hai-Bo; Zhu, Xin-Jian; Hu, Wan-Qi; Tu, Heng-Yong; Li, Jian; Yang, Jie

    To protect solid oxide fuel cell (SOFC) stack and meet the voltage demand of DC type loads, two control loops are designed for controlling fuel utilization and output voltage, respectively. A Hammerstein model of the SOFC is first presented for developing effective control strategies, in which the nonlinear static part is approximated by a radial basis function neural network (RBFNN) and the linear dynamic part is modeled by an autoregressive with exogenous input (ARX) model. As we know, the output voltage of the SOFC changes with load variations. After a primary control loop is designed to keep the fuel utilization as a steady-state constant, a nonlinear model predictive control (MPC) based on the Hammerstein model is developed to control the output voltage of the SOFC. The performance of the MPC controller is compared with that of the PI controller developed in [Y.H. Li, S.S. Choi, S. Rajakaruna, An analysis of the control and operation of a solid oxide fuel-cell power plant in an isolated system, IEEE Trans. Energy Convers. 20 (2) (2005) 381-387]. Simulation results demonstrate the potential of the proposed Hammerstein model for application to the control of the SOFC, while the excellence of the nonlinear MPC controller for voltage control of the SOFC is proved.

  1. Model Predictive Optimal Control of a Time-Delay Distributed-Parameter Systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan

    2006-01-01

    This paper presents an optimal control method for a class of distributed-parameter systems governed by first order, quasilinear hyperbolic partial differential equations that arise in many physical systems. Such systems are characterized by time delays since information is transported from one state to another by wave propagation. A general closed-loop hyperbolic transport model is controlled by a boundary control embedded in a periodic boundary condition. The boundary control is subject to a nonlinear differential equation constraint that models actuator dynamics of the system. The hyperbolic equation is thus coupled with the ordinary differential equation via the boundary condition. Optimality of this coupled system is investigated using variational principles to seek an adjoint formulation of the optimal control problem. The results are then applied to implement a model predictive control design for a wind tunnel to eliminate a transport delay effect that causes a poor Mach number regulation.

  2. Mixed H2/H∞ robust model predictive control with saturated inputs

    NASA Astrophysics Data System (ADS)

    Huang, He; Li, Dewei; Xi, Yugeng

    2014-12-01

    In this paper, we investigate the mixed H2/H∞ robust model predictive control (RMPC) for polytopic uncertain systems, which refers to the infinite horizon optimal guaranteed cost control (OGCC). To fully use the capability of actuators, we adopt a saturating feedback control law as the control strategy of RMPC. As the saturating feedback control law can be effectively represented by the convex hull of a group of auxiliary linear feedback laws, the auxiliary feedback laws allow us to design the actual feedback control law without consideration of the input constraints directly to achieve the improved performance. Moreover, we suggest the relative weights on the actual and auxiliary feedback laws to the RMPC, which in turn improves the closed-loop system performance. Furthermore, an off-line design of the proposed RMPC is also developed to make it more practical. Numerical studies demonstrate the effectiveness of the proposed algorithm.

  3. Higher Self-Control Capacity Predicts Lower Anxiety-Impaired Cognition during Math Examinations

    PubMed Central

    Bertrams, Alex; Baumeister, Roy F.; Englert, Chris

    2016-01-01

    We assumed that self-control capacity, self-efficacy, and self-esteem would enable students to keep attentional control during tests. Therefore, we hypothesized that the three personality traits would be negatively related to anxiety-impaired cognition during math examinations. Secondary school students (N = 158) completed measures of self-control capacity, self-efficacy, and self-esteem at the beginning of the school year. Five months later, anxiety-impaired cognition during math examinations was assessed. Higher self-control capacity, but neither self-efficacy nor self-esteem, predicted lower anxiety-impaired cognition 5 months later, over and above baseline anxiety-impaired cognition. Moreover, self-control capacity was indirectly related to math grades via anxiety-impaired cognition. The findings suggest that improving self-control capacity may enable students to deal with anxiety-related problems during school tests. PMID:27065013

  4. The design and optimization for light-algae bioreactor controller based on Artificial Neural Network-Model Predictive Control

    NASA Astrophysics Data System (ADS)

    Hu, Dawei; Liu, Hong; Yang, Chenliang; Hu, Enzhu

    As a subsystem of the bioregenerative life support system (BLSS), light-algae bioreactor (LABR) has properties of high reaction rate, efficiently synthesizing microalgal biomass, absorbing CO2 and releasing O2, so it is significant for BLSS to provide food and maintain gas balance. In order to manipulate the LABR properly, it has been designed as a closed-loop control system, and technology of Artificial Neural Network-Model Predictive Control (ANN-MPC) is applied to design the controller for LABR in which green microalgae, Spirulina platensis is cultivated continuously. The conclusion is drawn by computer simulation that ANN-MPC controller can intelligently learn the complicated dynamic performances of LABR, and automatically, robustly and self-adaptively regulate the light intensity illuminating on the LABR, hence make the growth of microalgae in the LABR be changed in line with the references, meanwhile provide appropriate damping to improve markedly the transient response performance of LABR.

  5. A cerebellar model for predictive motor control tested in a brain-based device.

    PubMed

    McKinstry, Jeffrey L; Edelman, Gerald M; Krichmar, Jeffrey L

    2006-02-28

    The cerebellum is known to be critical for accurate adaptive control and motor learning. We propose here a mechanism by which the cerebellum may replace reflex control with predictive control. This mechanism is embedded in a learning rule (the delayed eligibility trace rule) in which synapses onto a Purkinje cell or onto a cell in the deep cerebellar nuclei become eligible for plasticity only after a fixed delay from the onset of suprathreshold presynaptic activity. To investigate the proposal that the cerebellum is a general-purpose predictive controller guided by a delayed eligibility trace rule, a computer model based on the anatomy and dynamics of the cerebellum was constructed. It contained components simulating cerebellar cortex and deep cerebellar nuclei, and it received input from a middle temporal visual area and the inferior olive. The model was incorporated in a real-world brain-based device (BBD) built on a Segway robotic platform that learned to traverse curved paths. The BBD learned which visual motion cues predicted impending collisions and used this experience to avoid path boundaries. During learning, the BBD adapted its velocity and turning rate to successfully traverse various curved paths. By examining neuronal activity and synaptic changes during this behavior, we found that the cerebellar circuit selectively responded to motion cues in specific receptive fields of simulated middle temporal visual areas. The system described here prompts several hypotheses about the relationship between perception and motor control and may be useful in the development of general-purpose motor learning systems for machines.

  6. Prediction of Regulation Reserve Requirements in California ISO Control Area based on BAAL Standard

    SciTech Connect

    Etingov, Pavel V.; Makarov, Yuri V.; Samaan, Nader A.; Ma, Jian; Loutan, Clyde

    2013-07-21

    This paper presents new methodologies developed at Pacific Northwest National Laboratory (PNNL) to estimate regulation capacity requirements in the California ISO control area. Two approaches have been developed: (1) an approach based on statistical analysis of actual historical area control error (ACE) and regulation data, and (2) an approach based on balancing authority ACE limit control performance standard. The approaches predict regulation reserve requirements on a day-ahead basis including upward and downward requirements, for each operating hour of a day. California ISO data has been used to test the performance of the proposed algorithms. Results show that software tool allows saving up to 30% on the regulation procurements cost .

  7. Improving the feed-forward compensator in predictive control for setpoint tracking.

    PubMed

    Valencia-Palomo, G; Rossiter, J A; López-Estrada, F R

    2014-05-01

    Simple predictive control (MPC) algorithms produce a feed-forward compensator that may be a suboptimal choice. This paper gives some insights into this issue and simple means of modifying the feed-forward to produce a more systematic and optimal design. In particular, it is shown that the optimum procedure depends upon the underlying loop tuning and also that there are, as yet under utilised, potential benefits with regard to constraint handling procedures, which helps to improve the computational efficiency of the online controller implementation. A laboratory test in a programmable logic controller (PLC) was carried out to demonstrate the code on real hardware and the effectiveness of the solution.

  8. Output-Feedback Model Predictive Control of a Pasteurization Pilot Plant based on an LPV model

    NASA Astrophysics Data System (ADS)

    Karimi Pour, Fatemeh; Ocampo-Martinez, Carlos; Puig, Vicenç

    2017-01-01

    This paper presents a model predictive control (MPC) of a pasteurization pilot plant based on an LPV model. Since not all the states are measured, an observer is also designed, which allows implementing an output-feedback MPC scheme. However, the model of the plant is not completely observable when augmented with the disturbance models. In order to solve this problem, the following strategies are used: (i) the whole system is decoupled into two subsystems, (ii) an inner state-feedback controller is implemented into the MPC control scheme. A real-time example based on the pasteurization pilot plant is simulated as a case study for testing the behavior of the approaches.

  9. Modulation of grasping force in prosthetic hands using neural network-based predictive control.

    PubMed

    Pasluosta, Cristian F; Chiu, Alan W L

    2015-01-01

    This chapter describes the implementation of a neural network-based predictive control system for driving a prosthetic hand. Nonlinearities associated with the electromechanical aspects of prosthetic devices present great challenges for precise control of this type of device. Model-based controllers may overcome this issue. Moreover, given the complexity of these kinds of electromechanical systems, neural network-based modeling arises as a good fit for modeling the fingers' dynamics. The results of simulations mimicking potential situations encountered during activities of daily living demonstrate the feasibility of this technique.

  10. Toward Proof of Concept of a One Health Approach to Disease Prediction and Control

    PubMed Central

    Kock, Richard; Kachani, Malika; Kunkel, Rebekah; Thomas, Jason; Gilbert, Jeffrey; Wallace, Robert; Blackmore, Carina; Wong, David; Karesh, William; Natterson, Barbara; Dugas, Raymond; Rubin, Carol

    2013-01-01

    A One Health approach considers the role of changing environments with regard to infectious and chronic disease risks affecting humans and nonhuman animals. Recent disease emergence events have lent support to a One Health approach. In 2010, the Stone Mountain Working Group on One Health Proof of Concept assembled and evaluated the evidence regarding proof of concept of the One Health approach to disease prediction and control. Aspects examined included the feasibility of integrating human, animal, and environmental health and whether such integration could improve disease prediction and control efforts. They found evidence to support each of these concepts but also identified the need for greater incorporation of environmental and ecosystem factors into disease assessments and interventions. The findings of the Working Group argue for larger controlled studies to evaluate the comparative effectiveness of the One Health approach. PMID:24295136

  11. Modeling a multivariable reactor and on-line model predictive control.

    PubMed

    Yu, D W; Yu, D L

    2005-10-01

    A nonlinear first principle model is developed for a laboratory-scaled multivariable chemical reactor rig in this paper and the on-line model predictive control (MPC) is implemented to the rig. The reactor has three variables-temperature, pH, and dissolved oxygen with nonlinear dynamics-and is therefore used as a pilot system for the biochemical industry. A nonlinear discrete-time model is derived for each of the three output variables and their model parameters are estimated from the real data using an adaptive optimization method. The developed model is used in a nonlinear MPC scheme. An accurate multistep-ahead prediction is obtained for MPC, where the extended Kalman filter is used to estimate system unknown states. The on-line control is implemented and a satisfactory tracking performance is achieved. The MPC is compared with three decentralized PID controllers and the advantage of the nonlinear MPC over the PID is clearly shown.

  12. Implementation of model predictive control for resistive wall mode stabilization on EXTRAP T2R

    NASA Astrophysics Data System (ADS)

    Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.

    2015-10-01

    A model predictive control (MPC) method for stabilization of the resistive wall mode (RWM) in the EXTRAP T2R reversed-field pinch is presented. The system identification technique is used to obtain a linearized empirical model of EXTRAP T2R. MPC employs the model for prediction and computes optimal control inputs that satisfy performance criterion. The use of a linearized form of the model allows for compact formulation of MPC, implemented on a millisecond timescale, that can be used for real-time control. The design allows the user to arbitrarily suppress any selected Fourier mode. The experimental results from EXTRAP T2R show that the designed and implemented MPC successfully stabilizes the RWM.

  13. On robustness of constrained non-linear H ∞ predictive controllers with disturbances

    NASA Astrophysics Data System (ADS)

    He, De-Feng; Ji, Hai-Bo; Zheng, Tao

    2010-02-01

    This article considers the robustness problem of H ∞ model predictive controllers for constrained non-linear discrete-time systems subject to disturbances, which are dependent on the system state and input. The notions of input-to-state stability and finite L 2-gain of non-linear systems are introduced and exploited to investigate the robustness properties of this predictive controller under the state and input constraints and the disturbance. Moreover, this robustness of the controller is extended to the case of suboptimality of the solution. With its feasibility at initial time, the feasibility of the online optimisation problem is guaranteed for all times in the presence of disturbances and constraints. Finally, an example is employed to illustrate the proposed results.

  14. Predicting epistasis: an experimental test of metabolic control theory with bacterial transcription and translation.

    PubMed

    MacLean, R C

    2010-03-01

    Epistatic interactions between mutations are thought to play a crucial role in a number of evolutionary processes, including adaptation and sex. Evidence for epistasis is abundant, but tests of general theoretical models that can predict epistasis are lacking. In this study, I test the ability of metabolic control theory to predict epistasis using a novel experimental approach that combines phenotypic and genetic perturbations of enzymes involved in gene expression and protein synthesis in the bacterium Pseudomonas aeruginosa. These experiments provide experimental support for two key predictions of metabolic control theory: (i) epistasis between genes involved in the same pathway is antagonistic; (ii) epistasis becomes increasingly antagonistic as mutational severity increases. Metabolic control theory is a general theory that applies to any set of genes that are involved in the same linear processing chain, not just metabolic pathways, and I argue that this theory is likely to have important implications for predicting epistasis between functionally coupled genes, such as those involved in antibiotic resistance. Finally, this study highlights the fact that phenotypic manipulations of gene activity provide a powerful method for studying epistasis that complements existing genetic methods.

  15. Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR.

    PubMed

    Wu, Hsin-Yun; Gong, Cihun-Siyong Alex; Lin, Shih-Pin; Chang, Kuang-Yi; Tsou, Mei-Yung; Ting, Chien-Kun

    2016-06-01

    Patient-controlled epidural analgesia (PCEA) has been applied to reduce postoperative pain in orthopedic surgical patients. Unfortunately, PCEA is occasionally accompanied by nausea and vomiting. The logistic regression (LR) model is widely used to predict vomiting, and recently support vector machines (SVM), a supervised machine learning method, has been used for classification and prediction. Unlike our previous work which compared Artificial Neural Networks (ANNs) with LR, this study uses a SVM-based predictive model to identify patients with high risk of vomiting during PCEA and comparing results with those derived from the LR-based model. From January to March 2007, data from 195 patients undergoing PCEA following orthopedic surgery were applied to develop two predictive models. 75% of the data were randomly selected for training, while the remainder was used for testing to validate predictive performance. The area under curve (AUC) was measured using the Receiver Operating Characteristic curve (ROC). The area under ROC curves of LR and SVM models were 0.734 and 0.929, respectively. A computer-based predictive model can be used to identify those who are at high risk for vomiting after PCEA, allowing for patient-specific therapeutic intervention or the use of alternative analgesic methods.

  16. Photovoltaic power generation for air-conditioning system based on predictive control

    SciTech Connect

    Kim, S.; Choi, J.; Park, G.; Yoo Jiyoon

    1995-12-31

    In this paper an auxiliary power supply scheme using photovoltaic power generation for air-conditioning system and its novel control strategy are proposed. The proposed auxiliary power supply system employs a boost converter, a bidirectional power converter and photovoltaic arrays. The boost converter controlled by a predictive control strategy provides maximum power track (MPT) state on the photovoltaic (PV) arrays as well as power generation facility function on the ac utility grid. Furthermore the bidirectional power converter controls the power flow balance between the loads and two different power sources according to the condition of the load power and the supplied power from photovoltaic arrays. It is shown that the maximum power tracking of the PV arrays, the unit power factor of ac utility grid and the descent input dc voltage regulation of the air-conditioning system are achieved by the proposed predictive control strategy. The proposed switching strategy for the boost converter and the bidirectional power converter are based on the predictive control with ac line current and output voltage of the PV arrays. The bidirectional power converter is suitably modulation controlled to rectify the ac source during the power shortage under the poor power generation of PV arrays or over load conditions of air conditioner. During the opposite state, the bidirectional power converter is gated to function as a regeneration inverter. Controller design procedure for the proposed approach to achieve near sinusoidal input currents under the inverter mode and the rectifier mode is detailed. Simulation results on a laboratory prototype system are discussed. Experimental results from the laboratory prototype system will be presented in the near future.

  17. Advanced Models and Controls for Prediction and Extension of Battery Lifetime (Presentation)

    SciTech Connect

    Smith, K.; Wood, E.; Santhanagopalan, S.; Kim, G.; Pesaran, A.

    2014-02-01

    Predictive models of capacity and power fade must consider a multiplicity of degradation modes experienced by Li-ion batteries in the automotive environment. Lacking accurate models and tests, lifetime uncertainty must presently be absorbed by overdesign and excess warranty costs. To reduce these costs and extend life, degradation models are under development that predict lifetime more accurately and with less test data. The lifetime models provide engineering feedback for cell, pack and system designs and are being incorporated into real-time control strategies.

  18. Harmonic suppression and delay compensation for inverters via variable horizon nonlinear model predictive control

    NASA Astrophysics Data System (ADS)

    Mirzaeva, G.; Goodwin, G. C.

    2015-07-01

    Inverters play a central role in modern society including renewable energy integration and motor drives. Due to the inherent switched nature of the inverter waveforms harmonic distortion is an issue. Additionally, the switching patterns are perturbed by unavoidable switching delays. Amongst those, nonlinear and load-dependent switching delays (known as inverter 'dead-time delays') are the most difficult to compensate. In this paper, we propose a new approach to delay compensation and harmonic suppression in inverter voltage. The proposed approach is based on variable prediction horizon nonlinear model predictive control.

  19. Childhood Self-Control Predicts Smoking Throughout Life: Evidence From 21,000 Cohort Study Participants

    PubMed Central

    2016-01-01

    Objective: Low self-control has been linked with smoking, yet it remains unclear whether childhood self-control underlies the emergence of lifetime smoking patterns. We examined the contribution of childhood self-control to early smoking initiation and smoking across adulthood. Methods: 21,132 participants were drawn from 2 nationally representative cohort studies; the 1970 British Cohort Study (BCS) and the 1958 National Child Development Study (NCDS). Child self-control was teacher-rated at age 10 in the BCS and at ages 7 and 11 in the NCDS. Participants reported their smoking status and number of cigarettes smoked per day at 5 time-points in the BCS (ages 26–42) and 6 time-points in the NCDS (ages 23–55). Both studies controlled for socioeconomic background, cognitive ability, psychological distress, gender, and parental smoking; the NCDS also controlled for an extended set of background characteristics. Results: Early self-control made a substantial graded contribution to (not) smoking throughout life. In adjusted regression models, a 1-SD increase in self-control predicted a 6.9 percentage point lower probability of smoking in the BCS, and this was replicated in the NCDS (5.2 point reduced risk). Adolescent smoking explained over half of the association between self-control and adult smoking. Childhood self-control was positively related to smoking cessation and negatively related to smoking initiation, relapse to smoking, and the number of cigarettes smoked in adulthood. Conclusions: This study provides strong evidence that low childhood self-control predicts an increased risk of smoking throughout adulthood and points to adolescent smoking as a key pathway through which this may occur. PMID:27607137

  20. Multidimensional prediction of treatment response to antidepressants with cognitive control and functional MRI.

    PubMed

    Crane, Natania A; Jenkins, Lisanne M; Bhaumik, Runa; Dion, Catherine; Gowins, Jennifer R; Mickey, Brian J; Zubieta, Jon-Kar; Langenecker, Scott A

    2017-02-01

    Predicting treatment response for major depressive disorder can provide a tremendous benefit for our overstretched health care system by reducing number of treatments and time to remission, thereby decreasing morbidity. The present study used neural and performance predictors during a cognitive control task to predict treatment response (% change in Hamilton Depression Rating Scale pre- to post-treatment). Forty-nine individuals diagnosed with major depressive disorder were enrolled with intent to treat in the open-label study; 36 completed treatment, had useable data, and were included in most data analyses. Participants included in the data analysis sample received treatment with escitalopram (n = 22) or duloxetine (n = 14) for 10 weeks. Functional MRI and performance during a Parametric Go/No-go test were used to predict per cent reduction in Hamilton Depression Rating Scale scores after treatment. Haemodynamic response function-based contrasts and task-related independent components analysis (subset of sample: n = 29) were predictors. Independent components analysis component beta weights and haemodynamic response function modelling activation during Commission errors in the rostral and dorsal anterior cingulate, mid-cingulate, dorsomedial prefrontal cortex, and lateral orbital frontal cortex predicted treatment response. In addition, more commission errors on the task predicted better treatment response. Together in a regression model, independent component analysis, haemodynamic response function-modelled, and performance measures predicted treatment response with 90% accuracy (compared to 74% accuracy with clinical features alone), with 84% accuracy in 5-fold, leave-one-out cross-validation. Convergence between performance markers and functional magnetic resonance imaging, including novel independent component analysis techniques, achieved high accuracy in prediction of treatment response for major depressive disorder. The strong link to a task paradigm

  1. Networked min-max model predictive control of constrained nonlinear systems with delays and packet dropouts

    NASA Astrophysics Data System (ADS)

    Li, Huiping; Shi, Yang

    2013-04-01

    This article investigates a class of constrained nonlinear networked control systems (NCSs) subject to external disturbances, input and state constraints and network-induced constraints. From a practical perspective, the network-induced constraints considered include the time delays and packet dropouts on both the sensor-to-controller (S-C) channel and the controller-to-actuator (C-A) channel simultaneously. The min-max model predictive control method is proposed to design the control packets by incorporating the external disturbances into the optimisation problem. Moreover, the input-to-state practical stability of the resulting nonlinear NCS is established by constructing a novel Lyapunov function. Finally, the simulation results and the comparison studies are presented to demonstrate the effectiveness and improvement of the proposed method.

  2. Unity power factor converter based on a fuzzy controller and predictive input current.

    PubMed

    Bouafassa, Amar; Rahmani, Lazhar; Kessal, Abdelhalim; Babes, Badreddine

    2014-11-01

    This paper proposes analysis and control of a single-phase power factor corrector (PFC). The proposed control is capable of achieving a unity power factor for each DC link voltage or load fluctuation. The method under study is composed of two intelligent approaches, a fuzzy logic controller to ensure an output voltage at a suitable value and predictive current control. The fuzzy controller is used with minimum rules to attain a low cost. The method is verified and discussed through simulation on the MATLAB/Simulink platform. It presents high dynamic performance under various parameter changes. Moreover, in order to examine and evaluate the method in real-time, a test bench is built using dSPACE 1104. The implantation of the proposed method is very easy and flexible and allows for operation under parameter variations. Additionally, the obtained results are very significant.

  3. Model predictive control of a wet limestone flue gas desulfurization pilot plant

    SciTech Connect

    Perales, A.L.V.; Ollero, P.; Ortiz, F.J.G.; Gomez-Barea, A.

    2009-06-15

    A model predictive control (MPC) strategy based on a dynamic matrix (DMC) is designed and applied to a wet limestone flue gas desulfurization (WLFGD) pilot plant to evaluate what enhancement in control performance can be achieved with respect to a conventional decentralized feedback control strategy. The results reveal that MPC can significantly improve both reference tracking and disturbance rejection. For disturbance rejection, the main control objective in WLFGD plants, selection of tuning parameters and sample time, is of paramount importance due to the fast effect of the main disturbance (inlet SO{sub 2} load to the absorber) on the most important controlled variable (outlet flue gas SO{sub 2} concentration). The proposed MPC strategy can be easily applied to full-scale WLFGD plants.

  4. Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control

    PubMed Central

    Rueckert, Elmar; Čamernik, Jernej; Peters, Jan; Babič, Jan

    2016-01-01

    Human motor skill learning is driven by the necessity to adapt to new situations. While supportive contacts are essential for many tasks, little is known about their impact on motor learning. To study the effect of contacts an innovative full-body experimental paradigm was established. The task of the subjects was to reach for a distant target while postural stability could only be maintained by establishing an additional supportive hand contact. To examine adaptation, non-trivial postural perturbations of the subjects’ support base were systematically introduced. A novel probabilistic trajectory model approach was employed to analyze the correlation between the motions of both arms and the trunk. We found that subjects adapted to the perturbations by establishing target dependent hand contacts. Moreover, we found that the trunk motion adapted significantly faster than the motion of the arms. However, the most striking finding was that observations of the initial phase of the left arm or trunk motion (100–400 ms) were sufficient to faithfully predict the complete movement of the right arm. Overall, our results suggest that the goal-directed arm movements determine the supportive arm motions and that the motion of heavy body parts adapts faster than the light arms. PMID:27328750

  5. Modeling and Control Systems Design by Model Predictive Control for Air-path System of Diesel Engine

    NASA Astrophysics Data System (ADS)

    Iwadare, Mitsuhiro; Ueno, Masaki; Hattori, Yasuharu; Adachi, Shuichi

    Research has been conducted on a variety of combustion technologies in order to reduce diesel engine emissions. These technologies should precisely control the state of in-cylinder gas (EGR mass flow, air mass flow, and so on). However, because the controlled object is a multi-input, multi-output (MIMO) system and a coupled system, the use of control systems based on the conventional methods that employ PID controllers represents a challenge. Model predictive control (MPC) is well known as an MIMO algorithm. An intake control system that could be applied to the intake system of a diesel engine was constructed by supplementing MPC with a feedback function using a disturbance observer and compensator for the nonlinear characteristic of the actuators. Performance tests using an actual vehicle verified that, when applied to a two-input (throttle valve and EGR valve), two-output (air mass flow and intake chamber pressure) system, the proposed MPC is able to rapidly control each output independently to the target value.

  6. Modeling and control of tubular solid-oxide fuel cell systems: II. Nonlinear model reduction and model predictive control

    NASA Astrophysics Data System (ADS)

    Sanandaji, Borhan M.; Vincent, Tyrone L.; Colclasure, Andrew M.; Kee, Robert J.

    This paper describes a systematic method for developing model-based controllers for solid-oxide fuel cell (SOFC) systems. To enhance the system efficiency and to avoid possible damages, the system must be controlled within specific operating conditions, while satisfying a load requirement. Model predictive control (MPC) is a natural choice for control implementation. However, to implement MPC, a low-order model is needed that captures the dominant dynamic behavior over the operating range. A linear parameter varying (LPV) model structure is developed and applied to obtain a control-oriented dynamic model of the SOFC stack. This approach effectively reduces a detailed physical model to a form that is compatible with MPC. The LPV structure includes nonlinear scheduling functions that blend the dynamics of locally linear models to represent nonlinear dynamic behavior over large operating ranges. Alternative scheduling variables are evaluated, with cell current being shown to be an appropriate choice. Using the reduced-order model, an MPC controller is designed that can respond to the load requirement over a wide range of operation changes while maintaining input-output variables within specified constraints. To validate the approach, the LPV-based MPC controller is applied to the high-order physical model.

  7. Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory

    SciTech Connect

    Gregor P. Henze; Moncef Krarti

    2003-12-17

    Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building's massive structure or the use of active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this project investigates the merits of harnessing both storage media concurrently in the context of predictive optimal control. This topical report describes the demonstration of the model-based predictive optimal control for active and passive building thermal storage inventory in a test facility in real-time using time-of-use differentiated electricity prices without demand charges. The laboratory testing findings presented in this topical report cover the second of three project phases. The novel supervisory controller successfully executed a three-step procedure consisting of (1) short-term weather prediction, (2) optimization of control strategy over the next planning horizon using a calibrated building model, and (3) post-processing of the optimal strategy to yield a control command for the current time step that can be executed in the test facility. The primary and secondary building mechanical systems were effectively orchestrated by the model-based predictive optimal controller in real-time while observing comfort and operational constraints. The findings reveal that when the optimal controller is given imperfect weather fore-casts and when the building model used for planning control strategies does not match the actual building perfectly, measured utility costs savings relative to conventional building operation can be substantial. This requires that the facility under control lends itself to passive storage utilization and the building model

  8. Evidence for the flexible sensorimotor strategies predicted by optimal feedback control.

    PubMed

    Liu, Dan; Todorov, Emanuel

    2007-08-29

    Everyday movements pursue diverse and often conflicting mixtures of task goals, requiring sensorimotor strategies customized for the task at hand. Such customization is mostly ignored by traditional theories emphasizing movement geometry and servo control. In contrast, the relationship between the task and the strategy most suitable for accomplishing it lies at the core of our optimal feedback control theory of coordination. Here, we show that the predicted sensitivity to task goals affords natural explanations to a number of novel psychophysical findings. Our point of departure is the little-known fact that corrections for target perturbations introduced late in a reaching movement are incomplete. We show that this is not simply attributable to lack of time, in contradiction with alternative models and, somewhat paradoxically, in agreement with our model. Analysis of optimal feedback gains reveals that the effect is partly attributable to a previously unknown trade-off between stability and accuracy. This yields a testable prediction: if stability requirements are decreased, then accuracy should increase. We confirm the prediction experimentally in three-dimensional obstacle avoidance and interception tasks in which subjects hit a robotic target with programmable impedance. In additional agreement with the theory, we find that subjects do not rely on rigid control strategies but instead exploit every opportunity for increased performance. The modeling methodology needed to capture this extra flexibility is more general than the linear-quadratic methods we used previously. The results suggest that the remarkable flexibility of motor behavior arises from sensorimotor control laws optimized for composite cost functions.

  9. A Numerical Process Control Method for Circular-Tube Hydroforming Prediction

    SciTech Connect

    Johnson, Kenneth I.; Nguyen, Ba Nghiep; Davies, Richard W.; Grant, Glenn J.; Khaleel, Mohammad A.

    2004-03-01

    This paper describes the development of a solution control method that tracks the stresses, strains and mechanical behavior of a tube during hydroforming to estimate the proper axial feed (end-feed) and internal pressure loads through time. The analysis uses the deformation theory of plasticity and Hill?s criterion to describe the plastic flow. Before yielding, the pressure and end-feed increments are estimated based on the initial tube geometry, elastic properties and yield stress. After yielding, the pressure increment is calculated based on the tube geometry at the previous solution increment and the current hoop stress increment. The end-feed increment is computed from the increment of the axial plastic strain. Limiting conditions such as column buckling (of long tubes), local axi-symmetric wrinkling of shorter tubes, and bursting due to localized wall thinning are considered. The process control method has been implemented in the Marc finite element code. Hydroforming simulations using this process control method were conducted to predict the load histories for controlled expansion of 6061-T4 aluminum tubes within a conical die shape and under free hydroforming conditions. The predicted loading paths were transferred to the hydroforming equipment to form the conical and free-formed tube shapes. The model predictions and experimental results are compared for deformed shape, strains and the extent of forming at rupture.

  10. Model-based planning and real-time predictive control for laser-induced thermal therapy

    PubMed Central

    Feng, Yusheng; Fuentes, David

    2014-01-01

    In this article, the major idea and mathematical aspects of model-based planning and real-time predictive control for laser-induced thermal therapy (LITT) are presented. In particular, a computational framework and its major components developed by authors in recent years are reviewed. The framework provides the backbone for not only treatment planning but also real-time surgical monitoring and control with a focus on MR thermometry enabled predictive control and applications to image-guided LITT, or MRgLITT. Although this computational framework is designed for LITT in treating prostate cancer, it is further applicable to other thermal therapies in focal lesions induced by radio-frequency (RF), microwave and high-intensity-focused ultrasound (HIFU). Moreover, the model-based dynamic closed-loop predictive control algorithms in the framework, facilitated by the coupling of mathematical modelling and computer simulation with real-time imaging feedback, has great potential to enable a novel methodology in thermal medicine. Such technology could dramatically increase treatment efficacy and reduce morbidity. PMID:22098360

  11. Variability in phenylalanine control predicts IQ and executive abilities in children with phenylketonuria.

    PubMed

    Hood, Anna; Grange, Dorothy K; Christ, Shawn E; Steiner, Robert; White, Desirée A

    2014-04-01

    A number of studies have revealed significant relationships between cognitive performance and average phenylalanine (Phe) levels in children with phenylketonuria (PKU), but only a few studies have been conducted to examine the relationships between cognitive performance and variability (fluctuations) in Phe levels. In the current study, we examined a variety of indices of Phe control to determine which index best predicted IQ and executive abilities in 47 school-age children with early- and continuously-treated PKU. Indices of Phe control were mean Phe, the index of dietary control, change in Phe with age, and several indices of variability in Phe (standard deviation, standard error of estimate, and percentage of spikes). These indices were computed over the lifetime and during 3 developmental epochs (<5, 5.0-9.9, and ≥10 years of age). Results indicated that variability in Phe was generally a stronger predictor of cognitive performance than other indices of Phe control. In addition, executive performance was better predicted by variability in Phe during older than younger developmental epochs. These results indicate that variability in Phe should be carefully controlled to maximize cognitive outcomes and that Phe control should not be liberalized as children with PKU age.

  12. Event-driven model predictive control of sewage pumping stations for sulfide mitigation in sewer networks.

    PubMed

    Liu, Yiqi; Ganigué, Ramon; Sharma, Keshab; Yuan, Zhiguo

    2016-07-01

    Chemicals such as Mg(OH)2 and iron salts are widely dosed to sewage for mitigating sulfide-induced corrosion and odour problems in sewer networks. The chemical dosing rate is usually not automatically controlled but profiled based on experience of operators, often resulting in over- or under-dosing. Even though on-line control algorithms for chemical dosing in single pipes have been developed recently, network-wide control algorithms are currently not available. The key challenge is that a sewer network is typically wide-spread comprising many interconnected sewer pipes and pumping stations, making network-wide sulfide mitigation with a relatively limited number of dosing points challenging. In this paper, we propose and demonstrate an Event-driven Model Predictive Control (EMPC) methodology, which controls the flows of sewage streams containing the dosed chemical to ensure desirable distribution of the dosed chemical throughout the pipe sections of interests. First of all, a network-state model is proposed to predict the chemical concentration in a network. An EMPC algorithm is then designed to coordinate sewage pumping station operations to ensure desirable chemical distribution in the network. The performance of the proposed control methodology is demonstrated by applying the designed algorithm to a real sewer network simulated with the well-established SeweX model using real sewage flow and characteristics data. The EMPC strategy significantly improved the sulfide mitigation performance with the same chemical consumption, compared to the current practice.

  13. Using micro saint to predict performance in a nuclear power plant control room

    SciTech Connect

    Lawless, M.T.; Laughery, K.R.; Persenky, J.J.

    1995-09-01

    The United States Nuclear Regulatory Commission (NRC) requires a technical basis for regulatory actions. In the area of human factors, one possible technical basis is human performance modeling technology including task network modeling. This study assessed the feasibility and validity of task network modeling to predict the performance of control room crews. Task network models were built that matched the experimental conditions of a study on computerized procedures that was conducted at North Carolina State University. The data from the {open_quotes}paper procedures{close_quotes} conditions were used to calibrate the task network models. Then, the models were manipulated to reflect expected changes when computerized procedures were used. These models` predictions were then compared to the experimental data from the {open_quotes}computerized conditions{close_quotes} of the North Carolina State University study. Analyses indicated that the models predicted some subsets of the data well, but not all. Implications for the use of task network modeling are discussed.

  14. Academic performance and social competence of adolescents: predictions based on effortful control and empathy.

    PubMed

    Zorza, Juan P; Marino, Julián; de Lemus, Soledad; Acosta Mesas, Alberto

    2013-01-01

    This study explored the predictive power of effortful control (EC) on empathy, academic performance, and social competence in adolescents. We obtained self-report measures of EC and dispositional empathy in 359 students (197 girls and 162 boys) aged between 12 and 14 years. Each student provided information about the prosocial behavior of the rest of his/her classmates and completed a sociogram. At the end of the school year, we calculated the mean grade of each student and the teacher responsible for each class completed a questionnaire on the academic skills of his/her students. The study confirmed the existence of a structural equation model (SEM) in which EC directly predicted academic performance and social competence. Additionally, empathic concern partially mediated the effect of EC on social competence. Finally, social competence significantly predicted academic performance. The article discusses the practical applications of the model proposed.

  15. Children's construction task performance and spatial ability: controlling task complexity and predicting mathematics performance.

    PubMed

    Richardson, Miles; Hunt, Thomas E; Richardson, Cassandra

    2014-12-01

    This paper presents a methodology to control construction task complexity and examined the relationships between construction performance and spatial and mathematical abilities in children. The study included three groups of children (N = 96); ages 7-8, 10-11, and 13-14 years. Each group constructed seven pre-specified objects. The study replicated and extended previous findings that indicated that the extent of component symmetry and variety, and the number of components for each object and available for selection, significantly predicted construction task difficulty. Results showed that this methodology is a valid and reliable technique for assessing and predicting construction play task difficulty. Furthermore, construction play performance predicted mathematical attainment independently of spatial ability.

  16. Lithium-ion battery cell-level control using constrained model predictive control and equivalent circuit models

    SciTech Connect

    Xavier, MA; Trimboli, MS

    2015-07-01

    This paper introduces a novel application of model predictive control (MPC) to cell-level charging of a lithium-ion battery utilizing an equivalent circuit model of battery dynamics. The approach employs a modified form of the MPC algorithm that caters for direct feed-though signals in order to model near-instantaneous battery ohmic resistance. The implementation utilizes a 2nd-order equivalent circuit discrete-time state-space model based on actual cell parameters; the control methodology is used to compute a fast charging profile that respects input, output, and state constraints. Results show that MPC is well-suited to the dynamics of the battery control problem and further suggest significant performance improvements might be achieved by extending the result to electrochemical models. (C) 2015 Elsevier B.V. All rights reserved.

  17. Within-socket myoelectric prediction of continuous ankle kinematics for control of a powered transtibial prosthesis

    NASA Astrophysics Data System (ADS)

    Farmer, Samuel; Silver-Thorn, Barbara; Voglewede, Philip; Beardsley, Scott A.

    2014-10-01

    Objective. Powered robotic prostheses create a need for natural-feeling user interfaces and robust control schemes. Here, we examined the ability of a nonlinear autoregressive model to continuously map the kinematics of a transtibial prosthesis and electromyographic (EMG) activity recorded within socket to the future estimates of the prosthetic ankle angle in three transtibial amputees. Approach. Model performance was examined across subjects during level treadmill ambulation as a function of the size of the EMG sampling window and the temporal ‘prediction’ interval between the EMG/kinematic input and the model’s estimate of future ankle angle to characterize the trade-off between model error, sampling window and prediction interval. Main results. Across subjects, deviations in the estimated ankle angle from the actual movement were robust to variations in the EMG sampling window and increased systematically with prediction interval. For prediction intervals up to 150 ms, the average error in the model estimate of ankle angle across the gait cycle was less than 6°. EMG contributions to the model prediction varied across subjects but were consistently localized to the transitions to/from single to double limb support and captured variations from the typical ankle kinematics during level walking. Significance. The use of an autoregressive modeling approach to continuously predict joint kinematics using natural residual muscle activity provides opportunities for direct (transparent) control of a prosthetic joint by the user. The model’s predictive capability could prove particularly useful for overcoming delays in signal processing and actuation of the prosthesis, providing a more biomimetic ankle response.

  18. Predicting avian distributions to evaluate spatiotemporal overlap with locust control operations in eastern Australia.

    PubMed

    Szabo, Judit K; Davy, Pamela J; Hooper, Michael J; Astheimer, Lee B

    2009-12-01

    Locusts and grasshoppers cause considerable economic damage to agriculture worldwide. The Australian Plague Locust Commission uses multiple pesticides to control locusts in eastern Australia. Avian exposure to agricultural pesticides is of conservation concern, especially in the case of rare and threatened species. The aim of this study was to evaluate the probability of pesticide exposure of native avian species during operational locust control based on knowledge of species occurrence in areas and times of application. Using presence-absence data provided by the Birds Australia Atlas for 1998 to 2002, we developed a series of generalized linear models to predict avian occurrences on a monthly basis in 0.5 degrees grid cells for 280 species over 2 million km2 in eastern Australia. We constructed species-specific models relating occupancy patterns to survey date and location, rainfall, and derived habitat preference. Model complexity depended on the number of observations available. Model output was the probability of occurrence for each species at times and locations of past locust control operations within the 5-year study period. Given the high spatiotemporal variability of locust control events, the variability in predicted bird species presence was high, with 108 of the total 280 species being included at least once in the top 20 predicted species for individual space-time events. The models were evaluated using field surveys collected between 2000 and 2005, at sites with and without locust outbreaks. Model strength varied among species. Some species were under- or over-predicted as times and locations of interest typically did not correspond to those in the prediction data set and certain species were likely attracted to locusts as a food source. Field surveys demonstrated the utility of the spatially explicit species lists derived from the models but also identified the presence of a number of previously unanticipated species. These results also emphasize

  19. A predictive biophysical model of translational coupling to coordinate and control protein expression in bacterial operons

    PubMed Central

    Tian, Tian; Salis, Howard M.

    2015-01-01

    Natural and engineered genetic systems require the coordinated expression of proteins. In bacteria, translational coupling provides a genetically encoded mechanism to control expression level ratios within multi-cistronic operons. We have developed a sequence-to-function biophysical model of translational coupling to predict expression level ratios in natural operons and to design synthetic operons with desired expression level ratios. To quantitatively measure ribosome re-initiation rates, we designed and characterized 22 bi-cistronic operon variants with systematically modified intergenic distances and upstream translation rates. We then derived a thermodynamic free energy model to calculate de novo initiation rates as a result of ribosome-assisted unfolding of intergenic RNA structures. The complete biophysical model has only five free parameters, but was able to accurately predict downstream translation rates for 120 synthetic bi-cistronic and tri-cistronic operons with rationally designed intergenic regions and systematically increased upstream translation rates. The biophysical model also accurately predicted the translation rates of the nine protein atp operon, compared to ribosome profiling measurements. Altogether, the biophysical model quantitatively predicts how translational coupling controls protein expression levels in synthetic and natural bacterial operons, providing a deeper understanding of an important post-transcriptional regulatory mechanism and offering the ability to rationally engineer operons with desired behaviors. PMID:26117546

  20. Control theory prediction of resolved Cheyne-Stokes respiration in heart failure.

    PubMed

    Sands, Scott A; Edwards, Bradley A; Kee, Kirk; Stuart-Andrews, Christopher; Skuza, Elizabeth M; Roebuck, Teanau; Turton, Anthony; Hamilton, Garun S; Naughton, Matthew T; Berger, Philip J

    2016-11-01

    Cheyne-Stokes respiration (CSR) foretells deleterious outcomes in patients with heart failure. Currently, the size of therapeutic intervention is not guided by the patient's underlying pathophysiology. In theory, the intervention needed to resolve CSR, as a control system instability (loop gain >1), can be predicted knowing the baseline loop gain and how much it falls with therapy.In 12 patients with heart failure, we administered an inspiratory carbon dioxide fraction of 1-3% during CSR (n=95 interventions) as a means to reduce loop gain. We estimated the loop gain on therapy (LGtherapy), using the baseline loop gain (using hyperpnoea length/cycle length) and its expected reduction (18% per 1% inspired carbon dioxide), and tested the specific hypothesis that LGtherapy predicts CSR persistence (LGtherapy >1) versus resolution (LGtherapy <1).As predicted, when LGtherapy >1.0, CSR continued during therapy in 23 out of 25 (92%) trials. A borderline loop gain zone (0.8control theory provides predictive insight into CSR resolution in heart failure. Thus, we now have a means to calculate the size of interventions needed to ameliorate CSR on a patient-by-patient basis.

  1. Observer-based predictive controller design with network-enhanced time-delay compensation

    NASA Astrophysics Data System (ADS)

    Florin Caruntu, Constantin

    2015-02-01

    State feedback control is very attractive due to the precise computation of the gain matrix, but the implementation of a state feedback controller is possible only when all state variables are directly measurable. This condition is almost impossible to accomplish due to the excess number of required sensors or unavailability of states for measurement in most of the practical situations. Hence, the need for an estimator or observer is obvious to estimate all the state variables by observing the input and the output of the controlled system. As such, the goal of this paper is to provide a control design methodology based on a Luenberger observer design that can assure the closed-loop performances of a vehicle drivetrain with backlash, while compensating the network-enhanced time-varying delays. This goal is achieved in a sequential manner: firstly, a piecewise linear model of two inertias drivetrain, which takes into consideration the backlash nonlinearity and the network-enhanced time-varying delay effects is derived; then, a Luenberger observer which estimates the state variables is synthesized and the robust full state-feedback predictive controller based on flexible control Lyapunov functions is designed to explicitly take into account the bounds of the disturbances caused by time-varying delays and to guarantee also the input-to-state stability of the system in a non-conservative way. The full state-feedback predictive control strategy based on the Luenberger observer design was experimentally tested on a vehicle drivetrain emulator controlled through controller area network, with the aim of minimizing the backlash effects while compensating the network-enhanced delays.

  2. Automatic weight determination in nonlinear model predictive control of wind turbines using swarm optimization technique

    NASA Astrophysics Data System (ADS)

    Tofighi, Elham; Mahdizadeh, Amin

    2016-09-01

    This paper addresses the problem of automatic tuning of weighting coefficients for the nonlinear model predictive control (NMPC) of wind turbines. The choice of weighting coefficients in NMPC is critical due to their explicit impact on efficiency of the wind turbine control. Classically, these weights are selected based on intuitive understanding of the system dynamics and control objectives. The empirical methods, however, may not yield optimal solutions especially when the number of parameters to be tuned and the nonlinearity of the system increase. In this paper, the problem of determining weighting coefficients for the cost function of the NMPC controller is formulated as a two-level optimization process in which the upper- level PSO-based optimization computes the weighting coefficients for the lower-level NMPC controller which generates control signals for the wind turbine. The proposed method is implemented to tune the weighting coefficients of a NMPC controller which drives the NREL 5-MW wind turbine. The results are compared with similar simulations for a manually tuned NMPC controller. Comparison verify the improved performance of the controller for weights computed with the PSO-based technique.

  3. Prediction of Traffic Complexity and Controller Workload in Mixed Equipage NextGen Environments

    NASA Technical Reports Server (NTRS)

    Lee, Paul U.; Prevot, Thomas

    2012-01-01

    Controller workload is a key factor in limiting en route air traffic capacity. Past efforts to quantify and predict workload have resulted in identifying objective metrics that correlate well with subjective workload ratings during current air traffic control operations. Although these metrics provide a reasonable statistical fit to existing data, they do not provide a good mechanism for estimating controller workload for future air traffic concepts and environments that make different assumptions about automation, enabling technologies, and controller tasks. One such future environment is characterized by en route airspace with a mixture of aircraft equipped with and without Data Communications (Data Comm). In this environment, aircraft with Data Comm will impact controller workload less than aircraft requiring voice communication, altering the close correlation between aircraft count and controller workload that exists in current air traffic operations. This paper outlines a new trajectory-based complexity (TBX) calculation that was presented to controllers during a human-in-the-loop simulation. The results showed that TBX accurately estimated the workload in a mixed Data Comm equipage environment and the resulting complexity values were understood and readily interpreted by the controllers. The complexity was represented as a "modified aircraft account" that weighted different complexity factors and summed them in such a way that the controllers could effectively treat them as aircraft count. The factors were also relatively easy to tune without an extensive data set. The results showed that the TBX approach is well suited for presenting traffic complexity in future air traffic environments.

  4. Stock management in hospital pharmacy using chance-constrained model predictive control.

    PubMed

    Jurado, I; Maestre, J M; Velarde, P; Ocampo-Martinez, C; Fernández, I; Tejera, B Isla; Prado, J R Del

    2016-05-01

    One of the most important problems in the pharmacy department of a hospital is stock management. The clinical need for drugs must be satisfied with limited work labor while minimizing the use of economic resources. The complexity of the problem resides in the random nature of the drug demand and the multiple constraints that must be taken into account in every decision. In this article, chance-constrained model predictive control is proposed to deal with this problem. The flexibility of model predictive control allows taking into account explicitly the different objectives and constraints involved in the problem while the use of chance constraints provides a trade-off between conservativeness and efficiency. The solution proposed is assessed to study its implementation in two Spanish hospitals.

  5. Fast prediction of transient stability margin in systems with SVC control and HVDC link

    SciTech Connect

    Tso, S.K.; Cheung, S.P.

    1995-12-31

    Recent developments in transient stability margin (TSM) prediction using the energy-based direct method have included excitation controllers, power system stabilizers (PSSs) and/or static VAr compensators (SVCs). These devices can be represented in their detailed dynamic models to desired degrees of complexity while the proposed extended equal-area criterion can still be effectively applied. This paper describes further development of this technique to incorporate an HVDC transmission into the test network for TSM prediction. The method is examined with a practical 17-machine power network representing the South China/Hong Kong system. An SVC control scheme is also installed in a weak bus of the test network for transient stability improvement. The results obtained show that there is no sacrifice in accuracy, speed or reliability of the TSM method with SVC and HVDC realistically incorporated into the study.

  6. Design and analysis of a model predictive controller for active queue management.

    PubMed

    Wang, Ping; Chen, Hong; Yang, Xiaoping; Ma, Yan

    2012-01-01

    Model predictive (MP) control as a novel active queue management (AQM) algorithm in dynamic computer networks is proposed. According to the predicted future queue length in the data buffer, early packets at the router are dropped reasonably by the MPAQM controller so that the queue length reaches the desired value with minimal tracking error. The drop probability is obtained by optimizing the network performance. Further, randomized algorithms are applied to analyze the robustness of MPAQM successfully, and also to provide the stability domain of systems with uncertain network parameters. The performances of MPAQM are evaluated through a series of simulations in NS2. The simulation results show that the MPAQM algorithm outperforms RED, PI, and REM algorithms in terms of stability, disturbance rejection, and robustness.

  7. Indoor environmental quality (IEQ) and building energy optimization through model predictive control (MPC)

    NASA Astrophysics Data System (ADS)

    Woldekidan, Korbaga

    This dissertation aims at developing a novel and systematic approach to apply Model Predictive Control (MPC) to improve energy efficiency and indoor environmental quality in office buildings. Model predictive control is one of the advanced optimal control approaches that use models to predict the behavior of the process beyond the current time to optimize the system operation at the present time. In building system, MPC helps to exploit buildings' thermal storage capacity and to use the information on future disturbances like weather and internal heat gains to estimate optimal control inputs ahead of time. In this research the major challenges of applying MPC to building systems are addressed. A systematic framework has been developed for ease of implementation. New methods are proposed to develop simple and yet reasonably accurate models that can minimize the MPC development effort as well as computational time. The developed MPC is used to control a detailed building model represented by whole building performance simulation tool, EnergyPlus. A co-simulation strategy is used to communicate the MPC control developed in Matlab platform with the case building model in EnergyPlus. The co-simulation tool used (MLE+) also has the ability to talk to actual building management systems that support the BACnet communication protocol which makes it easy to implement the developed MPC control in actual buildings. A building that features an integrated lighting and window control and HVAC system with a dedicated outdoor air system and ceiling radiant panels was used as a case building. Though this study is specifically focused on the case building, the framework developed can be applied to any building type. The performance of the developed MPC was compared against a baseline control strategy using Proportional Integral and Derivative (PID) control. Various conventional and advanced thermal comfort as well as ventilation strategies were considered for the comparison. These

  8. Exploring the Neurocircuitry Underpinning Predictability of Threat in Soldiers with PTSD Compared to Deployment Exposed Controls

    PubMed Central

    Dretsch, Michael N.; Wood, Kimberly H.; Daniel, Thomas A.; Katz, Jeffrey S.; Deshpande, Gopikrishna; Goodman, Adam M.; Wheelock, Muriah D.; Wood, Kayli B.; Denney Jr., Thomas S.; Traynham, Stephanie; Knight, David C.

    2016-01-01

    Background: Prior work examining emotional dysregulation observed in posttraumatic stress disorder (PTSD) has primarily been limited to fear-learning processes specific to anticipation, habituation, and extinction of threat. In contrast, the response to threat itself has not been systematically evaluated. Objective: To explore potential disruption in fear conditioning neurocircuitry in service members with PTSD, specifically in response to predictable versus unpredictable threats. Method: In the current study, active-duty U.S. Army soldiers with (PTSD group; n = 38) and without PTSD (deployment-exposed controls; DEC; n = 40), participated in a fear-conditioning study in which threat predictability was manipulated by presenting an aversive unconditioned stimulus (UCS) that was either preceded by a conditioned stimulus (i.e., predictable) or UCS alone (i.e., unpredictable). Threat expectation, skin conductance response (SCR), and functional magnetic resonance imaging (fMRI) signal to predictable and unpredictable threats (i.e., UCS) were assessed. Results: Both groups showed greater threat expectancy and diminished threat-elicited SCRs to predictable compared to unpredictable threat. Significant group differences were observed within the amygdala, hippocampus, insula, and superior and middle temporal gyri. Contrary to our predictions, the PTSD group showed a diminished threat-related response within each of these brain regions during predictable compared to unpredictable threat, whereas the DEC group showed increased activation. Conclusion: Although, the PTSD group showed greater threat-related diminution, hypersensitivity to unpredictable threat cannot be ruled out. Furthermore, pre-trauma, trait-like factors may have contributed to group differences in activation of the neurocircuitry underpinning fear conditioning. PMID:27867434

  9. Time-Preference Tests Fail to Predict Behavior Related to Self-control

    PubMed Central

    Arfer, Kodi B.; Luhmann, Christian C.

    2017-01-01

    According to theory, choices relating to patience and self-control in domains as varied as drug use and retirement saving are driven by generalized preferences about delayed rewards. Past research has shown that measurements of these time preferences are associated with these choices. Research has also attempted to examine how well such measurements can predict choices, but only with inappropriate analytical methods. Moreover, it is not clear which of the many kinds of time-preference tests that have been proposed are most useful for prediction, and a theoretically important aspect of time preferences, nonstationarity, has been neglected in measurement. In Study 1, we examined three approaches to measuring time preferences with 181 users of Mechanical Turk. Retest reliability, for both immediate and 1-month intervals, was decent, as was convergent validity between tests, and association was similar to previous results, but predictive accuracy for 10 criterion variables (e.g., tobacco use) was approximately nil. In Study 2, we examined one other approach to measuring time preferences, and 40 criterion variables, using 7,127 participants in the National Longitudinal Survey of Youth 1979. Time preferences were significantly related to criterion variables, but predictive accuracy was again poor. Our findings imply serious problems for using time-preference tests to predict real-world decisions. The results of Study 1 further suggest there is little value in measuring nonstationarity separately from patience. PMID:28232810

  10. Prediction of forces and moments for flight vehicle control effectors: Workplan

    NASA Technical Reports Server (NTRS)

    Maughmer, Mark D.

    1989-01-01

    Two research activities directed at hypersonic vehicle configurations are currently underway. The first involves the validation of a number of classical local surface inclination methods commonly employed in preliminary design studies of hypersonic flight vehicles. Unlike studies aimed at validating such methods for predicting overall vehicle aerodynamics, this effort emphasizes validating the prediction of forces and moments for flight control studies. Specifically, several vehicle configurations for which experimental or flight-test data are available are being examined. By comparing the theoretical predictions with these data, the strengths and weaknesses of the local surface inclination methods can be ascertained and possible improvements suggested. The second research effort, of significance to control during take-off and landing of most proposed hypersonic vehicle configurations, is aimed at determining the change due to ground effect in control effectiveness of highly swept delta planforms. Central to this research is the development of a vortex-lattice computer program which incorporates an unforced trailing vortex sheet and an image ground plane. With this program, the change in pitching moment of the basic vehicle due to ground proximity, and whether or not there is sufficient control power available to trim, can be determined. In addition to the current work, two different research directions are suggested for future study. The first is aimed at developing an interactive computer program to assist the flight controls engineer in determining the forces and moments generated by different types of control effectors that might be used on hypersonic vehicles. The first phase of this work would deal in the subsonic portion of the flight envelope, while later efforts would explore the supersonic/hypersonic flight regimes. The second proposed research direction would explore methods for determining the aerodynamic trim drag of a generic hypersonic flight vehicle

  11. Advanced Control Filtering and Prediction for Phased Arrays in Directed Energy Systems

    DTIC Science & Technology

    2014-07-31

    SIMULINK model for prediction and feedback control of a phase ramp. Mirror represented by integrator with sample time tsim. The model shown has a...and simulating the closed-loop system in SIMULINK . Approved for public release; distribution unlimited 3 4.0 RESULTS AND DISCUSSION 4.1...although this measurement probably is not necessary. 4.2 Simulation Model There are three differences between the current SIMULINK model and the

  12. Self-statements, locus of control, and depression in predicting self-esteem.

    PubMed

    Philpot, V D; Holliman, W B; Madonna, S

    1995-06-01

    The contributions of frequency of positive and negative self-statements and their ratio, locus of control, and depression in prediction of self-esteem were examined. Volunteers were 145 college students (100 women and 45 men) who were administered the Coopersmith Self-esteem Inventory-Adult Form, Automatic Thought Questionnaire-Revised, the Beck Depression Inventory, and the Rotter Internal-External Locus of Control Scale. Intercorrelations suggested significant relationships among variables. The magnitude of the relationship was strongest between the frequency of negative self-statements and self-esteem. These results are consistent with and lend further support to prior studies of Kendall, et al. and Schwartz and Michaelson.

  13. Controlling bimetallic nanostructures by the microemulsion method with subnanometer resolution using a prediction model

    SciTech Connect

    Buceta, David; Tojo, Concha; Vukmirovic, Miomir B.; Deepak, F. Leonard; Lopez-Quintela, M. Arturo

    2015-06-02

    In this study, we present a theoretical model to predict the atomic structure of Au/Pt nanoparticles synthesized in microemulsions. Excellent concordance with the experimental results shows that the structure of the nanoparticles can be controlled at sub-nanometer resolution simply by changing the reactants concentration. The results of this study not only offer a better understanding of the complex mechanisms governing reactions in microemulsions, but open up a simple new way to synthesize bimetallic nanoparticles with ad-hoc controlled nanostructures.

  14. Antenna pointing system for satellite tracking based on Kalman filtering and model predictive control techniques

    NASA Astrophysics Data System (ADS)

    Souza, André L. G.; Ishihara, João Y.; Ferreira, Henrique C.; Borges, Renato A.; Borges, Geovany A.

    2016-12-01

    The present work proposes a new approach for an antenna pointing system for satellite tracking. Such a system uses the received signal to estimate the beam pointing deviation and then adjusts the antenna pointing. The present work has two contributions. First, the estimation is performed by a Kalman filter based conical scan technique. This technique uses the Kalman filter avoiding the batch estimator and applies a mathematical manipulation avoiding the linearization approximations. Secondly, a control technique based on the model predictive control together with an explicit state feedback solution are obtained in order to reduce the computational burden. Numerical examples illustrate the results.

  15. Model Predictive Control for Terminal Area Energy Management and Approach and Landing for a Reusable Launch Vehicle

    DTIC Science & Technology

    2002-06-01

    control may be used to augment an existing inner loop or may be used as a stand-alone controller. The design focuses primarily on the architecture without a stability augmentation system .... augmentation system with model predictive control used as an outer loop. The second architecture replaces the inner and outer loops with a single model...tracking is achieved through two model predictive control architectures, which are discussed. The first architecture has an inner loop stability

  16. Correlation of Predicted and Observed Optical Properties of Multilayer Thermal Control Coatings

    NASA Technical Reports Server (NTRS)

    Jaworske, Donald A.

    1998-01-01

    Thermal control coatings on spacecraft will be increasingly important, as spacecraft grow smaller and more compact. New thermal control coatings will be needed to meet the demanding requirements of next generation spacecraft. Computer programs are now available to design optical coatings and one such program was used to design several thermal control coatings consisting of alternating layers of WO3 and SiO2. The coatings were subsequently manufactured with electron beam evaporation and characterized with both optical and thermal techniques. Optical data were collected in both the visible region of the spectrum and the infrared. Predictions of solar absorptance and infrared emittance were successfully correlated to the observed thermal control properties. Functional performance of the coatings was verified in a bench top thermal vacuum chamber.

  17. Predicted performance benefits of an adaptive digital engine control system of an F-15 airplane

    NASA Technical Reports Server (NTRS)

    Burcham, F. W., Jr.; Myers, L. P.; Ray, R. J.

    1985-01-01

    The highly integrated digital electronic control (HIDEC) program will demonstrate and evaluate the improvements in performance and mission effectiveness that result from integrating engine-airframe control systems. Currently this is accomplished on the NASA Ames Research Center's F-15 airplane. The two control modes used to implement the systems are an integrated flightpath management mode and in integrated adaptive engine control system (ADECS) mode. The ADECS mode is a highly integrated mode in which the airplane flight conditions, the resulting inlet distortion, and the available engine stall margin are continually computed. The excess stall margin is traded for thrust. The predicted increase in engine performance due to the ADECS mode is presented in this report.

  18. Predicted performance benefits of an adaptive digital engine control system on an F-15 airplane

    NASA Technical Reports Server (NTRS)

    Burcham, F. W., Jr.; Myers, L. P.; Ray, R. J.

    1985-01-01

    The highly integrated digital electronic control (HIDEC) program will demonstrate and evaluate the improvements in performance and mission effectiveness that result from integrating engine-airframe control systems. Currently this is accomplished on the NASA Ames Research Center's F-15 airplane. The two control modes used to implement the systems are an integrated flightpath management mode and an integrated adaptive engine control system (ADECS) mode. The ADECS mode is a highly integrated mode in which the airplane flight conditions, the resulting inlet distortion, and the available engine stall margin are continually computed. The excess stall margin is traded for thrust. The predicted increase in engine performance due to the ADECS mode is presented in this report.

  19. Design of a generalized predictive controller for a biological wastewater treatment plant.

    PubMed

    Sadeghassadi, M; Macnab, C J B; Westwick, D

    2016-01-01

    This paper presents a generalized predictive control (GPC) technique to regulate the activated sludge process found in a bioreactor used in wastewater treatment. The control strategy can track dissolved oxygen setpoint changes quickly, adapting to the system uncertainties and disturbances. Tests occur on an Activated Sludge Model No. 1 benchmark of an activated sludge process. A T filter added to the GPC framework results in an effective control strategy in the presence of coloured measurement noise. This work also suggests how a constraint on the measured variable can be added as a penalty term to the GPC framework which leads to improved control of the dissolved oxygen concentration in the presence of dynamic input disturbance.

  20. Higher Executive Control and Visual Memory Performance Predict Treatment Completion in Borderline Personality Disorder

    PubMed Central

    Fertuck, Eric A.; Keilp, John; Song, Inkyung; Morris, Melissa C.; Wilson, Scott T.; Brodsky, Beth S.; Stanley, Barbara

    2011-01-01

    Background Non-completion of a prescribed course of treatment occurs in 20–60% of individuals diagnosed with borderline personality disorder (BPD). While symptom severity, personality traits and environmental factors have been implicated as predictors of treatment non-completion (TNC), there have been no studies of neuropsychological predictors in this population. Methods From a randomized controlled trial, a subsample of 31, unmedicated outpatients diagnosed with BPD with recent self-injurious behavior was assessed on 5 neuropsychological domains. Patients were also assessed for general IQ, demographic and other salient clinical variables. Patients were randomized to one of four treatment conditions, which lasted up to 1 year. Number of weeks in treatment (WIT) up to 1 year was utilized as the index of TNC. Results Thirty-three percent of the subsample (n = 12) did not complete 1 year of treatment. However, more WIT were predicted by better baseline executive control (Trails B; p < 0.01) and visual memory performance (Benton visual retention; p < 0.001); other neuropsychological domains did not predict WIT. Conclusion In the treatment of outpatients with BPD, better executive control and visual memory performance predict more WIT. Assessing and addressing these neurocognitive factors in treatment may reduce TNC in this high-risk population. PMID:22116411

  1. Model-on-Demand Predictive Control for Nonlinear Hybrid Systems With Application to Adaptive Behavioral Interventions

    PubMed Central

    Nandola, Naresh N.; Rivera, Daniel E.

    2011-01-01

    This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087

  2. Brain activity in predictive sensorimotor control for landings: an EEG pilot study.

    PubMed

    Baumeister, J; von Detten, S; van Niekerk, S-M; Schubert, M; Ageberg, E; Louw, Q A

    2013-12-01

    Landing from a jump is related to predictive sensorimotor control. Frontal, central and parietal brain areas are known to play a role in this process based on online sensory feedback. This can be measured by EEG. However, there is only limited knowledge about brain activity during predictive preparation for drop landings (DL). The purpose is to demonstrate changes in brain activity in preparation for DL in different conditions. After resting, 10 athletes performed a series of DLs and were asked to concentrate on the landing preparation for 10 s before an auditory signal required them to drop land from a 30 cm platform. This task was executed before and after a standardized fatigue protocol. EEG spectral power was calculated during DL preparation. Frontal Theta power was increased during preparation compared to rest. Parietal Alpha-2 power demonstrated higher values in preparation after fatigue condition while lower limb kinematics remained unchanged. Cortical activity in frontal and parietal brain areas is sensitive for predictive sensorimotor control of drop landings. Frontal Theta power demonstrates an increase and is related to higher attentional control. In a fatigued condition the parietal Alpha-2 power increase might be related to a deactivation in the somatosensory brain areas.

  3. A Comparison of Dose Metrics to Predict Local Tumor Control for Photofrin-mediated Photodynamic Therapy.

    PubMed

    Qiu, Haixia; Kim, Michele M; Penjweini, Rozhin; Finlay, Jarod C; Busch, Theresa M; Wang, Tianhao; Guo, Wensheng; Cengel, Keith A; Simone, Charles B; Glatstein, Eli; Zhu, Timothy C

    2017-01-13

    This preclinical study examines light fluence, photodynamic therapy (PDT) dose and "apparent reacted singlet oxygen," [(1) O2 ]rx , to predict local control rate (LCR) for Photofrin-mediated PDT of radiation-induced fibrosarcoma (RIF) tumors. Mice bearing RIF tumors were treated with in-air fluences (50-250 J cm(-2) ) and in-air fluence rates (50-150 mW cm(-2) ) at Photofrin dosages of 5 and 15 mg kg(-1) and a drug-light interval of 24 h using a 630-nm, 1-cm-diameter collimated laser. A macroscopic model was used to calculate [(1) O2 ]rx and PDT dose based on in vivo explicit dosimetry of the drug concentration, light fluence and tissue optical properties. PDT dose and [(1) O2 ]rx were defined as a temporal integral of drug concentration and fluence rate, and singlet oxygen concentration consumed divided by the singlet oxygen lifetime, respectively. LCR was stratified for different dose metrics for 74 mice (66 + 8 control). Complete tumor control at 14 days was observed for [(1) O2 ]rx ≥ 1.1 mm or PDT dose ≥1200 μm J cm(-2) but cannot be predicted with fluence alone. LCR increases with increasing [(1) O2 ]rx and PDT dose but is not well correlated with fluence. Comparing dosimetric quantities, [(1) O2 ]rx outperformed both PDT dose and fluence in predicting tumor response and correlating with LCR.

  4. Cell Orientation by a Microgrooved Substrate Can Be Predicted by Automatic Control Theory

    PubMed Central

    Kemkemer, Ralf; Jungbauer, Simon; Kaufmann, Dieter; Gruler, Hans

    2006-01-01

    Cells have the ability to measure and respond to extracellular signals like chemical molecules and topographical surface features by changing their orientation. Here, we examined the orientation of cultured human melanocytes exposed to grooved topographies. To predict the cells' orientation response, we describe the cell behavior with an automatic controller model. The predicted dependence of the cell response to height and spatial frequency of the grooves is obtained by considering the symmetry of the system (cell + substrate). One basic result is that the automatic controller responds to the square of the product of groove height and spatial frequency or to the aspect ratio for symmetric grooves. This theoretical prediction was verified by the experiments, in which melanocytes were exposed to microfabricated poly(dimethylsiloxane) substrates having parallel rectangular grooves of heights (h) between 25 and 200 nm and spatial frequencies (L) between 100 and 500 mm−1. In addition, the model of the cellular automatic controller is extended to include the case of different guiding signals acting simultaneously. PMID:16581835

  5. Jacques Loeb, B. F. Skinner, and the legacy of prediction and control.

    PubMed

    Hackenberg, T D

    1995-01-01

    The biologist Jacques Loeb is an important figure in the history of behavior analysis. Between 1890 and 1915, Loeb championed an approach to experimental biology that would later exert substantial influence on the work of B. F. Skinner and behavior analysis. This paper examines some of these sources of influence, with a particular emphasis on Loeb's firm commitment to prediction and control as fundamental goals of an experimental life science, and how these goals were extended and broadened by Skinner. Both Loeb and Skinner adopted a pragmatic approach to science that put practical control of their subject matter above formal theory testing, both based their research programs on analyses of reproducible units involving the intact organism, and both strongly endorsed technological applications of basic laboratory science. For Loeb, but especially for Skinner, control came to mean something more than mere experimental or technological control for its own sake; it became synonomous with scientific understanding. This view follows from (a) the successful working model of science Loeb and Skinner inherited from Ernst Mach, in which science is viewed as human social activity, and effective practical action is taken as the basis of scientific knowledge, and (b) Skinner's analysis of scientific activity, situated in the world of direct experience and related to practices arranged by scientific verbal communities. From this perspective, prediction and control are human acts that arise from and are maintained by social circumstances in which such acts meet with effective consequences.

  6. Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

    PubMed

    Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei

    2016-02-01

    A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

  7. A non-linear model predictive controller with obstacle avoidance for a space robot

    NASA Astrophysics Data System (ADS)

    Wang, Mingming; Luo, Jianjun; Walter, Ulrich

    2016-04-01

    This study investigates the use of the non-linear model predictive control (NMPC) strategy for a kinematically redundant space robot to approach an un-cooperative target in complex space environment. Collision avoidance, traditionally treated as a high level planning problem, can be effectively translated into control constraints as part of the NMPC. The objective of this paper is to evaluate the performance of the predictive controller in a constrained workspace and to investigate the feasibility of imposing additional constraints into the NMPC. In this paper, we reformulated the issue of the space robot motion control by using NMPC with predefined objectives under input, output and obstacle constraints over a receding horizon. An on-line quadratic programming (QP) procedure is employed to obtain the constrained optimal control decisions in real-time. This study has been implemented for a 7 degree-of-freedom (DOF) kinematically redundant manipulator mounted on a 6 DOF free-floating spacecraft via simulation studies. Real-time trajectory tracking and collision avoidance particularly demonstrate the effectiveness and potential of the proposed NMPC strategy for the space robot.

  8. Generalized Predictive Control of Dynamic Systems with Rigid-Body Modes

    NASA Technical Reports Server (NTRS)

    Kvaternik, Raymond G.

    2013-01-01

    Numerical simulations to assess the effectiveness of Generalized Predictive Control (GPC) for active control of dynamic systems having rigid-body modes are presented. GPC is a linear, time-invariant, multi-input/multi-output predictive control method that uses an ARX model to characterize the system and to design the controller. Although the method can accommodate both embedded (implicit) and explicit feedforward paths for incorporation of disturbance effects, only the case of embedded feedforward in which the disturbances are assumed to be unknown is considered here. Results from numerical simulations using mathematical models of both a free-free three-degree-of-freedom mass-spring-dashpot system and the XV-15 tiltrotor research aircraft are presented. In regulation mode operation, which calls for zero system response in the presence of disturbances, the simulations showed reductions of nearly 100%. In tracking mode operations, where the system is commanded to follow a specified path, the GPC controllers produced the desired responses, even in the presence of disturbances.

  9. Low speed hybrid generalized predictive control of a gasoline-propelled car.

    PubMed

    Romero, M; de Madrid, A P; Mañoso, C; Milanés, V

    2015-07-01

    Low-speed driving in traffic jams causes significant pollution and wasted time for commuters. Additionally, from the passengers׳ standpoint, this is an uncomfortable, stressful and tedious scene that is suitable to be automated. The highly nonlinear dynamics of car engines at low-speed turn its automation in a complex problem that still remains as unsolved. Considering the hybrid nature of the vehicle longitudinal control at low-speed, constantly switching between throttle and brake pedal actions, hybrid control is a good candidate to solve this problem. This work presents the analytical formulation of a hybrid predictive controller for automated low-speed driving. It takes advantage of valuable characteristics supplied by predictive control strategies both for compensating un-modeled dynamics and for keeping passengers security and comfort analytically by means of the treatment of constraints. The proposed controller was implemented in a gas-propelled vehicle to experimentally validate the adopted solution. To this end, different scenarios were analyzed varying road layouts and vehicle speeds within a private test track. The production vehicle is a commercial Citroën C3 Pluriel which has been modified to automatically act over its throttle and brake pedals.

  10. Testing predictions from the male control theory of men's partner violence.

    PubMed

    Bates, Elizabeth A; Graham-Kevan, Nicola; Archer, John

    2014-01-01

    The aim of this study was to test predictions from the male control theory of intimate partner violence (IPV) and Johnson's [Johnson, M. P. (1995). Journal of Marriage and the Family, 57, 282-294] typology. A student sample (N = 1,104) reported on their use of physical aggression and controlling behavior, to partners and to same-sex non-intimates. Contrary to the male control theory, women were found to be more physically aggressive to their partners than men were, and the reverse pattern was found for aggression to same-sex non-intimates. Furthermore, there were no substantial sex differences in controlling behavior, which significantly predicted physical aggression in both sexes. IPV was found to be associated with physical aggression to same-sex non-intimates, thereby demonstrating a link with aggression outside the family. Using Johnson's typology, women were more likely than men to be classed as "intimate terrorists," which was counter to earlier findings. Overall, these results do not support the male control theory of IPV. Instead, they fit the view that IPV does not have a special etiology, and is better studied within the context of other forms of aggression.

  11. Controller Strategies for Automation Tool Use under Varying Levels of Trajectory Prediction Uncertainty

    NASA Technical Reports Server (NTRS)

    Morey, Susan; Prevot, Thomas; Mercer, Joey; Martin, Lynne; Bienert, Nancy; Cabrall, Christopher; Hunt, Sarah; Homola, Jeffrey; Kraut, Joshua

    2013-01-01

    A human-in-the-loop simulation was conducted to examine the effects of varying levels of trajectory prediction uncertainty on air traffic controller workload and performance, as well as how strategies and the use of decision support tools change in response. This paper focuses on the strategies employed by two controllers from separate teams who worked in parallel but independently under identical conditions (airspace, arrival traffic, tools) with the goal of ensuring schedule conformance and safe separation for a dense arrival flow in en route airspace. Despite differences in strategy and methods, both controllers achieved high levels of schedule conformance and safe separation. Overall, results show that trajectory uncertainties introduced by wind and aircraft performance prediction errors do not affect the controllers' ability to manage traffic. Controller strategies were fairly robust to changes in error, though strategies were affected by the amount of delay to absorb (scheduled time of arrival minus estimated time of arrival). Using the results and observations, this paper proposes an ability to dynamically customize the display of information including delay time based on observed error to better accommodate different strategies and objectives.

  12. Digging Soil Experiments for Micro Hydraulic Excavators based on Model Predictive Tracking Control

    NASA Astrophysics Data System (ADS)

    Tomatsu, Takumi; Nonaka, Kenichiro; Sekiguchi, Kazuma; Suzuki, Katsumasa

    2016-09-01

    Recently, the increase of burden to operators and lack of skilled operators are the issue in the work of the hydraulic excavator. These problems are expected to be improved by autonomous control. In this paper, we present experimental results of hydraulic excavators using model predictive control (MPC) which incorporates servo mechanism. MPC optimizes digging operations by the optimal control input which is calculated by predicting the future states and satisfying the constraints. However, it is difficult for MPC to cope with the reaction force from soil when a hydraulic excavator performs excavation. Servo mechanism suppresses the influence of the constant disturbance using the error integration. However, the bucket tip deviates from a specified shape by the sudden change of the disturbance. We can expect that the tracking performance is improved by combining MPC and servo mechanism. Path-tracking controls of the bucket tip are performed using the optimal control input. We apply the proposed method to the Komatsu- made micro hydraulic excavator PC01 by experiments. We show the effectiveness of the proposed method through the experiment of digging soil by comparing servo mechanism and pure MPC with the proposed method.

  13. Model Predictive Control of the Current Profile and the Internal Energy of DIII-D Plasmas

    NASA Astrophysics Data System (ADS)

    Lauret, M.; Wehner, W.; Schuster, E.

    2015-11-01

    For efficient and stable operation of tokamak plasmas it is important that the current density profile and the internal energy are jointly controlled by using the available heating and current-drive (H&CD) sources. The proposed approach is a version of nonlinear model predictive control in which the input set is restricted in size by the possible combinations of the H&CD on/off states. The controller uses real-time predictions over a receding-time horizon of both the current density profile (nonlinear partial differential equation) and the internal energy (nonlinear ordinary differential equation) evolutions. At every time instant the effect of every possible combination of H&CD sources on the current profile and internal energy is evaluated over the chosen time horizon. The combination that leads to the best result, which is assessed by a user-defined cost function, is then applied up until the next time instant. Simulations results based on a control-oriented transport code illustrate the effectiveness of the proposed control method. Supported by the US DOE under DE-FC02-04ER54698 & DE-SC0010661.

  14. Model based predictive control of a high temperature gas cooled power plant coupled to a hydrogen production facility

    NASA Astrophysics Data System (ADS)

    Rhoads, Lloyd A.

    This thesis builds upon recent studies focusing on modeling, operation, and control of high temperature gas cooled reactors. A computer model was developed, based on mass, energy, and momentum balances of control volumes throughout the plant. Several simulations of the plant behavior were conducted and their results were compared with those from the literature. Proportional control was combined with optimal control to form a time varying, adjustable gain predictive controller which adjusts the proportional gains during transients. The controller was designed to utilize control rod motions and bypass control valves to maintain desired plant conditions. An optimization scheme was introduced to efficiently solve the optimization problem formulated as part of the predictive controller operation. Several additional transients were run to examine the full plant controller performance. Multiple predictive controllers were designed and their performance was compared with a proportional controller throughout each transient. The predictive controller results confirmed the importance of proper selection of the optimal controller parameters, in particular the controller time step size and the horizon time. The well-designed proportional controllers clearly demonstrated improvements in plant performance during short time scale transients, namely a loss of secondary heat transfer transient and a step change in desired power transient. Results from long time scale transients demonstrated the capabilities of the proposed bypass control system to control electrical power production without the need for storage vessels.

  15. Predictive functional control for active queue management in congested TCP/IP networks.

    PubMed

    Bigdeli, N; Haeri, M

    2009-01-01

    Predictive functional control (PFC) as a new active queue management (AQM) method in dynamic TCP networks supporting explicit congestion notification (ECN) is proposed. The ability of the controller in handling system delay along with its simplicity and low computational load makes PFC a privileged AQM method in the high speed networks. Besides, considering the disturbance term (which represents model/process mismatches, external disturbances, and existing noise) in the control formulation adds some level of robustness into the PFC-AQM controller. This is an important and desired property in the control of dynamically-varying computer networks. In this paper, the controller is designed based on a small signal linearized fluid-flow model of the TCP/AQM networks. Then, closed-loop transfer function representation of the system is derived to analyze the robustness with respect to the network and controller parameters. The analytical as well as the packet-level ns-2 simulation results show the out-performance of the developed controller for both queue regulation and resource utilization. Fast response, low queue fluctuations (and consequently low delay jitter), high link utilization, good disturbance rejection, scalability, and low packet marking probability are other features of the developed method with respect to other well-known AQM methods such as RED, PI, and REM which are also simulated for comparison.

  16. Adjusting to Job Demands: The Role of Work Self-Determination and Job Control in Predicting Burnout

    ERIC Educational Resources Information Center

    Fernet, Claude; Guay, Frederic; Senecal, Caroline

    2004-01-01

    This study examined the dynamic interplay among job demands, job control, and work self-determination in order to predict burnout dimensions. A three-way interaction effect was found between job demands, job control and work self-determination in predicting each dimension of burnout (emotional exhaustion, depersonalization, and personal…

  17. Flood mitigation through optimal control of a network of multi-purpose reservoirs by using Model Predictive Control

    NASA Astrophysics Data System (ADS)

    MyoLin, Nay; Rutten, Martine; van de Giesen, Nick

    2016-04-01

    Flooding is a common natural disaster in the world. Construction of reservoirs, sluice gates, dikes, embankments and sea walls are implemented to minimize loss of life and property in a flood event. Rather than completely relying on large structural measures, non-structural measures such as real time control of a reservoir system can also improve flood prevention and water supply in a river basin. In this paper, we present the optimal operation of a multi-reservoir system by using Model Predictive Control (MPC) and particular attention is focused on flood mitigation of the Sittaung River Basin, Myanmar. The main challenges are non-linearity in the dynamic behavior of the water system and exponential growth of computational complexity with the state and control dimension. To deal with an issue related to non-linearity, we applied simplified internal model based on linearization scheme with a large grid length. For solving curse of dimensionality, we utilize the reduced model in which the states of the system are reduced by considering outflows from uncontrolled catchments as disturbances in the water system. We also address the computational time for real time control by using large time step scheme. Simulation results indicate that this model is able to use for real time control of a reservoir system addressing trade-offs between the multiple objectives.

  18. Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint

    SciTech Connect

    Smith, Kandler; Shi, Ying; Santhanagopalan, Shriram

    2015-07-29

    Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under different levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.

  19. Model Predictive Control considering Reachable Range of Wheels for Leg / Wheel Mobile Robots

    NASA Astrophysics Data System (ADS)

    Suzuki, Naito; Nonaka, Kenichiro; Sekiguchi, Kazuma

    2016-09-01

    Obstacle avoidance is one of the important tasks for mobile robots. In this paper, we study obstacle avoidance control for mobile robots equipped with four legs comprised of three DoF SCARA leg/wheel mechanism, which enables the robot to change its shape adapting to environments. Our previous method achieves obstacle avoidance by model predictive control (MPC) considering obstacle size and lateral wheel positions. However, this method does not ensure existence of joint angles which achieves reference wheel positions calculated by MPC. In this study, we propose a model predictive control considering reachable mobile ranges of wheels positions by combining multiple linear constraints, where each reachable mobile range is approximated as a convex trapezoid. Thus, we achieve to formulate a MPC as a quadratic problem with linear constraints for nonlinear problem of longitudinal and lateral wheel position control. By optimization of MPC, the reference wheel positions are calculated, while each joint angle is determined by inverse kinematics. Considering reachable mobile ranges explicitly, the optimal joint angles are calculated, which enables wheels to reach the reference wheel positions. We verify its advantages by comparing the proposed method with the previous method through numerical simulations.

  20. Personality traits and individual differences predict threat-induced changes in postural control.

    PubMed

    Zaback, Martin; Cleworth, Taylor W; Carpenter, Mark G; Adkin, Allan L

    2015-04-01

    This study explored whether specific personality traits and individual differences could predict changes in postural control when presented with a height-induced postural threat. Eighty-two healthy young adults completed questionnaires to assess trait anxiety, trait movement reinvestment (conscious motor processing, movement self-consciousness), physical risk-taking, and previous experience with height-related activities. Tests of static (quiet standing) and anticipatory (rise to toes) postural control were completed under low and high postural threat conditions. Personality traits and individual differences significantly predicted height-induced changes in static, but not anticipatory postural control. Individuals less prone to taking physical risks were more likely to lean further away from the platform edge and sway at higher frequencies and smaller amplitudes. Individuals more prone to conscious motor processing were more likely to lean further away from the platform edge and sway at larger amplitudes. Individuals more self-conscious about their movement appearance were more likely to sway at smaller amplitudes. Evidence is also provided that relationships between physical risk-taking and changes in static postural control are mediated through changes in fear of falling and physiological arousal. Results from this study may have indirect implications for balance assessment and treatment; however, further work exploring these factors in patient populations is necessary.

  1. State dependent model predictive control for orbital rendezvous using pulse-width pulse-frequency modulated thrusters

    NASA Astrophysics Data System (ADS)

    Li, Peng; Zhu, Zheng H.; Meguid, S. A.

    2016-07-01

    This paper studies the pulse-width pulse-frequency modulation based trajectory planning for orbital rendezvous and proximity maneuvering near a non-cooperative spacecraft in an elliptical orbit. The problem is formulated by converting the continuous control input, output from the state dependent model predictive control, into a sequence of pulses of constant magnitude by controlling firing frequency and duration of constant-magnitude thrusters. The state dependent model predictive control is derived by minimizing the control error of states and control roughness of control input for a safe, smooth and fuel efficient approaching trajectory. The resulting nonlinear programming problem is converted into a series of quadratic programming problem and solved by numerical iteration using the receding horizon strategy. The numerical results show that the proposed state dependent model predictive control with the pulse-width pulse-frequency modulation is able to effectively generate optimized trajectories using equivalent control pulses for the proximity maneuvering with less energy consumption.

  2. Model Predictive Wind Turbine Control with Move-Blocking Strategy for Load Alleviation and Power Leveling

    NASA Astrophysics Data System (ADS)

    Jassmann, U.; Dickler, S.; Zierath, J.; Hakenberg, M.; Abel, D.

    2016-09-01

    This contribution presents a Model Predictive Controller (MPC) with moveblocking strategy for combined power leveling and load alleviation in wind turbine operation with a focus on extreme loads. The controller is designed for a 3 MW wind turbine developed by W2E Wind to Energy GmbH and compared to a baseline controller, using a classic control scheme, which currently operates the wind turbine. All simulations are carried out with a detailed multibody simulation turbine model implemented in alaska/Wind. The performance of the two different controllers is compared using a 50-year Extreme Operation Gust event, since it is one of the main design drivers for the wind turbine considered in this work. The implemented MPC is able to level electrical output power and reduce mechanical loads at the same time. Without de-rating the achieved control results, a move-blocking strategy is utilized and allowed to reduce the computational burden of the MPC by more than 50% compared to a baseline MPC implementation. This even allows to run the MPC on a state of the art Programmable Logic Controller.

  3. GOBF-ARMA based model predictive control for an ideal reactive distillation column.

    PubMed

    Seban, Lalu; Kirubakaran, V; Roy, B K; Radhakrishnan, T K

    2015-11-01

    This paper discusses the control of an ideal reactive distillation column (RDC) using model predictive control (MPC) based on a combination of deterministic generalized orthonormal basis filter (GOBF) and stochastic autoregressive moving average (ARMA) models. Reactive distillation (RD) integrates reaction and distillation in a single process resulting in process and energy integration promoting green chemistry principles. Improved selectivity of products, increased conversion, better utilization and control of reaction heat, scope for difficult separations and the avoidance of azeotropes are some of the advantages that reactive distillation offers over conventional technique of distillation column after reactor. The introduction of an in situ separation in the reaction zone leads to complex interactions between vapor-liquid equilibrium, mass transfer rates, diffusion and chemical kinetics. RD with its high order and nonlinear dynamics, and multiple steady states is a good candidate for testing and verification of new control schemes. Here a combination of GOBF-ARMA models is used to catch and represent the dynamics of the RDC. This GOBF-ARMA model is then used to design an MPC scheme for the control of product purity of RDC under different operating constraints and conditions. The performance of proposed modeling and control using GOBF-ARMA based MPC is simulated and analyzed. The proposed controller is found to perform satisfactorily for reference tracking and disturbance rejection in RDC.

  4. Cognitive Control Predicts Use of Model-Based Reinforcement-Learning

    PubMed Central

    Otto, A. Ross; Skatova, Anya; Madlon-Kay, Seth; Daw, Nathaniel D.

    2015-01-01

    Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information—in the service of overcoming habitual, stimulus-driven responses—in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior. PMID:25170791

  5. Why achievement motivation predicts success in business but failure in politics: the importance of personal control.

    PubMed

    Winter, David G

    2010-12-01

    Several decades of research have established that implicit achievement motivation (n Achievement) is associated with success in business, particularly in entrepreneurial or sales roles. However, several political psychology studies have shown that achievement motivation is not associated with success in politics; rather, implicit power motivation often predicts political success. Having versus lacking control may be a key difference between business and politics. Case studies suggest that achievement-motivated U.S. presidents and other world leaders often become frustrated and thereby fail because of lack of control, whereas power-motivated presidents develop ways to work with this inherent feature of politics. A reevaluation of previous research suggests that, in fact, relationships between achievement motivation and business success only occur when control is high. The theme of control is also prominent in the development of achievement motivation. Cross-national data are also consistent with this analysis: In democratic industrialized countries, national levels of achievement motivation are associated with strong executive control. In countries with low opportunity for education (thus fewer opportunities to develop a sense of personal control), achievement motivation is associated with internal violence. Many of these manifestations of frustrated achievement motivation in politics resemble authoritarianism. This conclusion is tested by data from a longitudinal study of 113 male college students, showing that high initial achievement motivation combined with frustrated desires for control is related to increases in authoritarianism (F-scale scores) during the college years. Implications for the psychology of leadership and practical politics are discussed.

  6. Level and Change in Perceived Control Predict 19-Year Mortality: Findings from the Americans' Changing Lives Study

    ERIC Educational Resources Information Center

    Infurna, Frank J.; Ram, Nilam; Gerstorf, Denis

    2013-01-01

    Perceived control plays an important role for health across adulthood and old age. However, little is known about the factors that account for such associations and whether changes in control (or control trajectory) uniquely predict major health outcomes over and above mean levels of control. Using data from the nationwide Americans' Changing…

  7. OVARIAN RESERVE TESTS AND THEIR UTILITY IN PREDICTING RESPONSE TO CONTROLLED OVARIAN STIMULATION IN RHESUS MONKEYS

    PubMed Central

    Wu, Julie M.; Takahashi, Diana L; Ingram, Donald K.; Mattison, Julie A.; Roth, George; Ottinger, Mary Ann; Zelinski, Mary B.

    2010-01-01

    Controlled ovarian stimulation (COS) is an alternative to natural breeding in nonhuman primates; however, these protocols are costly with no guarantee of success. Toward the objective of predicting COS outcome in rhesus monkeys, the current study evaluated three clinically used ovarian reserve tests (ORTs): day 3 (d3) follicle-stimulating hormone (FSH) with d3 inhibin B (INHB), the clomiphene citrate challenge test (CCCT), and the exogenous FSH Ovarian Reserve Test (EFORT). A COS was also performed and response was classified as either successful (COS+) or unsuccessful (COS−) and retrospectively compared to ORT predictions. FSH and INHB were assessed for best hormonal index in conjunction with the aforementioned tests. INHB was consistently more accurate than FSH in all ORTs used. Overall, a modified version of the CCCT using INHB values yielded the best percentage of correct predictions. This is the first report of ORT evaluation in rhesus monkeys and may provide a useful diagnostic test prior to costly follicle stimulations, as well as predicting the onset of menopause. PMID:20336797

  8. Prediction of forces and moments for hypersonic flight vehicle control effectors

    NASA Technical Reports Server (NTRS)

    Maughmer, Mark D.; Long, Lyle N.; Pagano, Peter J.

    1991-01-01

    Developing methods of predicting flight control forces and moments for hypersonic vehicles, included a preliminary assessment of subsonic/supersonic panel methods and hypersonic local flow inclination methods for such predictions. While these findings clearly indicate the usefulness of such methods for conceptual design activities, deficiencies exist in some areas. Thus, a second phase of research was proposed in which a better understanding is sought for the reasons of the successes and failures of the methods considered, particularly for the cases at hypersonic Mach numbers. To obtain this additional understanding, a more careful study of the results obtained relative to the methods used was undertaken. In addition, where appropriate and necessary, a more complete modeling of the flow was performed using well proven methods of computational fluid dynamics. As a result, assessments will be made which are more quantitative than those of phase 1 regarding the uncertainty involved in the prediction of the aerodynamic derivatives. In addition, with improved understanding, it is anticipated that improvements resulting in better accuracy will be made to the simple force and moment prediction.

  9. Hedonic Hunger Prospectively Predicts Onset and Maintenance of Loss of Control Eating among College Women

    PubMed Central

    Lowe, Michael R.; Arigo, Danielle; Butryn, Meghan L.; Gilbert, Jennifer R; Sarwer, David; Stice, Eric

    2016-01-01

    Objective The subjective feeling of loss of control (LOC) over eating is common among eating disordered individuals and has predicted weight gain in past research. Restrained eating and negative affect are risk factors for binge eating (which involves LOC), but intense feelings of pleasure derived from palatable foods might also predict the emergence or intensification of LOC eating. The Power of Food Scale (PFS; Lowe et al., 2009) assesses preoccupation with the pleasure derived from palatable food. Method The current sample (n = 294) comprised female college freshmen at risk for weight gain. LOC was assessed using an abbreviated version of the Eating Disorders Examination interview. LOC was assessed at baseline, 6 weeks and 6, 12 and 24 months follow-ups. Results Among those exhibiting LOC eating at baseline, (and controlling for baseline depression, restrained eating and body image dissatisfaction), those scoring higher on the PFS at baseline showed a smaller reduction in LOC frequency over time relative to those scoring lower. Using the same covariates, the PFS predicted the first emergence of LOC over two years among those showing no LOC at baseline. Conclusions These results suggest that powerful hedonic attraction to palatable foods may represent a risk factor for the maintenance of LOC in those initially experiencing it and the emergence of LOC eating in those who are not. An enhanced ability to identify individuals at increased risk of developing or maintaining LOC eating could be useful in prevention programs. PMID:26690638

  10. Prediction of circulation control performance characteristics for Super STOL and STOL applications

    NASA Astrophysics Data System (ADS)

    Naqvi, Messam Abbas

    due to the lack of a simple prediction capability. This research effort was focused on the creation of a rapid prediction capability of Circulation Control Aerodynamic Characteristics which could help designers with rapid performance estimates for design space exploration. A morphological matrix was created with the available set of options which could be chosen to create this prediction capability starting with purely analytical physics based modeling to high fidelity CFD codes. Based on the available constraints, and desired accuracy meta-models have been created around the two dimensional circulation control performance results computed using Navier Stokes Equations (Computational Fluid Dynamics). DSS2, a two dimensional RANS code written by Professor Lakshmi Sankar was utilized for circulation control airfoil characteristics. The CFD code was first applied to the NCCR 1510-7607N airfoil to validate the model with available experimental results. It was then applied to compute the results of a fractional factorial design of experiments array. Metamodels were formulated using the neural networks to the results obtained from the Design of Experiments. Additional validation runs were performed to validate the model predictions. Metamodels are not only capable of rapid performance prediction, but also help generate the relation trends of response matrices with control variables and capture the complex interactions between control variables. Quantitative as well as qualitative assessments of results were performed by computation of aerodynamic forces & moments and flow field visualizations. Wing characteristics in three dimensions were obtained by integration over the whole wing using Prandtl's Wing Theory. The baseline Super STOL configuration [3] was then analyzed with the application of circulation control technology. The desired values of lift and drag to achieve the target values of Takeoff & Landing performance were compared with the optimal configurations obtained

  11. HAS-BLED Predicts Warfarin Control in Australian Patients treated for Deep Vein Thrombosis.

    PubMed

    Mueller, Kylie; Bernaitis, Nijole; Badrick, Tony; Anoopkumar-Dukie, Shailendra

    2017-03-01

    The HAS-BLED model is widely utilized to assess patients' bleed risk prior to anticoagulant therapy including warfarin. Some of the variables assessed in the model are also known to influence warfarin control, commonly measured by time in therapeutic range (TTR). The aim of the study was to determine whether the HAS-BLED risk tool is a good predictor of bleed risk and warfarin control in deep vein thrombosis (DVT) patients. Retrospective data were collected for DVT warfarin care patients at Sullivan Nicolaides Pathology. Data included age, medical history and concurrent drug therapy to calculate HAS-BLED scores. INR results were used to calculate TTR with the Rosendaal method and mean TTR used for analysis and comparison. The eligible 533 patients had a mean TTR of 78.3%. Categorization according to HAS-BLED score resulted in 150 patients classified as low-risk, 331 as moderate-risk and 52 as high-risk with a haemorrhagic incidence per patient of 0.08, 0.53 and 0.54, respectively. Patients in the low-, moderate- and high-risk HAS-BLED categories had a mean TTR of 81%, 79% and 65%, respectively, with significant differences (p < 0.001) found in TTR between the low- and high-risk and moderate- and high-risk categories. In an Australian DVT population, the HAS-BLED score accurately predicts decreasing warfarin control with increasing risk category, and patients with scores ≥3 achieve poor control as indicated by a TTR <70%. In addition to predicting bleed risk, the HAS-BLED tool may also predict the potential benefit of warfarin treatment and hence influence choice of anticoagulant therapy.

  12. A Prediction Method of TV Camera Image for Space Manual-control Rendezvous and Docking

    NASA Astrophysics Data System (ADS)

    Zhen, Huang; Qing, Yang; Wenrui, Wu

    Space manual-control rendezvous and docking (RVD) is a key technology for accomplishing the RVD mission in manned space engineering, especially when automatic control system is out of work. The pilot on chase spacecraft manipulates the hand-stick by the image of target spacecraft captured by TV camera. From the TV image, the relative position and attitude of chase and target spacecrafts can be shown. Therefore, the size, the position, the brightness and the shadow of the target on TV camera are key to guarantee the success of manual-control RVD. A method of predicting the on-orbit TV camera image at different relative positions and light conditions during the process of RVD is discussed. Firstly, the basic principle of capturing the image of cross drone on target spacecraft by TV camera is analyzed theoretically, based which the strategy of manual-control RVD is discussed in detail. Secondly, the relationship between the displayed size or position and the real relative distance of chase and target spacecrafts is presented, the brightness and reflection by the target spacecraft at different light conditions are decribed, the shadow on cross drone caused by the chase or target spacecraft is analyzed. Thirdly, a prediction method of on-orbit TV camera images at certain orbit and light condition is provided, and the characteristics of TV camera image during the RVD is analyzed. Finally, the size, the position, the brightness and the shadow of target spacecraft on TV camera image at typical orbit is simulated. The result, by comparing the simulated images with the real images captured by the TV camera on Shenzhou manned spaceship , shows that the prediction method is reasonable

  13. Wide-area Power System Oscillation Damping using Model Predictive Control Technique

    NASA Astrophysics Data System (ADS)

    Mohamed, Tarek Hassan; Abdel-Rahim, Abdel-Moamen Mohammed; Hassan, Ahmed Abd-Eltawwab; Hiyama, Takashi

    This paper presents a new approach to deal with the problem of robust tuning of power system stabilizer (PSS) and automatic voltage regulator (AVR) in multi-machine power systems. The proposed method is based on a model predictive control (MPC) technique, for improvement stability of the wide-area power system with multiple generators and distribution systems including dispersed generations. The proposed method provides better damping of power system oscillations under small and large disturbances even with the inclusion of local PSSs. The effectiveness of the proposed approach is demonstrated through a two areas, four machines power system. A performance comparison between the proposed controller and some of other controllers is carried out confirming the superiority of the proposed technique. It has also been observed that the proposed algorithm can be successfully applied to larger multiarea power systems and do not suffer with computational difficulties. The proposed algorithm carried out using MATLAB/SIMULINK software package.

  14. Integrated CFD and Controls Analysis Interface for High Accuracy Liquid Propellant Slosh Predictions

    NASA Technical Reports Server (NTRS)

    Marsell, Brandon; Griffin, David; Schallhorn, Paul; Roth, Jacob

    2012-01-01

    Coupling computational fluid dynamics (CFD) with a controls analysis tool elegantly allows for high accuracy predictions of the interaction between sloshing liquid propellants and the control system of a launch vehicle. Instead of relying on mechanical analogs which are n0t va lid during all stages of flight, this method allows for a direct link between the vehicle dynamic environments calculated by the solver in the controls analysis tool to the fluid now equations solved by the CFD code. This paper describes such a coupling methodology, presents the results of a series of test cases, and compares said results against equivalent results from extensively validated tools. The coupling methodology, described herein, has proven to be highly accurate in a variety of different cases.

  15. High Accuracy Liquid Propellant Slosh Predictions Using an Integrated CFD and Controls Analysis Interface

    NASA Technical Reports Server (NTRS)

    Marsell, Brandon; Griffin, David; Schallhorn, Dr. Paul; Roth, Jacob

    2012-01-01

    Coupling computational fluid dynamics (CFD) with a controls analysis tool elegantly allows for high accuracy predictions of the interaction between sloshing liquid propellants and th e control system of a launch vehicle. Instead of relying on mechanical analogs which are not valid during aU stages of flight, this method allows for a direct link between the vehicle dynamic environments calculated by the solver in the controls analysis tool to the fluid flow equations solved by the CFD code. This paper describes such a coupling methodology, presents the results of a series of test cases, and compares said results against equivalent results from extensively validated tools. The coupling methodology, described herein, has proven to be highly accurate in a variety of different cases.

  16. Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

    SciTech Connect

    Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming; Elizondo, Marcelo A.

    2013-01-07

    Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive control (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.

  17. Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

    SciTech Connect

    Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming; Elizondo, Marcelo A.

    2013-04-03

    Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive control (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.

  18. A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles

    NASA Astrophysics Data System (ADS)

    Gao, Yiqi; Gray, Andrew; Tseng, H. Eric; Borrelli, Francesco

    2014-06-01

    This paper proposes a robust control framework for lane-keeping and obstacle avoidance of semiautonomous ground vehicles. It presents a systematic way of enforcing robustness during the MPC design stage. A robust nonlinear model predictive controller (RNMPC) is used to help the driver navigating the vehicle in order to avoid obstacles and track the road centre line. A force-input nonlinear bicycle vehicle model is developed and used in the RNMPC control design. A robust invariant set is used in the RNMPC design to guarantee that state and input constraints are satisfied in the presence of disturbances and model error. Simulations and experiments on a vehicle show the effectiveness of the proposed framework.

  19. Distributed model predictive control for constrained nonlinear systems with decoupled local dynamics.

    PubMed

    Zhao, Meng; Ding, Baocang

    2015-03-01

    This paper considers the distributed model predictive control (MPC) of nonlinear large-scale systems with dynamically decoupled subsystems. According to the coupled state in the overall cost function of centralized MPC, the neighbors are confirmed and fixed for each subsystem, and the overall objective function is disassembled into each local optimization. In order to guarantee the closed-loop stability of distributed MPC algorithm, the overall compatibility constraint for centralized MPC algorithm is decomposed into each local controller. The communication between each subsystem and its neighbors is relatively low, only the current states before optimization and the optimized input variables after optimization are being transferred. For each local controller, the quasi-infinite horizon MPC algorithm is adopted, and the global closed-loop system is proven to be exponentially stable.

  20. Evaluation of Transport and Dispersion Models: A Controlled Comparison of HPAC and NARAC Predictions

    SciTech Connect

    Warner, S; Heagy, J F; Platt, N; Larson, D; Sugiyama, G; Nasstrom, J S; Foster, K T; Bradley, S; Bieberbach, G

    2001-05-01

    During fiscal year 2000, a series of studies in support of the Defense Threat Reduction Agency (DTRA) was begun. The goal of these studies is to improve the verification, validation, and accreditation (VV&A) of hazard prediction and assessment models and capabilities. These studies are part of a larger joint VV&A collaborative effort that DTRA and the Department of Energy (DOE), via the Lawrence Livermore National Laboratory (LLNL), are conducting. This joint effort includes comparisons of the LLNL and DTRA transport and dispersion (T&D) modeling systems, NARAC and HPAC, respectively. The purpose of this work is to compare, in a systematic way, HPAC and NARAC model predictions for a set of controlled hypothetical release scenarios. Only ''model-versus-model'' comparisons are addressed in this work. Model-to-field trial comparisons for HPAC and NARAC have been addressed in a recent companion study, in support of the same joint VV&A effort.

  1. Model Predictions of Chemically Controlled Slow Crack Growth with Application to Mechanical Effects in Geothermal Environments

    SciTech Connect

    Viani, B E

    2001-04-11

    Representative, simplified geothermal rock-fluid systems are investigated with a modeling approach to estimate how rock water interactions affect coupled properties related to mechanical stability and permeability improvement through fracturing. First, geochemical modeling is used to determine the evolution of fluid chemistry at temperatures up to 300 C when fluids are in contact with representative rocks of continental origin. Then, a kinetic crack growth model for quartz is used to predict growth rate for subcritical cracks in acidic and basic environments. The predicted growth rate is highly sensitive to temperature and pH in the ranges tested. At present, the model is limited to situations in which quartz controls the mechanical process of interest, such as well bore stability in silica cemented rocks and the opening of quartz filled veins to enhance permeability.

  2. Power and Performance Management in Nonlinear Virtualized Computing Systems via Predictive Control.

    PubMed

    Wen, Chengjian; Mu, Yifen

    2015-01-01

    The problem of power and performance management captures growing research interest in both academic and industrial field. Virtulization, as an advanced technology to conserve energy, has become basic architecture for most data centers. Accordingly, more sophisticated and finer control are desired in virtualized computing systems, where multiple types of control actions exist as well as time delay effect, which make it complicated to formulate and solve the problem. Furthermore, because of improvement on chips and reduction of idle power, power consumption in modern machines shows significant nonlinearity, making linear power models(which is commonly adopted in previous work) no longer suitable. To deal with this, we build a discrete system state model, in which all control actions and time delay effect are included by state transition and performance and power can be defined on each state. Then, we design the predictive controller, via which the quadratic cost function integrating performance and power can be dynamically optimized. Experiment results show the effectiveness of the controller. By choosing a moderate weight, a good balance can be achieved between performance and power: 99.76% requirements can be dealt with and power consumption can be saved by 33% comparing to the case with open loop controller.

  3. Power and Performance Management in Nonlinear Virtualized Computing Systems via Predictive Control

    PubMed Central

    Wen, Chengjian; Mu, Yifen

    2015-01-01

    The problem of power and performance management captures growing research interest in both academic and industrial field. Virtulization, as an advanced technology to conserve energy, has become basic architecture for most data centers. Accordingly, more sophisticated and finer control are desired in virtualized computing systems, where multiple types of control actions exist as well as time delay effect, which make it complicated to formulate and solve the problem. Furthermore, because of improvement on chips and reduction of idle power, power consumption in modern machines shows significant nonlinearity, making linear power models(which is commonly adopted in previous work) no longer suitable. To deal with this, we build a discrete system state model, in which all control actions and time delay effect are included by state transition and performance and power can be defined on each state. Then, we design the predictive controller, via which the quadratic cost function integrating performance and power can be dynamically optimized. Experiment results show the effectiveness of the controller. By choosing a moderate weight, a good balance can be achieved between performance and power: 99.76% requirements can be dealt with and power consumption can be saved by 33% comparing to the case with open loop controller. PMID:26225769

  4. Predictive transport simulations of real-time profile control in JET advanced tokamak plasmas

    NASA Astrophysics Data System (ADS)

    Tala, T.; Laborde, L.; Mazon, D.; Moreau, D.; Corrigan, G.; Crisanti, F.; Garbet, X.; Heading, D.; Joffrin, E.; Litaudon, X.; Parail, V.; Salmi, A.; EFDA-JET workprogramme, contributors to the

    2005-09-01

    Predictive, time-dependent transport simulations with a semi-empirical plasma model have been used in closed-loop simulations to control the q-profile and the strength and location of the internal transport barrier (ITB). Five transport equations (Te, Ti, q, ne, vΦ) are solved, and the power levels of lower hybrid current drive, NBI and ICRH are calculated in a feedback loop determined by the feedback controller matrix. The real-time control (RTC) technique and algorithms used in the transport simulations are identical to those implemented and used in JET experiments (Laborde L. et al 2005 Plasma Phys. Control. Fusion 47 155 and Moreau D. et al 2003 Nucl. Fusion 43 870). The closed-loop simulations with RTC demonstrate that varieties of q-profiles and pressure profiles in the ITB can be achieved and controlled simultaneously. The simulations also showed that with the same RTC technique as used in JET experiments, it is possible to sustain the q-profiles and pressure profiles close to their set-point profiles for longer than the current diffusion time. In addition, the importance of being able to handle the multiple time scales to control the location and strength of the ITB is pointed out. Several future improvements and perspectives of the RTC scheme are presented.

  5. Model Predictive Control with Integral Action for Current Density Profile Tracking in NSTX-U

    NASA Astrophysics Data System (ADS)

    Ilhan, Z. O.; Wehner, W. P.; Schuster, E.; Boyer, M. D.

    2016-10-01

    Active control of the toroidal current density profile may play a critical role in non-inductively sustained long-pulse, high-beta scenarios in a spherical torus (ST) configuration, which is among the missions of the NSTX-U facility. In this work, a previously developed physics-based control-oriented model is embedded in a feedback control scheme based on a model predictive control (MPC) strategy to track a desired current density profile evolution specified indirectly by a desired rotational transform profile. An integrator is embedded into the standard MPC formulation to reject various modeling uncertainties and external disturbances. Neutral beam powers, electron density, and total plasma current are used as actuators. The proposed MPC strategy incorporates various state and actuator constraints directly into the control design process by solving a constrained optimization problem in real-time to determine the optimal actuator requests. The effectiveness of the proposed controller in regulating the current density profile in NSTX-U is demonstrated in closed-loop nonlinear simulations. Supported by the US DOE under DE-AC02-09CH11466.

  6. Physical modelling and adaptive predictive control of diffusion/LPCVD reactors

    NASA Astrophysics Data System (ADS)

    Dewaard, H.

    1992-12-01

    The aim of this study is to design a temperature controller for batch electric diffusion/low pressure chemical vapor deposition (LPCVD) furnaces, that complies with the increasingly more stringent requirements of VLSI processing. A mathematical model has been developed for batch electric diffusion/LPCVD reactors that are currently used in the semiconductor industry for the fabrication of micro-electronic devices. The model has been formulated in terms of partial integro-differential equations, which are derived from the basic energy conservation law of physics. The model takes into account the effects of radiation and conduction. Chapter 2 gives a detailed description of the furnace system and provides some insight into the processes that take place. In chapter 3, the model of the diffusion/LPPCVD furnace is derived. Chapter 4 deals with the design of a temperature control system for the diffusion/LPCVD reactor, that makes use of the model as developed in chapter 3. Chapter 5 gives the results of the control designs, both of simulation and of application on a real furnace. Results of the linear quadratic Gaussian controller, the (non-adaptive) reduced order controller, and the adaptive predictive controller are presented. Finally, in chapter 6, some conclusions are drawn and suggestions for further research are given.

  7. Improved model predictive control of resistive wall modes by error field estimator in EXTRAP T2R

    NASA Astrophysics Data System (ADS)

    Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.

    2016-12-01

    Many implementations of a model-based approach for toroidal plasma have shown better control performance compared to the conventional type of feedback controller. One prerequisite of model-based control is the availability of a control oriented model. This model can be obtained empirically through a systematic procedure called system identification. Such a model is used in this work to design a model predictive controller to stabilize multiple resistive wall modes in EXTRAP T2R reversed-field pinch. Model predictive control is an advanced control method that can optimize the future behaviour of a system. Furthermore, this paper will discuss an additional use of the empirical model which is to estimate the error field in EXTRAP T2R. Two potential methods are discussed that can estimate the error field. The error field estimator is then combined with the model predictive control and yields better radial magnetic field suppression.

  8. Threshold behavior in hydrological systems and geo-ecosystems: manifestations, controls and implications for predictability

    NASA Astrophysics Data System (ADS)

    Zehe, E.; Sivapalan, M.

    2008-11-01

    The aim of this paper is to provide evidence that the dynamics of hydrological systems and geo-ecosystems is often influenced by threshold behavior at a variety of space and time scales. Based on well known characteristics of elementary threshold phenomena we suggest criteria for detecting threshold behavior in hydrological systems. The most important one is intermittence of phenomena, i.e. the rapid switching of related state variables/fluxes from zero to finite values, or existence of behavior regimes where the same process/response appears qualitatively differently at the macroscopic level. From the literature we present several examples for intermittent hydrological phenomena, ranging from overland flow generation in different landscapes, including the effects of hydrophobicity, to soil water flow occurring in the matrix continuum or via preferential pathways, including the case of cracking soils, nonlinear subsurface stormflow response of hillslopes during severe rainfall events, and long-term catchment flooding responses. Since threshold phenomena are often associated with environmental hazards such as floods, soil erosion, and contamination of shallow groundwater resources, we discuss common difficulties that complicate predictions of whether or not they might even occur. Predicting the onset of threshold phenomena requires a thorough understanding of the underlying controls. Through examples we illustrate that threshold behavior in hydrological systems can manifest at (a) the process level, (b) the response level, and (c) the functional level, and explain that the complexity of the underlying controls and of the interacting phenomena that determine threshold behavior become increasingly complex at the higher levels. Finally we provide evidence from field observations and model predictions that show that within an "unstable range" of system states "close" to a threshold, it is difficult to predict whether or not the system will switch behavior, for instance

  9. Models of basal ganglia and cerebellum for sensorimotor integration and predictive control

    NASA Astrophysics Data System (ADS)

    Jabri, Marwan A.; Huang, Jerry; Coenen, Olivier J. D.; Sejnowski, Terrence J.

    2000-10-01

    This paper presents a sensorimotor architecture integrating computational models of a cerebellum and a basal ganglia and operating on a microrobot. The computational models enable a microrobot to learn to track a moving object and anticipate future positions using a CCD camera. The architecture features pre-processing modules for coordinate transformation and instantaneous orientation extraction. Learning of motor control is implemented using predictive Hebbian reinforcement-learning algorithm in the basal ganglia model. Learning of sensory predictions makes use of a combination of long-term depression (LTD) and long-term potentiation (LTP) adaptation rules within the cerebellum model. The basal ganglia model uses the visual inputs to develop sensorimotor mapping for motor control, while the cerebellum module uses robot orientation and world- coordinate transformed inputs to predict the location of the moving object in a robot centered coordinate system. We propose several hypotheses about the functional role of cell populations in the cerebellum and argue that mossy fiber projections to the deep cerebellar nucleus (DCN) could play a coordinate transformation role and act as gain fields. We propose that such transformation could be learnt early in the brain development stages and could be guided by the activity of the climbing fibers. Proprioceptor mossy fibers projecting to the DCN and providing robot orientation with respect to a reference system could be involved in this case. Other mossy fibers carrying visual sensory input provide visual patterns to the granule cells. The combined activities of the granule and the Purkinje cells store spatial representations of the target patterns. The combinations of mossy and Purkinje projections to the DCN provide a prediction of the location of the moving target taking into consideration the robot orientation. Results of lesion simulations based on our model show degradations similar to those reported in cerebellar lesion

  10. Model Predictive Control of A Matrix-Converter Based Solid State Transformer for Utility Grid Interaction

    SciTech Connect

    Xue, Yaosuo

    2016-01-01

    The matrix converter solid state transformer (MC-SST), formed from the back-to-back connection of two three-to-single-phase matrix converters, is studied for use in the interconnection of two ac grids. The matrix converter topology provides a light weight and low volume single-stage bidirectional ac-ac power conversion without the need for a dc link. Thus, the lifetime limitations of dc-bus storage capacitors are avoided. However, space vector modulation of this type of MC-SST requires to compute vectors for each of the two MCs, which must be carefully coordinated to avoid commutation failure. An additional controller is also required to control power exchange between the two ac grids. In this paper, model predictive control (MPC) is proposed for an MC-SST connecting two different ac power grids. The proposed MPC predicts the circuit variables based on the discrete model of MC-SST system and the cost function is formulated so that the optimal switch vector for the next sample period is selected, thereby generating the required grid currents for the SST. Simulation and experimental studies are carried out to demonstrate the effectiveness and simplicity of the proposed MPC for such MC-SST-based grid interfacing systems.

  11. Nonlinear model predictive control using parameter varying BP-ARX combination model

    NASA Astrophysics Data System (ADS)

    Yang, J.-F.; Xiao, L.-F.; Qian, J.-X.; Li, H.

    2012-03-01

    A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.

  12. Nonparametric Hammerstein model based model predictive control for heart rate regulation.

    PubMed

    Su, Steven W; Huang, Shoudong; Wang, Lu; Celler, Branko G; Savkin, Andrey V; Guo, Ying; Cheng, Teddy

    2007-01-01

    This paper proposed a novel nonparametric model based model predictive control approach for the regulation of heart rate during treadmill exercise. As the model structure of human cardiovascular system is often hard to determine, nonparametric modelling is a more realistic manner to describe complex behaviours of cardiovascular system. This paper presents a new nonparametric Hammerstein model identification approach for heart rate response modelling. Based on the pseudo-random binary sequence experiment data, we decouple the identification of linear dynamic part and input nonlinearity of the Hammerstein system. Correlation analysis is applied to acquire step response of linear dynamic component. Support Vector Regression is adopted to obtain a nonparametric description of the inverse of input static nonlinearity that is utilized to form an approximate linear model of the Hammerstein system. Based on the established model, a model predictive controller under predefined speed and acceleration constraints is designed to achieve safer treadmill exercise. Simulation results show that the proposed control algorithm can achieve optimal heart rate tracking performance under predefined constraints.

  13. Perceived Sexual Control, Sex-Related Alcohol Expectancies and Behavior Predict Substance-Related Sexual Revictimization

    PubMed Central

    Walsh, Kate; Messman-Moore, Terri; Zerubavel, Noga; Chandley, Rachel B.; DeNardi, Kathleen A.; Walker, Dave P.

    2013-01-01

    Objectives Although numerous studies have documented linkages between childhood sexual abuse (CSA) and later sexual revictimization, mechanisms underlying revictimization, particularly assaults occurring in the context of substance use, are not well-understood. Consistent with Traumagenic Dynamics theory, the present study tested a path model positing that lowered perceptions of sexual control resulting from CSA may be associated with increased sex-related alcohol expectancies and heightened likelihood of risky sexual behavior, which in turn, may predict adult substance-related rape. Methods Participants were 546 female college students who completed anonymous surveys regarding CSA and adult rape, perceptions of sexual control, sex-related alcohol expectancies, and likelihood of engaging in risky sexual behavior. Results The data fit the hypothesized model well and all hypothesized path coefficients were significant and in the expected directions. As expected, sex-related alcohol expectancies and likelihood of risky sexual behavior only predicted substance-related rape, not forcible rape. Conclusions Findings suggested that low perceived sexual control stemming from CSA is associated with increased sex-related alcohol expectancies and a higher likelihood of engaging in sexual behavior in the context of alcohol use. In turn these proximal risk factors heighten vulnerability to substance-related rape. Programs which aim to reduce risk for substance-related rape could be improved by addressing expectancies and motivations for risky sexual behavior in the context of substance use. Implications and future directions are discussed. PMID:23312991

  14. A Hierarchical Model Predictive Tracking Control for Independent Four-Wheel Driving/Steering Vehicles with Coaxial Steering Mechanism

    NASA Astrophysics Data System (ADS)

    Itoh, Masato; Hagimori, Yuki; Nonaka, Kenichiro; Sekiguchi, Kazuma

    2016-09-01

    In this study, we apply a hierarchical model predictive control to omni-directional mobile vehicle, and improve the tracking performance. We deal with an independent four-wheel driving/steering vehicle (IFWDS) equipped with four coaxial steering mechanisms (CSM). The coaxial steering mechanism is a special one composed of two steering joints on the same axis. In our previous study with respect to IFWDS with ideal steering, we proposed a model predictive tracking control. However, this method did not consider constraints of the coaxial steering mechanism which causes delay of steering. We also proposed a model predictive steering control considering constraints of this mechanism. In this study, we propose a hierarchical system combining above two control methods for IFWDS. An upper controller, which deals with vehicle kinematics, runs a model predictive tracking control, and a lower controller, which considers constraints of coaxial steering mechanism, runs a model predictive steering control which tracks the predicted steering angle optimized an upper controller. We verify the superiority of this method by comparing this method with the previous method.

  15. Multivariate morphological brain signatures predict chronic abdominal pain patients from healthy control subjects

    PubMed Central

    Labus, Jennifer S.; Van Horn, John D.; Gupta, Arpana; Alaverdyan, Mher; Torgerson, Carinna; Ashe-McNalley, Cody; Irimia, Andrei; Hong, Jui-Yang; Naliboff, Bruce; Tillisch, Kirsten; Mayer, Emeran A.

    2015-01-01

    Irritable bowel syndrome (IBS) is the most common chronic visceral pain disorder. The pathophysiology of IBS is incompletely understood, however evidence strongly suggests dysregulation of the brain-gut axis. The aim of this study was to apply multivariate pattern analysis to identify an IBS-related morphometric brain signature which could serve as a central biological marker and provide new mechanistic insights into the pathophysiology of IBS. Parcellation of 165 cortical and subcortical regions was performed using Freesurfer and the Destrieux and Harvard-Oxford atlases. Volume, mean curvature, surface area and cortical thickness were calculated for each region. Sparse partial least squares-discriminant analysis was applied to develop a diagnostic model using a training set of 160 females (80 healthy controls, 80 IBS). Predictive accuracy was assessed in an age matched holdout test set of 52 females (26 health controls, 26 IBS). A two-component classification algorithm comprised of the morphometry of 1) primary somato-sensory and motor regions, and 2) multimodal network regions, explained 36% of the variance. Overall predictive accuracy of the classification algorithm was 70%. Small effect size associations were observed between the somatosensory and motor signature and non-gastrointestinal somatic symptoms. The findings demonstrate the predictive accuracy of a classification algorithm based solely on regional brain morphometry is not sufficient but they do provide support for the utility of multivariate pattern analysis for identifying meaningful neurobiological markers in IBS. Perspective This article presents the development, optimization, and testing of a classification algorithm for discriminating female IBS patients from healthy controls using only brain morphometry data. The results provide support for utility of multivariate pattern analysis for identifying meaningful neurobiological markers in IBS. PMID:25906347

  16. Internally directed cognition and mindfulness: an integrative perspective derived from predictive and reactive control systems theory

    PubMed Central

    Tops, Mattie; Boksem, Maarten A. S.; Quirin, Markus; IJzerman, Hans; Koole, Sander L.

    2013-01-01

    In the present paper, we will apply the predictive and reactive control systems (PARCS) theory as a framework that integrates competing theories of neural substrates of awareness by describing the “default mode network” (DMN) and anterior insula (AI) as parts of two different behavioral and homeostatic control systems. The DMN, a network that becomes active at rest when there is no external stimulation or task to perform, has been implicated in self-reflective awareness and prospection. By contrast, the AI is associated with awareness and task-related attention. This has led to competing theories stressing the role of the DMN in self-awareness vs. the role of interoceptive and emotional information integration in the AI in awareness of the emotional moment. In PARCS, the respective functions of the DMN and AI in a specific control system explains their association with different qualities of awareness, and how mental states can shift from one state (e.g., prospective self-reflection) to the other (e.g., awareness of the emotional moment) depending on the relative dominance of control systems. These shifts between reactive and predictive control are part of processes that enable the intake of novel information, integration of this novel information within existing knowledge structures, and the creation of a continuous personal context in which novel information can be integrated and understood. As such, PARCS can explain key characteristics of mental states, such as their temporal and spatial focus (e.g., a focus on the here and now vs. the future; a first person vs. a third person perspective). PARCS further relates mental states to brain states and functions, such as activation of the DMN or hemispheric asymmetry in frontal cortical functions. Together, PARCS deepens the understanding of a broad range of mental states, including mindfulness, mind wandering, rumination, autobiographical memory, imagery, and the experience of self. PMID:24904455

  17. Low Vitamin D Levels Do Not Predict Hyperglycemia in Elderly Endurance Athletes (but in Controls)

    PubMed Central

    Nistler, Sonja; Batmyagmar, Delgerdalai; Ponocny-Seliger, Elisabeth; Perkmann, Thomas; Scherzer, Thomas M.; Kundi, Michael; Endler, Georg; Ratzinger, Franz; Pilger, Alexander; Wagner, Oswald F.; Winker, Robert

    2016-01-01

    Background and Aim Recent studies revealed a link between hypovitaminosis D3 and the risk for hyperglycemia. Further mechanistic and interventional investigations suggested a common reason for both conditions rather than a causal relationship. Exposure to sunlight is the most relevant source of vitamin D3 (25(OH)D), whereas adipose tissue is able to store relevant amounts of the lipophilic vitamin. Since running/bicycling leads to increased out-door time and alters physiological response mechanisms, it can be hypothesized that the correlation between hypovitaminosis D3 and hyperglycemia might be disturbed in outdoor athletes. Methods 47 elderly marathoners/bicyclists and 47 age/sex matched controls were studied in a longitudinal setting at baseline and after three years. HbA1c as a surrogate for (pre-)diabetic states was quantified via HPLC, 25(OH)D levels were measured by means of chemiluminescent assays. Physical performance was assessed by ergometry. Results When adjusted for seasonal variations, 25(OH)D was significantly higher in athletes than in controls. 25(OH)D levels inversely correlated with triglycerides in both groups, whereas only in controls an association between high BMI or low physical performance with hypovitaminosis D3 had been found. Likewise, the presence of hypovitaminosis D3 at baseline successfully predicted hyperglycemia at the follow up examinations within the control group (AUC = 0.85, 95% CI [0.74, 0.96], p < .001, statistically independent from BMI), but not in athletes. Conclusion Our data suggest that mechanisms of HbA1c elevation might differ between athletes and controls. Thus, intense physical activity must be taken into account as a potential pre-analytic confounder when it is aimed to predict metabolic risk by vitamin D3 levels. PMID:27304888

  18. Specific predictive power of automatic spider-related affective associations for controllable and uncontrollable fear responses toward spiders.

    PubMed

    Huijding, Jorg; de Jong, Peter J

    2006-02-01

    This study examined the predictive power of automatically activated spider-related affective associations for automatic and controllable fear responses. The Extrinsic Affective Simon Task (EAST; De Houwer, 2003) was used to indirectly assess automatic spider fear-related associations. The EAST and the Fear of Spiders Questionnaire (FSQ) were used to predict fear responses in 48 female students from Maastricht University with varying levels of spider fear. Results showed that: (i) the EAST best predicted automatic fear responses, whereas (ii) the FSQ best predicted strategic avoidance behavior. These results suggest that indirect measures of automatic associations may have specific predictive power for automatic fear responses.

  19. Advanced Train and Traffic Control Based on Prediction of Train Movement

    NASA Astrophysics Data System (ADS)

    Hiraguri, Shigeto; Hirao, Yuji; Watanabe, Ikuo; Tomii, Norio; Hase, Shinichi

    Trains are often forced to decelerate or stop between stations on commuter lines due to the delay of the preceding train. If a train stops between stations, both the travel time and the interval between trains will increase. This situation has an adverse effect on energy consumption. To solve this problem, we propose a new train control method based on the prediction of train movement and data communications between railway sub-systems. Computer simulations are carried out to verify the effect of the proposed method. As a result, it has been proved that the new method reduces the train stopping time between stations and the electric energy consumption at substations.

  20. Graph-Switching Based Modeling of Mode Transition Constraints for Model Predictive Control of Hybrid Systems

    NASA Astrophysics Data System (ADS)

    Kobayashi, Koichi; Hiraishi, Kunihiko

    The model predictive/optimal control problem for hybrid systems is reduced to a mixed integer quadratic programming (MIQP) problem. However, the MIQP problem has one serious weakness, i.e., the computation time to solve the MIQP problem is too long for practical plants. For overcoming this technical issue, there are several approaches. In this paper, a modeling of mode transition constraints, which are expressed by a directed graph, is focused, and a new method to represent a directed graph is proposed. The effectiveness of the proposed method is shown by numerical examples on linear switched systems and piecewise linear systems.

  1. Prediction of the lifetime of the elements of the safety and control rods of nuclear reactors

    SciTech Connect

    Voskoboinikov, V.V.; Emel'yanov, I. Ya.; Lineva, A.F.; Pushkin, S.N.; Semchenko, E.L.; Usov, P.P.

    1987-07-01

    The authors construct a mathematical model based on the analytical solution to such parameters as magnetic flux, alignment, sliding friction, erosion, abrasion, corrosion, and irradiation for the purpose of predicting the service life of electromagnetically driven control rods and their drives. The analytical solution was verified experimentally on a bench simulation where it was found that incorrect assembly and alignment not only serve as the largest contributors to shortened service life of the rods and drives but also render the calculations of the model invalid.

  2. Prediction and control of vortex-dominated and vortex-wake flows

    NASA Technical Reports Server (NTRS)

    Kandil, Osama

    1993-01-01

    This progress report documents the accomplishments achieved in the period from December 1, 1992 until November 30, 1993. These accomplishments include publications, national and international presentations, NASA presentations, and the research group supported under this grant. Topics covered by documents incorporated into this progress report include: active control of asymmetric conical flow using spinning and rotary oscillation; supersonic vortex breakdown over a delta wing in transonic flow; shock-vortex interaction over a 65-degree delta wing in transonic flow; three dimensional supersonic vortex breakdown; numerical simulation and physical aspects of supersonic vortex breakdown; and prediction of asymmetric vortical flows around slender bodies using Navier-Stokes equations.

  3. Data quality assurance and control in cognitive research: Lessons learned from the PREDICT-HD study.

    PubMed

    Westervelt, Holly James; Bernier, Rachel A; Faust, Melanie; Gover, Mary; Bockholt, H Jeremy; Zschiegner, Roland; Long, Jeffrey D; Paulsen, Jane S

    2017-02-17

    We discuss the strategies employed in data quality control and quality assurance for the cognitive core of Neurobiological Predictors of Huntington's Disease (PREDICT-HD), a long-term observational study of over 1,000 participants with prodromal Huntington disease. In particular, we provide details regarding the training and continual evaluation of cognitive examiners, methods for error corrections, and strategies to minimize errors in the data. We present five important lessons learned to help other researchers avoid certain assumptions that could potentially lead to inaccuracies in their cognitive data.

  4. Modelling and control issues of dynamically substructured systems: adaptive forward prediction taken as an example

    PubMed Central

    Tu, Jia-Ying; Hsiao, Wei-De; Chen, Chih-Ying

    2014-01-01

    Testing techniques of dynamically substructured systems dissects an entire engineering system into parts. Components can be tested via numerical simulation or physical experiments and run synchronously. Additional actuator systems, which interface numerical and physical parts, are required within the physical substructure. A high-quality controller, which is designed to cancel unwanted dynamics introduced by the actuators, is important in order to synchronize the numerical and physical outputs and ensure successful tests. An adaptive forward prediction (AFP) algorithm based on delay compensation concepts has been proposed to deal with substructuring control issues. Although the settling performance and numerical conditions of the AFP controller are improved using new direct-compensation and singular value decomposition methods, the experimental results show that a linear dynamics-based controller still outperforms the AFP controller. Based on experimental observations, the least-squares fitting technique, effectiveness of the AFP compensation and differences between delay and ordinary differential equations are discussed herein, in order to reflect the fundamental issues of actuator modelling in relevant literature and, more specifically, to show that the actuator and numerical substructure are heterogeneous dynamic components and should not be collectively modelled as a homogeneous delay differential equation. PMID:25104902

  5. Approximate model predictive control laws for constrained nonlinear discrete-time systems: analysis and offline design

    NASA Astrophysics Data System (ADS)

    Pin, G.; Filippo, M.; Pellegrino, F. A.; Fenu, G.; Parisini, T.

    2013-05-01

    The objective of this work consists in the offline approximation of possibly discontinuous model predictive control laws for nonlinear discrete-time systems, while enforcing hard constraints on state and input variables. Obtaining an offline approximation of the receding horizon control law may lead to a very significant reduction of the online computational burden with respect to algorithms based on iterated optimization, thus allowing the application to fast dynamics plants. The proposed approximation scheme allows to cope with discontinuous control laws, such as those arising from constrained nonlinear finite horizon optimal control problems. A detailed stability analysis of the closed-loop system driven by the approximated state-feedback controller shows that the devised technique guarantees the input-to-state practical stability with respect to the (non-fading) approximation-induced errors. Two examples are provided to show the effectiveness of the method when the approximator is chosen either as a discontinuous nearest point function or as a smooth neural network.

  6. Nonlinear predictive control for durability enhancement and efficiency improvement in a fuel cell power system

    NASA Astrophysics Data System (ADS)

    Luna, Julio; Jemei, Samir; Yousfi-Steiner, Nadia; Husar, Attila; Serra, Maria; Hissel, Daniel

    2016-10-01

    In this work, a nonlinear model predictive control (NMPC) strategy is proposed to improve the efficiency and enhance the durability of a proton exchange membrane fuel cell (PEMFC) power system. The PEMFC controller is based on a distributed parameters model that describes the nonlinear dynamics of the system, considering spatial variations along the gas channels. Parasitic power from different system auxiliaries is considered, including the main parasitic losses which are those of the compressor. A nonlinear observer is implemented, based on the discretised model of the PEMFC, to estimate the internal states. This information is included in the cost function of the controller to enhance the durability of the system by means of avoiding local starvation and inappropriate water vapour concentrations. Simulation results are presented to show the performance of the proposed controller over a given case study in an automotive application (New European Driving Cycle). With the aim of representing the most relevant phenomena that affects the PEMFC voltage, the simulation model includes a two-phase water model and the effects of liquid water on the catalyst active area. The control model is a simplified version that does not consider two-phase water dynamics.

  7. Algorithms for a Closed-Loop Artificial Pancreas: The Case for Model Predictive Control

    PubMed Central

    Bequette, B. Wayne

    2013-01-01

    The relative merits of model predictive control (MPC) and proportional-integral-derivative (PID) control are discussed, with the end goal of a closed-loop artificial pancreas (AP). It is stressed that neither MPC nor PID are single algorithms, but rather are approaches or strategies that may be implemented very differently by different engineers. The primary advantages to MPC are that (i) constraints on the insulin delivery rate (and/or insulin on board) can be explicitly included in the control calculation; (ii) it is a general framework that makes it relatively easy to include the effect of meals, exercise, and other events that are a function of the time of day; and (iii) it is flexible enough to include many different objectives, from set-point tracking (target) to zone (control to range). In the end, however, it is recognized that the control algorithm, while important, represents only a portion of the effort required to develop a closed-loop AP. Thus, any number of algorithms/approaches can be successful—the engineers involved in the design must have experience with the particular technique, including the important experience of implementing the algorithm in human studies and not simply through simulation studies. PMID:24351190

  8. Modelling and control issues of dynamically substructured systems: adaptive forward prediction taken as an example.

    PubMed

    Tu, Jia-Ying; Hsiao, Wei-De; Chen, Chih-Ying

    2014-08-08

    Testing techniques of dynamically substructured systems dissects an entire engineering system into parts. Components can be tested via numerical simulation or physical experiments and run synchronously. Additional actuator systems, which interface numerical and physical parts, are required within the physical substructure. A high-quality controller, which is designed to cancel unwanted dynamics introduced by the actuators, is important in order to synchronize the numerical and physical outputs and ensure successful tests. An adaptive forward prediction (AFP) algorithm based on delay compensation concepts has been proposed to deal with substructuring control issues. Although the settling performance and numerical conditions of the AFP controller are improved using new direct-compensation and singular value decomposition methods, the experimental results show that a linear dynamics-based controller still outperforms the AFP controller. Based on experimental observations, the least-squares fitting technique, effectiveness of the AFP compensation and differences between delay and ordinary differential equations are discussed herein, in order to reflect the fundamental issues of actuator modelling in relevant literature and, more specifically, to show that the actuator and numerical substructure are heterogeneous dynamic components and should not be collectively modelled as a homogeneous delay differential equation.

  9. Design of a data-driven predictive controller for start-up process of AMT vehicles.

    PubMed

    Lu, Xiaohui; Chen, Hong; Wang, Ping; Gao, Bingzhao

    2011-12-01

    In this paper, a data-driven predictive controller is designed for the start-up process of vehicles with automated manual transmissions (AMTs). It is obtained directly from the input-output data of a driveline simulation model constructed by the commercial software AMESim. In order to obtain offset-free control for the reference input, the predictor equation is gained with incremental inputs and outputs. Because of the physical characteristics, the input and output constraints are considered explicitly in the problem formulation. The contradictory requirements of less friction losses and less driveline shock are included in the objective function. The designed controller is tested under nominal conditions and changed conditions. The simulation results show that, during the start-up process, the AMT clutch with the proposed controller works very well, and the process meets the control objectives: fast clutch lockup time, small friction losses, and the preservation of driver comfort, i.e., smooth acceleration of the vehicle. At the same time, the closed-loop system has the ability to reject uncertainties, such as the vehicle mass and road grade.

  10. Hybrid Active/Passive Control of Sound Radiation from Panels with Constrained Layer Damping and Model Predictive Feedback Control

    NASA Technical Reports Server (NTRS)

    Cabell, Randolph H.; Gibbs, Gary P.

    2000-01-01

    make the controller adaptive. For example, a mathematical model of the plant could be periodically updated as the plant changes, and the feedback gains recomputed from the updated model. To be practical, this approach requires a simple plant model that can be updated quickly with reasonable computational requirements. A recent paper by the authors discussed one way to simplify a feedback controller, by reducing the number of actuators and sensors needed for good performance. The work was done on a tensioned aircraft-style panel excited on one side by TBL flow in a low speed wind tunnel. Actuation was provided by a piezoelectric (PZT) actuator mounted on the center of the panel. For sensing, the responses of four accelerometers, positioned to approximate the response of the first radiation mode of the panel, were summed and fed back through the controller. This single input-single output topology was found to have nearly the same noise reduction performance as a controller with fifteen accelerometers and three PZT patches. This paper extends the previous results by looking at how constrained layer damping (CLD) on a panel can be used to enhance the performance of the feedback controller thus providing a more robust and efficient hybrid active/passive system. The eventual goal is to use the CLD to reduce sound radiation at high frequencies, then implement a very simple, reduced order, low sample rate adaptive controller to attenuate sound radiation at low frequencies. Additionally this added damping smoothes phase transitions over the bandwidth which promotes robustness to natural frequency shifts. Experiments were conducted in a transmission loss facility on a clamped-clamped aluminum panel driven on one side by a loudspeaker. A generalized predictive control (GPC) algorithm, which is suited to online adaptation of its parameters, was used in single input-single output and multiple input-single output configurations. Because this was a preliminary look at the potential

  11. Simulation of complex glazing products; from optical data measurements to model based predictive controls

    SciTech Connect

    Kohler, Christian

    2012-04-01

    Complex glazing systems such as venetian blinds, fritted glass and woven shades require more detailed optical and thermal input data for their components than specular non light-redirecting glazing systems. Various methods for measuring these data sets are described in this paper. These data sets are used in multiple simulation tools to model the thermal and optical properties of complex glazing systems. The output from these tools can be used to generate simplified rating values or as an input to other simulation tools such as whole building annual energy programs, or lighting analysis tools. I also describe some of the challenges of creating a rating system for these products and which factors affect this rating. A potential future direction of simulation and building operations is model based predictive controls, where detailed computer models are run in real-time, receiving data for an actual building and providing control input to building elements such as shades.

  12. Iterated non-linear model predictive control based on tubes and contractive constraints.

    PubMed

    Murillo, M; Sánchez, G; Giovanini, L

    2016-05-01

    This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle.

  13. Interior noise control prediction study for high-speed propeller-driven aircraft

    NASA Technical Reports Server (NTRS)

    Rennison, D. C.; Wilby, J. F.; Marsh, A. H.; Wilby, E. G.

    1979-01-01

    An analytical model was developed to predict the noise levels inside propeller-driven aircraft during cruise at M = 0.8. The model was applied to three study aircraft with fuselages of different size (wide body, narrow body and small diameter) in order to determine the noise reductions required to achieve the goal of an A-weighted sound level which does not exceed 80 dB. The model was then used to determine noise control methods which could achieve the required noise reductions. Two classes of noise control treatments were investigated: add-on treatments which can be added to existing structures, and advanced concepts which would require changes to the fuselage primary structure. Only one treatment, a double wall with limp panel, provided the required noise reductions. Weight penalties associated with the treatment were estimated for the three study aircraft.

  14. HIV-1 DNA predicts disease progression and post-treatment virological control

    PubMed Central

    Williams, James P; Hurst, Jacob; Stöhr, Wolfgang; Robinson, Nicola; Brown, Helen; Fisher, Martin; Kinloch, Sabine; Cooper, David; Schechter, Mauro; Tambussi, Giuseppe; Fidler, Sarah; Carrington, Mary; Babiker, Abdel; Weber, Jonathan

    2014-01-01

    In HIV-1 infection, a population of latently infected cells facilitates viral persistence despite antiretroviral therapy (ART). With the aim of identifying individuals in whom ART might induce a period of viraemic control on stopping therapy, we hypothesised that quantification of the pool of latently infected cells in primary HIV-1 infection (PHI) would predict clinical progression and viral replication following ART. We measured HIV-1 DNA in a highly characterised randomised population of individuals with PHI. We explored associations between HIV-1 DNA and immunological and virological markers of clinical progression, including viral rebound in those interrupting therapy. In multivariable analyses, HIV-1 DNA was more predictive of disease progression than plasma viral load and, at treatment interruption, predicted time to plasma virus rebound. HIV-1 DNA may help identify individuals who could safely interrupt ART in future HIV-1 eradication trials. Clinical trial registration: ISRCTN76742797 and EudraCT2004-000446-20 DOI: http://dx.doi.org/10.7554/eLife.03821.001 PMID:25217531

  15. Prediction of forces and moments for hypersonic flight vehicle control effectors

    NASA Technical Reports Server (NTRS)

    Maughmer, Mark D.; Long, Lyle N.; Guilmette, Neal; Pagano, Peter

    1993-01-01

    This research project includes three distinct phases. For completeness, all three phases of the work are briefly described in this report. The goal was to develop methods of predicting flight control forces and moments for hypersonic vehicles which could be used in a preliminary design environment. The first phase included a preliminary assessment of subsonic/supersonic panel methods and hypersonic local flow inclination methods for such predictions. While these findings clearly indicated the usefulness of such methods for conceptual design activities, deficiencies exist in some areas. Thus, a second phase of research was conducted in which a better understanding was sought for the reasons behind the successes and failures of the methods considered, particularly for the cases at hypersonic Mach numbers. This second phase involved using computational fluid dynamics methods to examine the flow fields in detail. Through these detailed predictions, the deficiencies in the simple surface inclination methods were determined. In the third phase of this work, an improvement to the surface inclination methods was developed. This used a novel method for including viscous effects by modifying the geometry to include the viscous/shock layer.

  16. Battery available power prediction of hybrid electric vehicle based on improved Dynamic Matrix Control algorithms

    NASA Astrophysics Data System (ADS)

    Wang, Limei; Cheng, Yong; Zou, Ju

    2014-09-01

    The core technology to any hybrid engine vehicle (HEV) is the design of energy management strategy (EMS). To develop a reasonable EMS, it is necessary to monitor the state of capacity, state of health and instantaneous available power of battery packs. A new method that linearizes RC equivalent circuit model and predicts battery available power according to original Dynamic Matrix Control algorithm is proposed. To verify the validity of the new algorithm, a bench test with lithium-ion battery cell and a HEV test with lithium-ion battery packs are carried out. The bench test results indicate that a single RC block equivalent circuit model could be used to describe the dynamic and the steady state characteristics of a battery under testing conditions. However, lacking of long time constant of RC modules, there is a sample deviation in the open-circuit voltage identified and that measured. The HEV testing results show that the battery voltage predicted is in good agreement with that measured, the maximum difference is within 3.7%. Fixing the time constant to a numeric value, satisfactory results can still be achieved. After setting a battery discharge cut-off voltage, the instantaneous available power of the battery can be predicted.

  17. A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making.

    PubMed

    van der Lee, J H; Svrcek, W Y; Young, B R

    2008-01-01

    Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.

  18. Temporal and spatial controls on variability in water repellency for prediction of runoff from burned watersheds

    NASA Astrophysics Data System (ADS)

    Luce, C.

    2004-12-01

    Prediction of runoff in catchments affected by wildfire is an extreme example of the ungauged basin problem. After wildfire, there is an increase in the risk of severe erosion, floods, and debris flows, often resulting from water repellency. The transient nature of wildfire effects on soils makes the gauging information from affected systems anecdotal in nature; so modeling is required to assimilate the scattered observations and provide for prediction of runoff from newly burnt watersheds. While most water repellency research has focused on the strength of water repellency and its duration for time scales of seconds to seasons measured at the point scale, there is a lack of information on spatial variability of water repellency and the temporal variation of the spatial properties at multi-annual time scales. The lack of spatial information on water repellency both results from and perpetuates a lack of modeling using information on water repellency to predict runoff from burned watersheds. The recently developed FERGI model uses information on the fractional water repellent area to predict runoff from burned hillslopes and catchments. Modeling a catchment requires information to describe the spatial and temporal variations in the fractional water repellent area. Spatial controls on initial water repellency include vegetation density, fire severity, and soil texture, which can be mapped with remote sensing. At finer scales, there is high connectivity of water repellent areas on soils with greater than 70% water repellency; so fractional repellency is adequate information for prediction of runoff for the great majority of storms. In addition, observations on the temporal decay of water repellency show a spatial organization to the water repellency caused by erosion where repellent areas contribute water to non-repellent areas, further supporting use of a fractional repellent area. Measurements of burned areas on different parent materials show that the fractional

  19. Conjunctively optimizing flash flood control and water quality in urban water reservoirs by model predictive control and dynamic emulation

    NASA Astrophysics Data System (ADS)

    Galelli, Stefano; Goedbloed, Albert; Schmitter, Petra; Castelletti, Andrea

    2014-05-01

    Urban water reservoirs are a viable adaptation option to account for increasing drinking water demand of urbanized areas as they allow storage and re-use of water that is normally lost. In addition, the direct availability of freshwater reduces pumping costs and diversifies the portfolios of drinking water supply. Yet, these benefits have an associated twofold cost. Firstly, the presence of large, impervious areas increases the hydraulic efficiency of urban catchments, with short time of concentration, increased runoff rates, losses of infiltration and baseflow, and higher risk of flash floods. Secondly, the high concentration of nutrients and sediments characterizing urban discharges is likely to cause water quality problems. In this study we propose a new control scheme combining Model Predictive Control (MPC), hydro-meteorological forecasts and dynamic model emulation to design real-time operating policies that conjunctively optimize water quantity and quality targets. The main advantage of this scheme stands in its capability of exploiting real-time hydro-meteorological forecasts, which are crucial in such fast-varying systems. In addition, the reduced computational requests of the MPC scheme allows coupling it with dynamic emulators of water quality processes. The approach is demonstrated on Marina Reservoir, a multi-purpose reservoir located in the heart of Singapore and characterized by a large, highly urbanized catchment with a short (i.e. approximately one hour) time of concentration. Results show that the MPC scheme, coupled with a water quality emulator, provides a good compromise between different operating objectives, namely flood risk reduction, drinking water supply and salinity control. Finally, the scheme is used to assess the effect of source control measures (e.g. green roofs) aimed at restoring the natural hydrological regime of Marina Reservoir catchment.

  20. No site unseen: predicting the failure to control problematic Internet use among young adults.

    PubMed

    Yamada, Tetsuhiro; Moshier, Samantha J; Otto, Michael W

    2016-11-01

    Problematic Internet use has been associated with the neglect of valued activities such as work, exercise, social activities, and relationships. In the present study, we expanded the understanding of problematic Internet use by identifying an important predictor of the inability to curb Internet use despite the desire to do so. Specifically, in a college student sample reporting a mean of 27.8 h of recreational Internet use in the past week, we investigated the role of distress intolerance (DI)-an individual difference variable that refers to the inability of an individual to tolerate emotional discomfort and to engage in goal-directed behavior when distressed-to predict the failure to meet personal restrictions on Internet use. Consistent with hypotheses, DI emerged as a significant predictor of the failure to meet self-control goals in both bivariate and multivariate models, indicating that DI offers unique prediction of self-control failure with problematic Internet use. Given that DI is a modifiable trait, these results encourage consideration of DI-focused early intervention strategies.

  1. To predict sufentanil requirement for postoperative pain control using a real-time method

    PubMed Central

    Zhang, Yuhao; Duan, Guangyou; Guo, Shanna; Ying, Ying; Huang, Penghao; Zhang, Mi; Li, Ningbo; Zhang, Xianwei

    2016-01-01

    Abstract Preoperative identification of individual sensitivity to opioid analgesics could improve the quality of postoperative analgesia. We explored the feasibility and utility of a real-time assessment of sufentanil sensitivity in predicting postoperative analgesic requirement. Our primary study included 111 patients who underwent measurements of pressure and quantitative pricking pain thresholds before and 5 minutes after sufentanil infusion. Pain intensity was assessed during the first 24-hour postsurgery, and patients who reported inadequate levels of analgesia were excluded from the study. The sufentanil requirement for patient-controlled analgesia was recorded, and a subsequent exploratory study of 20 patients facilitated the interpretation of the primary study results. In the primary study, experimental pain thresholds increased (P < 0.001) 5 minutes after sufentanil infusion, and the percent change in pricking pain threshold was positively associated with sufentanil requirement at 12 and 24 hours after surgery (β = 0.318, P = 0.001; and β = 0.335, P = 0.001). A receiver-operating characteristic curve analysis showed that patients with a change in pricking pain threshold >188% were >50% likely to require more sufentanil for postoperative pain control. In the exploratory study, experimental pain thresholds significantly decreased after the operation (P < 0.001), and we observed a positive correlation (P < 0.001) between the percent change in pricking pain threshold before and after surgery. Preoperative detection of individual sensitivity to sufentanil via the above described real-time method was effective in predicting postoperative sufentanil requirement. Thus, percent change in pricking pain threshold might be a feasible predictive marker of postoperative analgesia requirement. PMID:27336880

  2. Model Predictive Control of HVAC Systems: Implementation and Testing at the University of California, Merced

    SciTech Connect

    Haves, Phillip; Hencey, Brandon; Borrell, Francesco; Elliot, John; Ma, Yudong; Coffey, Brian; Bengea, Sorin; Wetter, Michael

    2010-06-29

    A Model Predictive Control algorithm was developed for the UC Merced campus chilled water plant. Model predictive control (MPC) is an advanced control technology that has proven successful in the chemical process industry and other industries. The main goal of the research was to demonstrate the practical and commercial viability of MPC for optimization of building energy systems. The control algorithms were developed and implemented in MATLAB, allowing for rapid development, performance, and robustness assessment. The UC Merced chilled water plant includes three water-cooled chillers and a two million gallon chilled water storage tank. The tank is charged during the night to minimize on-peak electricity consumption and take advantage of the lower ambient wet bulb temperature. The control algorithms determined the optimal chilled water plant operation including chilled water supply (CHWS) temperature set-point, condenser water supply (CWS) temperature set-point and the charging start and stop times to minimize a cost function that includes energy consumption and peak electrical demand over a 3-day prediction horizon. A detailed model of the chilled water plant and simplified models of the buildings served by the plant were developed using the equation-based modeling language Modelica. Steady state models of the chillers, cooling towers and pumps were developed, based on manufacturers performance data, and calibrated using measured data collected and archived by the control system. A detailed dynamic model of the chilled water storage tank was also developed and calibrated. Simple, semi-empirical models were developed to predict the temperature and flow rate of the chilled water returning to the plant from the buildings. These models were then combined and simplified for use in a model predictive control algorithm that determines the optimal chiller start and stop times and set-points for the condenser water temperature and the chilled water supply temperature. The

  3. Simulation of the Predictive Control Algorithm for Container Crane Operation using Matlab Fuzzy Logic Tool Box

    NASA Technical Reports Server (NTRS)

    Richardson, Albert O.

    1997-01-01

    This research has investigated the use of fuzzy logic, via the Matlab Fuzzy Logic Tool Box, to design optimized controller systems. The engineering system for which the controller was designed and simulate was the container crane. The fuzzy logic algorithm that was investigated was the 'predictive control' algorithm. The plant dynamics of the container crane is representative of many important systems including robotic arm movements. The container crane that was investigated had a trolley motor and hoist motor. Total distance to be traveled by the trolley was 15 meters. The obstruction height was 5 meters. Crane height was 17.8 meters. Trolley mass was 7500 kilograms. Load mass was 6450 kilograms. Maximum trolley and rope velocities were 1.25 meters per sec. and 0.3 meters per sec., respectively. The fuzzy logic approach allowed the inclusion, in the controller model, of performance indices that are more effectively defined in linguistic terms. These include 'safety' and 'cargo swaying'. Two fuzzy inference systems were implemented using the Matlab simulation package, namely the Mamdani system (which relates fuzzy input variables to fuzzy output variables), and the Sugeno system (which relates fuzzy input variables to crisp output variable). It is found that the Sugeno FIS is better suited to including aspects of those plant dynamics whose mathematical relationships can be determined.

  4. Predictive utility of the attached segment in the quality control of a cord blood graft.

    PubMed

    Rodríguez, Luciano; García, Joan; Querol, Sergi

    2005-04-01

    The limited number of progenitor stem cells in umbilical cord blood (UCB) enforces the optimization and strict control of all the procedures involved in its therapeutic use--ie, collection, processing, cryopreservation, thawing, and transportation--to ensure graft potency at transplantation. For this reason, international UCB standards recommend storage of a cell sample attached to the UCB unit as a quantitative and functional control of the unit selected for transplantation. To validate the use of the sample attached to the UCB unit as a quality-control tool for the final product, UCB units (n = 20) stored in liquid nitrogen with the Bioarchive system were analyzed. The UCB units and their attached segments were thawed, and the number and viability of total nucleated cells, mononucleated cells, CD45 + cells, and CD34+ cells were determined, as were colony-forming cell counts. There was no significant difference between UCB units and segments for any of the parameters assessed. Additionally, the linear correlation coefficient (R2) in these paired samples was 0.85 and 0.78 for CD34+ cells and colony-forming cells, respectively. In conclusion, the cell sample in the tube segment physically linked to the transplant UCB bag predicts the total cell content and functionality of the unit and may serve as a source for final quality control of the UCB unit before transplantation.

  5. Predicting the effects of unmodeled dynamics on an aircraft flight control system design using eigenspace assignment

    NASA Technical Reports Server (NTRS)

    Johnson, Eric N.; Davidson, John B.; Murphy, Patrick C.

    1994-01-01

    When using eigenspace assignment to design an aircraft flight control system, one must first develop a model of the plant. Certain questions arise when creating this model as to which dynamics of the plant need to be included in the model and which dynamics can be left out or approximated. The answers to these questions are important because a poor choice can lead to closed-loop dynamics that are unpredicted by the design model. To alleviate this problem, a method has been developed for predicting the effect of not including certain dynamics in the design model on the final closed-loop eigenspace. This development provides insight as to which characteristics of unmodeled dynamics will ultimately affect the closed-loop rigid-body dynamics. What results from this insight is a guide for eigenstructure control law designers to aid them in determining which dynamics need or do not need to be included and a new way to include these dynamics in the flight control system design model to achieve a required accuracy in the closed-loop rigid-body dynamics. The method is illustrated for a lateral-directional flight control system design using eigenspace assignment for the NASA High Alpha Research Vehicle (HARV).

  6. Ambulatory wireless sensor network power management using constrained explicit generalised predictive control

    NASA Astrophysics Data System (ADS)

    Witheephanich, K.; Escaño, J. M.; Hayes, M. J.

    2011-08-01

    This work considers the problem of controlling transmit power within a wireless sensor network (WSN), where the practical constraints typically posed by an ambulatory healthcare setting are explicitly taken into account, as a constrained received signal strength indicator (RSSI) tracking control problem. The problem is formulated using an explicit generalised predictive control (GPC) strategy for dynamic transmission power control that ensures a balance between energy consumption and quality of service (QoS) through the creation of a stable floor on information throughput. Optimal power assignment is achieved by an explicit solution of the constrained GPC problem that is computed off-line using a multi-parametric quadratic program (mpQP). The solution is shown to be a piecewise-affine function. The new design is demonstrated to be practically feasible via a resource-constrained, fully IEEE 802.15.4 compliant, Moteiv's Tmote Sky sensor node platform. Design utility is benchmarked experimentally using a representative selection of scaled ambulatory scenarios.

  7. A distributed model predictive control (MPC) fault reconfiguration strategy for formation flying satellites

    NASA Astrophysics Data System (ADS)

    Esfahani, N. R.; Khorasani, K.

    2016-05-01

    In this paper, an active distributed (also referred to as semi-decentralised) fault recovery control scheme is proposed that employs inaccurate and unreliable fault information into a model-predictive-control-based design. The objective is to compensate for the identified actuator faults that are subject to uncertainties and detection time delays, in the attitude control subsystems of formation flying satellites. The proposed distributed fault recovery scheme is developed through a two-level hierarchical framework. In the first level, or the agent level, the fault is recovered locally to maintain as much as possible the design specifications, feasibility, and tracking performance of all the agents. In the second level, or the formation level, the recovery is carried out by enhancing the entire team performance. The fault recovery performance of our proposed distributed (semi-decentralised) scheme is compared with two other alternative schemes, namely the centralised and the decentralised fault recovery schemes. It is shown that the distributed (semi-decentralised) fault recovery scheme satisfies the recovery design specifications and also imposes lower fault compensation control effort cost and communication bandwidth requirements as compared to the centralised scheme. Our proposed distributed (semi-decentralised) scheme also outperforms the achievable performance capabilities of the decentralised scheme. Simulation results corresponding to a network of four precision formation flight satellites are also provided to demonstrate and illustrate the advantages of our proposed distributed (semi-decentralised) fault recovery strategy.

  8. Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models

    PubMed Central

    Coelho, Antonio Augusto Rodrigues

    2016-01-01

    This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723

  9. Exploratory Studies in Generalized Predictive Control for Active Gust Load Alleviation

    NASA Technical Reports Server (NTRS)

    Kvaternik, Raymond G.; Eure, Kenneth W.; Juang, Jer-Nan

    2006-01-01

    The results of numerical simulations aimed at assessing the efficacy of Generalized Predictive Control (GPC) for active gust load alleviation using trailing- and leading-edge control surfaces are presented. The equations underlying the method are presented and discussed, including system identification, calculation of control law matrices, and calculation of commands applied to the control effectors. Both embedded and explicit feedforward paths for inclusion of disturbance effects are addressed. Results from two types of simulations are shown. The first used a 3-DOF math model of a mass-spring-dashpot system subject to user-defined external disturbances. The second used open-loop data from a wind-tunnel test in which a wing model was excited by sinusoidal vertical gusts; closed-loop behavior was simulated in post-test calculations. Results obtained from these simulations have been decidedly positive. In particular, results of closed-loop simulations for the wing model showed reductions in root moments by factors as high as 1000, depending on whether the excitation is from a constant- or variable-frequency gust and on the direction of the response.

  10. An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems

    PubMed Central

    Nandola, Naresh N.; Rivera, Daniel E.

    2013-01-01

    We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty. PMID:24348004

  11. Effects of modeling errors on trajectory predictions in air traffic control automation

    NASA Technical Reports Server (NTRS)

    Jackson, Michael R. C.; Zhao, Yiyuan; Slattery, Rhonda

    1996-01-01

    Air traffic control automation synthesizes aircraft trajectories for the generation of advisories. Trajectory computation employs models of aircraft performances and weather conditions. In contrast, actual trajectories are flown in real aircraft under actual conditions. Since synthetic trajectories are used in landing scheduling and conflict probing, it is very important to understand the differences between computed trajectories and actual trajectories. This paper examines the effects of aircraft modeling errors on the accuracy of trajectory predictions in air traffic control automation. Three-dimensional point-mass aircraft equations of motion are assumed to be able to generate actual aircraft flight paths. Modeling errors are described as uncertain parameters or uncertain input functions. Pilot or autopilot feedback actions are expressed as equality constraints to satisfy control objectives. A typical trajectory is defined by a series of flight segments with different control objectives for each flight segment and conditions that define segment transitions. A constrained linearization approach is used to analyze trajectory differences caused by various modeling errors by developing a linear time varying system that describes the trajectory errors, with expressions to transfer the trajectory errors across moving segment transitions. A numerical example is presented for a complete commercial aircraft descent trajectory consisting of several flight segments.

  12. A practical approach to Model Predictive Control (MPC) for solar communities

    NASA Astrophysics Data System (ADS)

    Quintana, Humberto

    Solar district heating (SDH) systems are part of the solution to reduce energy consumption and GHG emissions required for space heating. This kind of installation takes advantage of the convenience of a centralized system and of solar energy to reduce dependency on fossil-fuels. An SDH system is a proven concept that can be enhanced with the addition of long-term thermal energy storage to compensate the seasonal disparity between solar energy supply and heating load demand. These systems are especially deployed in Europe. In Canada, the only SDH installation is the Drake Landing Solar Community (DLSC). This project, which includes seasonal storage (Borehole Thermal Energy Storage-BTES), has been a remarkable success, reaching a solar fraction of 97% by the fifth year of operation. An SDH system cannot be complete without an appropriate supervisory control that coordinates the operation and interaction of system components. The control is based on a set of rules that must consider the system's internal status and external conditions to guarantee occupant comfort with minimal fossil-fuels consumption. This research project is mainly focused on conceiving and assessing new control mechanisms aiming towards an increase of SDH systems' overall energy efficiency. The case study is the DLSC plant, and the proposed control strategies are based on the practical application of Model Predictive Control (MPC) theory. A calibrated model of DLSC including the supervisory control strategies was developed in TRNSYS, building upon the model used for design studies. The model was improved and new components were created when needed. The calibration process delivered a very good agreement for the most important yearly energy performance indices (2 % for solar heat input to the district and for gas consumption, and 5 % for electricity use). Proposed control strategies were conceived for modifying four aspects of the current control: the parameters that define the interaction between

  13. Integrating Predictive Modeling with Control System Design for Managed Aquifer Recharge and Recovery Applications

    NASA Astrophysics Data System (ADS)

    Drumheller, Z. W.; Regnery, J.; Lee, J. H.; Illangasekare, T. H.; Kitanidis, P. K.; Smits, K. M.

    2014-12-01

    Aquifers around the world show troubling signs of irreversible depletion and seawater intrusion as climate change, population growth, and urbanization led to reduced natural recharge rates and overuse. Scientists and engineers have begun to re-investigate the technology of managed aquifer recharge and recovery (MAR) as a means to increase the reliability of the diminishing and increasingly variable groundwater supply. MAR systems offer the possibility of naturally increasing groundwater storage while improving the quality of impaired water used for recharge. Unfortunately, MAR systems remain wrought with operational challenges related to the quality and quantity of recharged and recovered water stemming from a lack of data-driven, real-time control. Our project seeks to ease the operational challenges of MAR facilities through the implementation of active sensor networks, adaptively calibrated flow and transport models, and simulation-based meta-heuristic control optimization methods. The developed system works by continually collecting hydraulic and water quality data from a sensor network embedded within the aquifer. The data is fed into an inversion algorithm, which calibrates the parameters and initial conditions of a predictive flow and transport model. The calibrated model is passed to a meta-heuristic control optimization algorithm (e.g. genetic algorithm) to execute the simulations and determine the best course of action, i.e., the optimal pumping policy for current aquifer conditions. The optimal pumping policy is manually or autonomously applied. During operation, sensor data are used to assess the accuracy of the optimal prediction and augment the pumping strategy as needed. At laboratory-scale, a small (18"H x 46"L) and an intermediate (6'H x 16'L) two-dimensional synthetic aquifer were constructed and outfitted with sensor networks. Data collection and model inversion components were developed and sensor data were validated by analytical measurements.

  14. Multi-model predictive control based on LMI: from the adaptation of the state-space model to the analytic description of the control law

    NASA Astrophysics Data System (ADS)

    Falugi, P.; Olaru, S.; Dumur, D.

    2010-08-01

    This article proposes an explicit robust predictive control solution based on linear matrix inequalities (LMIs). The considered predictive control strategy uses different local descriptions of the system dynamics and uncertainties and thus allows the handling of less conservative input constraints. The computed control law guarantees constraint satisfaction and asymptotic stability. The technique is effective for a class of nonlinear systems embedded into polytopic models. A detailed discussion of the procedures which adapt the partition of the state space is presented. For the practical implementation the construction of suitable (explicit) descriptions of the control law are described upon concrete algorithms.

  15. Zone Model Predictive Control: A Strategy to Minimize Hyper- and Hypoglycemic Events

    PubMed Central

    Grosman, Benyamin; Dassau, Eyal; Zisser, Howard C.; Jovanoviĉ, Lois; Doyle, Francis J.

    2010-01-01

    Background Development of an artificial pancreas based on an automatic closed-loop algorithm that uses a subcutaneous insulin pump and continuous glucose sensor is a goal for biomedical engineering research. However, closing the loop for the artificial pancreas still presents many challenges, including model identification and design of a control algorithm that will keep the type 1 diabetes mellitus subject in normoglycemia for the longest duration and under maximal safety considerations. Method An artificial pancreatic β-cell based on zone model predictive control (zone-MPC) that is tuned automatically has been evaluated on the University of Virginia/University of Padova Food and Drug Administration-accepted metabolic simulator. Zone-MPC is applied when a fixed set point is not defined and the control variable objective can be expressed as a zone. Because euglycemia is usually defined as a range, zone-MPC is a natural control strategy for the artificial pancreatic β-cell. Clinical data usually include discrete information about insulin delivery and meals, which can be used to generate personalized models. It is argued that mapping clinical insulin administration and meal history through two different second-order transfer functions improves the identification accuracy of these models. Moreover, using mapped insulin as an additional state in zone-MPC enriches information about past control moves, thereby reducing the probability of overdosing. In this study, zone-MPC is tested in three different modes using unannounced and announced meals at their nominal value and with 40% uncertainty. Ten adult in silico subjects were evaluated following a scenario of mixed meals with 75, 75, and 50 grams of carbohydrates (CHOs) consumed at 7 am, 1 pm, and 8 pm, respectively. Zone-MPC results are compared to those of the “optimal” open-loop preadjusted treatment. Results Zone-MPC succeeds in maintaining glycemic responses closer to euglycemia compared to the

  16. Predominant Environmental Factors Controlling and Predicting Phenological Seasonality Across the CONUS over the Last Decade

    NASA Astrophysics Data System (ADS)

    Hargrove, W. W.; Kumar, J.; Erguner-Baytok, Y.; Hoffman, F. M.

    2014-12-01

    up to that DOY was developed to predict DOY when a particular Phenological Milestone was achieved within each phenoregion. Resulting maps of R-squared, primary driver, and residuals were coherent and interpretable. The desert southwest "spring" was highly predictable, and was controlled predominantly by accumulated precipitation.

  17. Prediction of extubation outcome: a randomised, controlled trial with automatic tube compensation vs. pressure support ventilation

    PubMed Central

    Cohen, Jonathan; Shapiro, Maury; Grozovski, Elad; Fox, Ben; Lev, Shaul; Singer, Pierre

    2009-01-01

    ATC of 0.80 (p = 0.87). Finally, the ATC-assisted f/VT was found to have a significant contribution in predicting successful liberation and extubation compared with the non-significant contribution of the unassisted f/VT (unassisted f/VT, p = 0.19; ATC-assisted f/VT, p = 0.005). Conclusions This study confirms the usefulness of ATC during the weaning process, being at least as effective as PSV in predicting successful extubation outcome and significantly improving the predictive value of the f/VT. Trial registration Current Controlled Trials ISRCTN16080446 PMID:19236688

  18. Predictive Sea State Estimation for Automated Ride Control and Handling - PSSEARCH

    NASA Technical Reports Server (NTRS)

    Huntsberger, Terrance L.; Howard, Andrew B.; Aghazarian, Hrand; Rankin, Arturo L.

    2012-01-01

    PSSEARCH provides predictive sea state estimation, coupled with closed-loop feedback control for automated ride control. It enables a manned or unmanned watercraft to determine the 3D map and sea state conditions in its vicinity in real time. Adaptive path-planning/ replanning software and a control surface management system will then use this information to choose the best settings and heading relative to the seas for the watercraft. PSSEARCH looks ahead and anticipates potential impact of waves on the boat and is used in a tight control loop to adjust trim tabs, course, and throttle settings. The software uses sensory inputs including IMU (Inertial Measurement Unit), stereo, radar, etc. to determine the sea state and wave conditions (wave height, frequency, wave direction) in the vicinity of a rapidly moving boat. This information can then be used to plot a safe path through the oncoming waves. The main issues in determining a safe path for sea surface navigation are: (1) deriving a 3D map of the surrounding environment, (2) extracting hazards and sea state surface state from the imaging sensors/map, and (3) planning a path and control surface settings that avoid the hazards, accomplish the mission navigation goals, and mitigate crew injuries from excessive heave, pitch, and roll accelerations while taking into account the dynamics of the sea surface state. The first part is solved using a wide baseline stereo system, where 3D structure is determined from two calibrated pairs of visual imagers. Once the 3D map is derived, anything above the sea surface is classified as a potential hazard and a surface analysis gives a static snapshot of the waves. Dynamics of the wave features are obtained from a frequency analysis of motion vectors derived from the orientation of the waves during a sequence of inputs. Fusion of the dynamic wave patterns with the 3D maps and the IMU outputs is used for efficient safe path planning.

  19. A distributed model predictive control based load frequency control scheme for multi-area interconnected power system using discrete-time Laguerre functions.

    PubMed

    Zheng, Yang; Zhou, Jianzhong; Xu, Yanhe; Zhang, Yuncheng; Qian, Zhongdong

    2017-03-23

    This paper proposes a distributed model predictive control based load frequency control (MPC-LFC) scheme to improve control performances in the frequency regulation of power system. In order to reduce the computational burden in the rolling optimization with a sufficiently large prediction horizon, the orthonormal Laguerre functions are utilized to approximate the predicted control trajectory. The closed-loop stability of the proposed MPC scheme is achieved by adding a terminal equality constraint to the online quadratic optimization and taking the cost function as the Lyapunov function. Furthermore, the treatments of some typical constraints in load frequency control have been studied based on the specific Laguerre-based formulations. Simulations have been conducted in two different interconnected power systems to validate the effectiveness of the proposed distributed MPC-LFC as well as its superiority over the comparative methods.

  20. Neural substrates of visuomotor learning based on improved feedback control and prediction.

    PubMed

    Grafton, Scott T; Schmitt, Paul; Van Horn, John; Diedrichsen, Jörn

    2008-02-01

    Motor skills emerge from learning feedforward commands as well as improvements in feedback control. These two components of learning were investigated in a compensatory visuomotor tracking task on a trial-by-trial basis. Between-trial learning was characterized with a state-space model to provide smoothed estimates of feedforward and feedback learning, separable from random fluctuations in motor performance and error. The resultant parameters were correlated with brain activity using magnetic resonance imaging. Learning related to the generation of a feedforward command correlated with activity in dorsal premotor cortex, inferior parietal lobule, supplementary motor area and cingulate motor area, supporting a role of these areas in retrieving and executing a predictive motor command. Modulation of feedback control was associated with activity in bilateral posterior superior parietal lobule as well as right ventral premotor cortex. Performance error correlated with activity in a widespread cortical and subcortical network including bilateral parietal, premotor and rostral anterior cingulate cortex as well as the cerebellar cortex. Finally, trial-by-trial changes of kinematics, as measured by mean absolute hand acceleration, correlated with activity in motor cortex and anterior cerebellum. The results demonstrate that incremental, learning-dependent changes can be modeled on a trial-by-trial basis and neural substrates for feedforward control of novel motor programs are localized to secondary motor areas.

  1. Cognitive task load in a naval ship control centre: from identification to prediction.

    PubMed

    Grootjen, M; Neerincx, M A; Veltman, J A

    Deployment of information and communication technology will lead to further automation of control centre tasks and an increasing amount of information to be processed. A method for establishing adequate levels of cognitive task load for the operators in such complex environments has been developed. It is based on a model distinguishing three load factors: time occupied, task-set switching, and level of information processing. Application of the method resulted in eight scenarios for eight extremes of task load (i.e. low and high values for each load factor). These scenarios were performed by 13 teams in a high-fidelity control centre simulator of the Royal Netherlands Navy. The results show that the method provides good prediction of the task load that will actually appear in the simulator. The model allowed identification of under- and overload situations showing negative effects on operator performance corresponding to controlled experiments in a less realistic task environment. Tools proposed to keep the operator at an optimum task load are (adaptive) task allocation and interface support.

  2. An Optimal Current Observer for Predictive Current Controlled Buck DC-DC Converters

    PubMed Central

    Min, Run; Chen, Chen; Zhang, Xiaodong; Zou, Xuecheng; Tong, Qiaoling; Zhang, Qiao

    2014-01-01

    In digital current mode controlled DC-DC converters, conventional current sensors might not provide isolation at a minimized price, power loss and size. Therefore, a current observer which can be realized based on the digital circuit itself, is a possible substitute. However, the observed current may diverge due to the parasitic resistors and the forward conduction voltage of the diode. Moreover, the divergence of the observed current will cause steady state errors in the output voltage. In this paper, an optimal current observer is proposed. It achieves the highest observation accuracy by compensating for all the known parasitic parameters. By employing the optimal current observer-based predictive current controller, a buck converter is implemented. The converter has a convergently and accurately observed inductor current, and shows preferable transient response than the conventional voltage mode controlled converter. Besides, costs, power loss and size are minimized since the strategy requires no additional hardware for current sensing. The effectiveness of the proposed optimal current observer is demonstrated experimentally. PMID:24854061

  3. Design of a dual-hormone model predictive control for artificial pancreas with exercise model.

    PubMed

    Resalat, Navid; El Youssef, Joseph; Reddy, Ravi; Jacobs, Peter G

    2016-08-01

    The Artificial Pancreas (AP) is a new technology for helping people with type 1 diabetes to better control their glucose levels through automated delivery of insulin and optionally glucagon in response to sensed glucose levels. In a dual hormone AP, insulin and glucagon are delivered automatically to the body based on glucose sensor measurements using a control algorithm that calculates the amount of hormones to be infused. A dual-hormone MPC may deliver insulin continuously; however, it must avoid continuous delivery of glucagon because nausea can occur from too much glucagon. In this paper, we propose a novel dual-hormone (DH) switching model predictive control and compare it with a single-hormone (SH) MPC. We extended both MPCs by integrating an exercise model and compared performance with and without the exercise model included. Results were obtained on a virtual patient population undergoing a simulated exercise event using a mathematical glucoregulatory model that includes exercise. Time spent in hypoglycemia is significantly less with the DH-MPC than the SH-MPC (p=0.0022). Additionally, including the exercise model in the DH-MPC can help prevent hypoglycemia (p <; 0.001).

  4. Predictive factors of death in patients with tuberculosis: a nested case-control study.

    PubMed

    Moosazadeh, M; Nezammahalleh, A; Movahednia, M; Movahednia, N; Khanjani, N; Afshari, M

    2015-06-09

    Tuberculosis is one of the main causes of death worldwide. This study aimed to determine predictive factors for death in patients with tuberculosis to set priorities for public heath interventions to reduce mortality in these patients. This nested case-control study was carried out in Mazandaran province of Islamic Republic of Iran among tuberculosis patients who were treated during 2002-2009. Each deceased patient was individually matched with a control patient according to sex, age, area of involvement and time of follow-up. Potential risk factors for death were evaluated using multivariate conditional logistic regression models. From 2206 patients 376 cases and 376 matched controls were selected. Only positive serology for HIV (OR = 19.1), history of kidney disease (OR = 6.81) and use of immunosuppressant drugs (OR = 3.96) significantly increased the risk of death in tuberculosis patients. These potentially modifiable risk factors could be taken into account in preventive interventions for tuberculosis patients in our country.

  5. Toward a model-based predictive controller design in brain-computer interfaces.

    PubMed

    Kamrunnahar, M; Dias, N S; Schiff, S J

    2011-05-01

    A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.

  6. Projection-free parallel quadratic programming for linear model predictive control

    NASA Astrophysics Data System (ADS)

    Di Cairano, S.; Brand, M.; Bortoff, S. A.

    2013-08-01

    A key component in enabling the application of model predictive control (MPC) in fields such as automotive, aerospace, and factory automation is the availability of low-complexity fast optimisation algorithms to solve the MPC finite horizon optimal control problem in architectures with reduced computational capabilities. In this paper, we introduce a projection-free iterative optimisation algorithm and discuss its application to linear MPC. The algorithm, originally developed by Brand for non-negative quadratic programs, is based on a multiplicative update rule and it is shown to converge to a fixed point which is the optimum. An acceleration technique based on a projection-free line search is also introduced, to speed-up the convergence to the optimum. The algorithm is applied to MPC through the dual of the quadratic program (QP) formulated from the MPC finite time optimal control problem. We discuss how termination conditions with guaranteed degree of suboptimality can be enforced, and how the algorithm performance can be optimised by pre-computing the matrices in a parametric form. We show computational results of the algorithm in three common case studies and we compare such results with the results obtained by other available free and commercial QP solvers.

  7. Electrophysiological Indices of Response Inhibition in a Go/NoGo Task Predict Self-Control in a Social Context

    PubMed Central

    Nash, Kyle; Schiller, Bastian; Gianotti, Lorena R. R.; Baumgartner, Thomas; Knoch, Daria

    2013-01-01

    Recent research demonstrates that response inhibition—a core executive function—may subserve self-regulation and self-control. However, it is unclear whether response inhibition also predicts self-control in the multifaceted, high-level phenomena of social decision-making. Here we examined whether electrophysiological indices of response inhibition would predict self-control in a social context. Electroencephalography was recorded as participants completed a widely used Go/NoGo task (the cued Continuous Performance Test). Participants then interacted with a partner in an economic exchange game that requires self-control. Results demonstrated that greater NoGo-Anteriorization and larger NoGo-P300 peak amplitudes—two established electrophysiological indices of response inhibition—both predicted more self-control in this social game. These findings support continued integration of executive function and self-regulation and help extend prior research into social decision-making processes. PMID:24265773

  8. Poor infant inhibitory control predicts food fussiness in childhood - A possible protective role of n-3 PUFAs for vulnerable children.

    PubMed

    Reis, Roberta Sena; Bernardi, Juliana Rombaldi; Steiner, Meir; Meaney, Michael J; Levitan, Robert D; Silveira, Patrícia Pelufo

    2015-06-01

    Intrauterine growth restriction (IUGR) children are more impulsive towards a sweet reward and have altered feeding behavior in adulthood. We hypothesized that early life inhibitory control predicts feeding behaviors later on in childhood, and the consumption of n-3 PUFAs during infancy may protect IUGR children from developing problematic feeding behaviors. 156 children had information on the Early Childhood Behavior Questionnaire (ECBQ) at 18 months, Food Frequency Questionnaire at 48 months and Children׳s Eating Behavior Questionnaire (CEBQ) at 72 months. There was a significant negative correlation between inhibitory control at 18 months and food fussiness at 72 months. A GLM model predicting food fussiness at 72 months showed significant interaction between n-3 PUFAs, inhibitory control and IUGR, with higher intakes associated with decreased risk for fussiness in IUGR children with poor inhibitory control. Deficits in early inhibitory control predict later food fussiness, and higher intakes of n-3 PUFAs in infancy may protect IUGR children from developing such behavior later.

  9. Robust meta-analytic-predictive priors in clinical trials with historical control information.

    PubMed

    Schmidli, Heinz; Gsteiger, Sandro; Roychoudhury, Satrajit; O'Hagan, Anthony; Spiegelhalter, David; Neuenschwander, Beat

    2014-12-01

    Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior-data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta-analytic-predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta-analytic-predictive prior, which is not available analytically. We propose two- or three-component mixtures of standard priors, which allow for good approximations and, for the one-parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy-tailed and therefore robust. Further robustness and a more rapid reaction to prior-data conflicts can be achieved by adding an extra weakly-informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof-of-concept trial with historical controls from four studies. Robust meta-analytic-predictive priors alleviate prior-data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.

  10. A tide prediction and tide height control system for laboratory mesocosms

    PubMed Central

    Long, Jeremy D.

    2015-01-01

    Experimental mesocosm studies of rocky shore and estuarine intertidal systems may benefit from the application of natural tide cycles to better replicate variation in immersion time, water depth, and attendant fluctuations in abiotic and edaphic conditions. Here we describe a stand-alone microcontroller tide prediction open-source software program, coupled with a mechanical tidal elevation control system, which allows continuous adjustment of aquarium water depths in synchrony with local tide cycles. We used this system to monitor the growth of Spartina foliosa marsh cordgrass and scale insect herbivores at three simulated shore elevations in laboratory mesocosms. Plant growth decreased with increasing shore elevation, while scale insect population growth on the plants was not strongly affected by immersion time. This system shows promise for a range of laboratory mesocosm studies where natural tide cycling could impact organism performance or behavior, while the tide prediction system could additionally be utilized in field experiments where treatments need to be applied at certain stages of the tide cycle. PMID:26623195

  11. A tide prediction and tide height control system for laboratory mesocosms.

    PubMed

    Miller, Luke P; Long, Jeremy D

    2015-01-01

    Experimental mesocosm studies of rocky shore and estuarine intertidal systems may benefit from the application of natural tide cycles to better replicate variation in immersion time, water depth, and attendant fluctuations in abiotic and edaphic conditions. Here we describe a stand-alone microcontroller tide prediction open-source software program, coupled with a mechanical tidal elevation control system, which allows continuous adjustment of aquarium water depths in synchrony with local tide cycles. We used this system to monitor the growth of Spartina foliosa marsh cordgrass and scale insect herbivores at three simulated shore elevations in laboratory mesocosms. Plant growth decreased with increasing shore elevation, while scale insect population growth on the plants was not strongly affected by immersion time. This system shows promise for a range of laboratory mesocosm studies where natural tide cycling could impact organism performance or behavior, while the tide prediction system could additionally be utilized in field experiments where treatments need to be applied at certain stages of the tide cycle.

  12. Automatic generation of predictive dynamic models reveals nuclear phosphorylation as the key Msn2 control mechanism.

    PubMed

    Sunnåker, Mikael; Zamora-Sillero, Elias; Dechant, Reinhard; Ludwig, Christina; Busetto, Alberto Giovanni; Wagner, Andreas; Stelling, Joerg

    2013-05-28

    Predictive dynamical models are critical for the analysis of complex biological systems. However, methods to systematically develop and discriminate among systems biology models are still lacking. We describe a computational method that incorporates all hypothetical mechanisms about the architecture of a biological system into a single model and automatically generates a set of simpler models compatible with observational data. As a proof of principle, we analyzed the dynamic control of the transcription factor Msn2 in Saccharomyces cerevisiae, specifically the short-term mechanisms mediating the cells' recovery after release from starvation stress. Our method determined that 12 of 192 possible models were compatible with available Msn2 localization data. Iterations between model predictions and rationally designed phosphoproteomics and imaging experiments identified a single-circuit topology with a relative probability of 99% among the 192 models. Model analysis revealed that the coupling of dynamic phenomena in Msn2 phosphorylation and transport could lead to efficient stress response signaling by establishing a rate-of-change sensor. Similar principles could apply to mammalian stress response pathways. Systematic construction of dynamic models may yield detailed insight into nonobvious molecular mechanisms.

  13. Distributed model predictive control with hierarchical architecture for communication: application in automated irrigation channels

    NASA Astrophysics Data System (ADS)

    Farhadi, Alireza; Khodabandehlou, Ali

    2016-08-01

    This paper is concerned with a distributed model predictive control (DMPC) method that is based on a distributed optimisation method with two-level architecture for communication. Feasibility (constraints satisfaction by the approximated solution), convergence and optimality of this distributed optimisation method are mathematically proved. For an automated irrigation channel, the satisfactory performance of the proposed DMPC method in attenuation of the undesired upstream transient error propagation and amplification phenomenon is illustrated and compared with the performance of another DMPC method that exploits a single-level architecture for communication. It is illustrated that the DMPC that exploits a two-level architecture for communication has a better performance by better managing communication overhead.

  14. Predicting individual differences in low-income children's executive control from early to middle childhood.

    PubMed

    Cybele Raver, C; McCoy, Dana Charles; Lowenstein, Amy E; Pess, Rachel

    2013-05-01

    The present longitudinal study tested the roles of early childhood executive control (EC) as well as exposure to poverty-related adversity at family and school levels as key predictors of low-income children's EC in elementary school (n = 391). Findings suggest that children's EC difficulties in preschool and lower family income from early to middle childhood are robust predictors of later EC difficulties as rated by teachers in 2nd and 3rd grades. Findings also suggest enrollment in unsafe elementary schools is significantly predictive of higher levels of teacher-rated EC difficulty, but only for those children who showed initially elevated levels of EC difficulty in early childhood. Implications for scientific models of cognitive development and poverty-related adversity are discussed.

  15. Bias-free identification of a linear model-predictive steering controller from measured driver steering behavior.

    PubMed

    Keen, Steven D; Cole, David J

    2012-04-01

    Recent developments in modeling driver steering control with preview are reviewed. While some validation with experimental data has been presented, the rigorous application of formal system identification methods has not yet been attempted. This paper describes a steering controller based on linear model-predictive control. An indirect identification method that minimizes steering angle prediction error is developed. Special attention is given to filtering the prediction error so as to avoid identification bias that arises from the closed-loop operation of the driver-vehicle system. The identification procedure is applied to data collected from 14 test drivers performing double lane change maneuvers in an instrumented vehicle. It is found that the identification procedure successfully finds parameter values for the model that give small prediction errors. The procedure is also able to distinguish between the different steering strategies adopted by the test drivers.

  16. Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory

    SciTech Connect

    Gregor P. Henze; Moncef Krarti

    2005-09-30

    Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building's massive structure or the use of active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this project investigated the merits of harnessing both storage media concurrently in the context of predictive optimal control. To pursue the analysis, modeling, and simulation research of Phase 1, two separate simulation environments were developed. Based on the new dynamic building simulation program EnergyPlus, a utility rate module, two thermal energy storage models were added. Also, a sequential optimization approach to the cost minimization problem using direct search, gradient-based, and dynamic programming methods was incorporated. The objective function was the total utility bill including the cost of reheat and a time-of-use electricity rate either with or without demand charges. An alternative simulation environment based on TRNSYS and Matlab was developed to allow for comparison and cross-validation with EnergyPlus. The initial evaluation of the theoretical potential of the combined optimal control assumed perfect weather prediction and match between the building model and the actual building counterpart. The analysis showed that the combined utilization leads to cost savings that is significantly greater than either storage but less than the sum of the individual savings. The findings reveal that the cooling-related on-peak electrical demand of commercial buildings can be considerably reduced. A subsequent analysis of the impact of forecasting uncertainty in the required short-term weather forecasts determined that it takes only very simple

  17. Implementation of reactive and predictive real-time control strategies to optimize dry stormwater detention ponds

    NASA Astrophysics Data System (ADS)

    Gaborit, Étienne; Anctil, François; Vanrolleghem, Peter A.; Pelletier, Geneviève

    2013-04-01

    Dry detention ponds have been widely implemented in U.S.A (National Research Council, 1993) and Canada (Shammaa et al. 2002) to mitigate the impacts of urban runoff on receiving water bodies. The aim of such structures is to allow a temporary retention of the water during rainfall events, decreasing runoff velocities and volumes (by infiltration in the pond) as well as providing some water quality improvement from sedimentation. The management of dry detention ponds currently relies on static control through a fixed pre-designed limitation of their maximum outflow (Middleton and Barrett 2008), for example via a proper choice of their outlet pipe diameter. Because these ponds are designed for large storms, typically 1- or 2-hour duration rainfall events with return periods comprised between 5 and 100 years, one of their main drawbacks is that they generally offer almost no retention for smaller rainfall events (Middleton and Barrett 2008), which are by definition much more common. Real-Time Control (RTC) has a high potential for optimizing retention time (Marsalek 2005) because it allows adopting operating strategies that are flexible and hence more suitable to the prevailing fluctuating conditions than static control. For dry ponds, this would basically imply adapting the outlet opening percentage to maximize water retention time, while being able to open it completely for severe storms. This study developed several enhanced RTC scenarios of a dry detention pond located at the outlet of a small urban catchment near Québec City, Canada, following the previous work of Muschalla et al. (2009). The catchment's runoff quantity and TSS concentration were simulated by a SWMM5 model with an improved wash-off formulation. The control procedures rely on rainfall detection and measures of the pond's water height for the reactive schemes, and on rainfall forecasts in addition to these variables for the predictive schemes. The automatic reactive control schemes implemented

  18. Predictive control of water distribution in the Dutch National Hydrological Instrument (NHI)

    NASA Astrophysics Data System (ADS)

    Talsma, J.; Patzke, S.; Becker, B. P. J.; Schwanenberg, D.; Jansen, M.

    2012-04-01

    In the Netherlands, water is extracted from rivers, lakes and canals for drinking water supply as well as industrial, agricultural and environmental water demands. These water extractions must be managed in such a way that constraints such as water quality, safety and minimum water levels for navigation are maintained as long as possible. The National Hydrological Instrument (NHI) has been developed for modeling the water distribution in the Netherlands and supporting the development of water management strategies. It is also integrated into the national Dutch forecasting system for predicting dry periods and their impacts on water supply, agriculture, aquatic ecosystems and navigation. With such setup, the NHI will be a fundamental tool for drought forecast in the Netherlands. The NHI consists of a groundwater model (MODFLOW), an unsaturated zone model (Metaswap) and surface water models which interact with each other in every time step via an OpenMI interface. The surface water models consist of a hydrological model MOZART for representing the regional catchments and computing a desired water demand, a SOBEK open channel flow model for flow routing in the network of the larger rivers, lakes and canals, and a real-time control component (RTC-Tools). The latter links the water demand generated by MOZART to the availably supply in the network for generating optimum water allocation policies within the prediction horizon of 10 days of the operational forecasting system. The approach relies on predictive control consisting of a simplified internal model of the network within a system-wide optimization algorithm. In a period of water shortages, the user can refine the water allocation by defining specific objectives and related priorities. Finally, the optimum water extractions from RTC-Tools are passed back to MOZART and SOBEK as allocated values. The RTC-Tools integration into the NHI is an ongoing activity. We present the new functionality based on a pilot system

  19. Salvage brachytherapy in prostate local recurrence after radiation therapy: predicting factors for control and toxicity

    PubMed Central

    2014-01-01

    Purpose To evaluate efficacy and toxicity after salvage brachytherapy (BT) in prostate local recurrence after radiation therapy. Methods and materials Between 1993 and 2007, we retrospectively analyzed 56 consecutively patients (pts) undergoing salvage brachytherapy. After local biopsy-proven recurrence, pts received 145 Gy LDR-BT (37 pts, 66%) or HDR-BT (19 pts, 34%) in different dose levels according to biological equivalent doses (BED2 Gy). By the time of salvage BT, only 15 pts (27%) received ADT. Univariate and multivariate analyses were performed to identify predictors of biochemical control and toxicities. Acute and late genitourinary (GU) and gastrointestinal (GI) toxicities were graded using Common Terminology Criteria for Adverse Events (CTCv3.0). Results Median follow-up after salvage BT was 48 months. The 5-year FFbF was 77%. HDR and LDR late grade 3 GU toxicities were observed in 21% and 24%. Late grade 3 GI toxicities were observed in 2% (HDR) and 2.7% (LDR). On univariate analysis, pre-salvage prostate-specific antigen (PSA) > 10 ng/ml (p = 0.004), interval to relapse after initial treatment < 24 months (p = 0.004) and salvage HDR-BT doses BED2 Gy level < 227 Gy (p = 0.012) were significant in predicting biochemical failure. On Cox multivariate analysis, pre-salvage PSA, and time to relapse were significant in predicting biochemical failure. HDR-BT BED2 Gy (α/β 1.5 Gy) levels ≥ 227 (p = 0.013), and ADT (p = 0.049) were significant in predicting grade ≥ 2 urinary toxicity. Conclusions Prostate BT is an effective salvage modality in some selected prostate local recurrence patients after radiation therapy. Even, we provide some potential predictors of biochemical control and toxicity for prostate salvage BT, further investigation is recommended. PMID:24885287

  20. Health Locus of Control Predicts Free-living, but Not Supervised, Physical Activity: A Test of Exercise-Specific Control and Outcome-Expectancy Hypotheses.

    ERIC Educational Resources Information Center

    Dishman, Rod K.; Steinhardt, Mary

    1990-01-01

    Discusses a study that compared internal health locus of control (IHLOC) with internal exercise locus of control for predicting college students' activity. Results indicate an independent influence of IHLOC on free-living physical activity and suggest that testing adjust for fitness, barriers to physical activity, and outcome-expectancy values.…

  1. Inhibitory Control in Preschool Predicts Early Math Skills in First Grade: Evidence from an Ethnically Diverse Sample

    ERIC Educational Resources Information Center

    Ng, Florrie Fei-Yin; Tamis-LeMonda, Catherine; Yoshikawa, Hirokazu; Sze, Irene Nga-Lam

    2015-01-01

    Preschoolers' inhibitory control and early math skills were concurrently and longitudinally examined in 255 Chinese, African American, Dominican, and Mexican 4-year-olds in the United States. Inhibitory control at age 4, assessed with a peg-tapping task, was associated with early math skills at age 4 and predicted growth in such skills from age 4…

  2. Control and prediction for blackouts caused by frequency collapse in smart grids

    NASA Astrophysics Data System (ADS)

    Wang, Chengwei; Grebogi, Celso; Baptista, Murilo S.

    2016-09-01

    The electric power system is one of the cornerstones of modern society. One of its most serious malfunctions is the blackout, a catastrophic event that may disrupt a substantial portion of the system, playing havoc to human life and causing great economic losses. Thus, understanding the mechanisms leading to blackouts and creating a reliable and resilient power grid has been a major issue, attracting the attention of scientists, engineers, and stakeholders. In this paper, we study the blackout problem in power grids by considering a practical phase-oscillator model. This model allows one to simultaneously consider different types of power sources (e.g., traditional AC power plants and renewable power sources connected by DC/AC inverters) and different types of loads (e.g., consumers connected to distribution networks and consumers directly connected to power plants). We propose two new control strategies based on our model, one for traditional power grids and another one for smart grids. The control strategies show the efficient function of the fast-response energy storage systems in preventing and predicting blackouts in smart grids. This work provides innovative ideas which help us to build up a robuster and more economic smart power system.

  3. A Novel Model Predictive Control Formulation for Hybrid Systems With Application to Adaptive Behavioral Interventions

    PubMed Central

    Nandola, Naresh N.; Rivera, Daniel E.

    2010-01-01

    This paper presents a novel model predictive control (MPC) formulation for linear hybrid systems. The algorithm relies on a multiple-degree-of-freedom formulation that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on move suppression weights as traditionally used in MPC schemes. The formulation is motivated by the need to achieve robust performance in using the algorithm in emerging applications, for instance, as a decision policy for adaptive, time-varying interventions used in behavioral health. The proposed algorithm is demonstrated on a hypothetical adaptive intervention problem inspired by the Fast Track program, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results in the presence of simultaneous disturbances and significant plant-model mismatch are presented. These demonstrate that a hybrid MPC-based approach for this class of interventions can be tuned for desired performance under demanding conditions that resemble participant variability that is experienced in practice when applying an adaptive intervention to a population. PMID:20830213

  4. Internal disinhibition predicts 5‐year weight regain in the National Weight Control Registry (NWCR)

    PubMed Central

    Thomas, J. G.; Niemeier, H.; Wing, R. R.

    2016-01-01

    Summary Background Maintenance of weight loss remains elusive for most individuals. One potential innovative target is internal disinhibition (ID) or the tendency to eat in response to negative thoughts, feelings or physical sensations. Individuals high on ID do worse on average in standard behavioural treatment programmes, and recent studies suggest that disinhibition could play a significant role in weight regain. Purpose The purpose of the current study was to examine whether ID was associated with weight change over 5 years of follow‐up in the National Weight Control Registry, a registry of individuals who have successfully lost weight and maintained it. Methods From the National Weight Control Registry, 5,320 participants were examined across 5 years. Weight data were gathered annually. The disinhibition subscale of the Eating Inventory was used to calculate internal disinhibition and External Disinhibition (ED) and was collected at baseline, year 1, year 3 and year 5. Linear mixed models were used to estimate the weight loss maintained across follow‐up years 1 to 5 using ID and ED as baseline and prospective predictors. Results Internal disinhibition predicted weight regain in all analyses. ED interacted with ID, such that individuals who were high on ID showed greater weight regain if they were also higher on ED. Conclusions The ID scale could be a useful screening measure for risk of weight regain, given its brevity. Improved psychological coping could be a useful target for maintenance or booster interventions. PMID:27812382

  5. Geochemical tracers of dolomitizing fluids: A tool for predicting diagenetically controlled porosity on a reservoir scale

    SciTech Connect

    Major, R.P.; Lucia, F.J.; Ruppel, S.C. )

    1992-04-01

    Mole-per-mole replacement of calcite by dolomite yields an approximately 12% increase in porosity because dolomite is denser than calcite. However, data from the Pliocene-Pleistocene Seroe Domi Formation of Bonaire, Netherlands Antilles, demonstrate the dolomitization of these rocks resulted in porosity reduction. Rocks proximal to the source of dolomitizing fluids exhibit a greater amount of porosity occlusion than more distal rocks, indicating that dolomitization is a porosity-destroying process and that the degree of destruction can be calibrated to the flow path of dolomitizing fludis. Porosity observed in Bonaire dolomite varies over a distance of hundreds of meters along fluid-flow paths. In three examples of dolomites in which the pathways of dolomitizing fluids can be interpreted from the spatial geometry of dolomite compositions, the distances over which these changes occur are pertinent to interpreting diagenetically controlled reservoir heterogeneity in oil and gas fields. These include the Bonaire Dolomite, the Lower Ordovician Ranger Peak Formation of west Texas, and the Permian Clear Fork Formation of west Texas. Tracing dolomitizing fluid pathways may predict diagenetically controlled porosity trends in ancient rocks. Because porosity trends are associated with significant changes in petrophysical characteristics and fluid storage capacity at a between-well scale, this interpretation scheme may aid mapping of flow units in hydrocarbon reservoirs.

  6. Control and prediction for blackouts caused by frequency collapse in smart grids.

    PubMed

    Wang, Chengwei; Grebogi, Celso; Baptista, Murilo S

    2016-09-01

    The electric power system is one of the cornerstones of modern society. One of its most serious malfunctions is the blackout, a catastrophic event that may disrupt a substantial portion of the system, playing havoc to human life and causing great economic losses. Thus, understanding the mechanisms leading to blackouts and creating a reliable and resilient power grid has been a major issue, attracting the attention of scientists, engineers, and stakeholders. In this paper, we study the blackout problem in power grids by considering a practical phase-oscillator model. This model allows one to simultaneously consider different types of power sources (e.g., traditional AC power plants and renewable power sources connected by DC/AC inverters) and different types of loads (e.g., consumers connected to distribution networks and consumers directly connected to power plants). We propose two new control strategies based on our model, one for traditional power grids and another one for smart grids. The control strategies show the efficient function of the fast-response energy storage systems in preventing and predicting blackouts in smart grids. This work provides innovative ideas which help us to build up a robuster and more economic smart power system.

  7. Statistical monitoring and dynamic simulation of a wastewater treatment plant: A combined approach to achieve model predictive control.

    PubMed

    Wang, Xiaodong; Ratnaweera, Harsha; Holm, Johan Abdullah; Olsbu, Vibeke

    2017-02-07

    The on-line monitoring of Chemical oxygen demand (COD) and total phosphorus (TP) restrains wastewater treatment plants to achieve better control of aeration and chemical dosing. In this study, we applied principal components analysis (PCA) to find out significant variables for COD and TP prediction. Multiple regression method applied the variables suggested by PCA to predict influent COD and TP. Moreover, a model of full-scale wastewater treatment plant with moving bed bioreactor (MBBR) and ballasted separation process was developed to simulate the performance of wastewater treatment. The predicted COD and TP data by multiple regression served as model input for dynamic simulation. Besides, the wastewater characteristic of the wastewater treatment plant and MBBR model parameters were given for model calibration. As a result, R(2) of predicted COD and TP versus measured data are 81.6% and 77.2%, respectively. The model output in terms of sludge production and effluent COD based on predicted input data fitted measured data well, which provides possibility to enabled model predictive control of aeration and coagulant dosing in practice. This study provide a feasible and economical approach to overcome monitoring and modelling restrictions that limits model predictive control of wastewater treatment plant.

  8. Application of multi-model switching predictive functional control on the temperature system of an electric heating furnace.

    PubMed

    Xu, Weide; Zhang, Junfeng; Zhang, Ridong

    2017-02-06

    A method of multi-model switching based predictive functional control is proposed and applied to the temperature control system of an electric heating furnace. The control strategies provide the effective and independent control modes of the electric heating furnace temperature in order to obtain improved control performance. The method depends on conventional implementation of the multi-model switching state, which requires some endeavors to tune the switching model in the model predictive control and allows a reduction of the calculation compared with the weighted multiple model algorithms. In order to test the advantage of the proposed method, experimental equipment is set up and experiments are done on the temperature process of a heating furnace, which verify the validity and effectiveness of the proposed algorithm.

  9. Multi-objective optimal design of online PID controllers using model predictive control based on the group method of data handling-type neural networks

    NASA Astrophysics Data System (ADS)

    Majdabadi-Farahani, V.; Hanif, M.; Gholaminezhad, I.; Jamali, A.; Nariman-Zadeh, N.

    2014-10-01

    In this paper, model predictive control (MPC) is used for optimal selection of proportional-integral-derivative (PID) controller gains. In conventional tuning methods a history of response error of the system under control in the passed time is measured and used to adjust PID parameters in order to improve the performance of the system in proceeding time. But MPC obviates this characteristic of classic PID. In fact MPC tries to tune the controller by predicting the system's behaviour some time steps ahead. In this way, PID parameters are adjusted before any real error occurs in the system's response. For this purpose, polynomial meta-models based on the evolved group method of data handling neural networks are obtained to simply simulate the time response of the dynamic system. Moreover, a non-dominated sorting genetic algorithm has been used in a multi-objective Pareto optimisation to select the parameters of the MPC which are prediction horizon, control horizon and relation of weight of Δ u and error, to minimise simultaneously two objective functions that are control effort and integral time absolute error of the system response. The results mentioned at the end obviously declare that the proposed method surpasses conventional tuning methods for PID controllers, and Pareto optimal selection of predictive parameters also improves the performance of the introduced method.

  10. Raoult’s law revisited: accurately predicting equilibrium relative humidity points for humidity control experiments

    PubMed Central

    Bowler, Michael G.

    2017-01-01

    The humidity surrounding a sample is an important variable in scientific experiments. Biological samples in particular require not just a humid atmosphere but often a relative humidity (RH) that is in equilibrium with a stabilizing solution required to maintain the sample in the same state during measurements. The controlled dehydration of macromolecular crystals can lead to significant increases in crystal order, leading to higher diffraction quality. Devices that can accurately control the humidity surrounding crystals while monitoring diffraction have led to this technique being increasingly adopted, as the experiments become easier and more reproducible. Matching the RH to the mother liquor is the first step in allowing the stable mounting of a crystal. In previous work [Wheeler, Russi, Bowler & Bowler (2012). Acta Cryst. F68, 111–114], the equilibrium RHs were measured for a range of concentrations of the most commonly used precipitants in macromolecular crystallography and it was shown how these related to Raoult’s law for the equilibrium vapour pressure of water above a solution. However, a discrepancy between the measured values and those predicted by theory could not be explained. Here, a more precise humidity control device has been used to determine equilibrium RH points. The new results are in agreement with Raoult’s law. A simple argument in statistical mechanics is also presented, demonstrating that the equilibrium vapour pressure of a solvent is proportional to its mole fraction in an ideal solution: Raoult’s law. The same argument can be extended to the case where the solvent and solute molecules are of different sizes, as is the case with polymers. The results provide a framework for the correct maintenance of the RH surrounding a sample. PMID:28381983

  11. Raoult's law revisited: accurately predicting equilibrium relative humidity points for humidity control experiments.

    PubMed

    Bowler, Michael G; Bowler, David R; Bowler, Matthew W

    2017-04-01

    The humidity surrounding a sample is an important variable in scientific experiments. Biological samples in particular require not just a humid atmosphere but often a relative humidity (RH) that is in equilibrium with a stabilizing solution required to maintain the sample in the same state during measurements. The controlled dehydration of macromolecular crystals can lead to significant increases in crystal order, leading to higher diffraction quality. Devices that can accurately control the humidity surrounding crystals while monitoring diffraction have led to this technique being increasingly adopted, as the experiments become easier and more reproducible. Matching the RH to the mother liquor is the first step in allowing the stable mounting of a crystal. In previous work [Wheeler, Russi, Bowler & Bowler (2012). Acta Cryst. F68, 111-114], the equilibrium RHs were measured for a range of concentrations of the most commonly used precipitants in macromolecular crystallography and it was shown how these related to Raoult's law for the equilibrium vapour pressure of water above a solution. However, a discrepancy between the measured values and those predicted by theory could not be explained. Here, a more precise humidity control device has been used to determine equilibrium RH points. The new results are in agreement with Raoult's law. A simple argument in statistical mechanics is also presented, demonstrating that the equilibrium vapour pressure of a solvent is proportional to its mole fraction in an ideal solution: Raoult's law. The same argument can be extended to the case where the solvent and solute molecules are of different sizes, as is the case with polymers. The results provide a framework for the correct maintenance of the RH surrounding a sample.

  12. Interpersonal Problems Predict Differential Response to Cognitive Versus Behavioral Treatment in a Randomized Controlled Trial

    PubMed Central

    Newman, Michelle G.; Jacobson, Nicholas C.; Erickson, Thane M.; Fisher, Aaron J.

    2016-01-01

    Objective We examined dimensional interpersonal problems as moderators of cognitive behavioral therapy (CBT) versus its components (cognitive therapy [CT] and behavioral therapy [BT]). We predicted that people with generalized anxiety disorder (GAD) whose interpersonal problems reflected more dominance and intrusiveness would respond best to a relaxation-based BT compared to CT or CBT, based on studies showing that people with personality features associated with a need for autonomy respond best to treatments that are more experiential, concrete, and self-directed compared to therapies involving abstract analysis of one’s problems (e.g., containing CT). Method This was a secondary analysis of Borkovec, Newman, Pincus, and Lytle (2002). Forty-seven participants with principal diagnoses of GAD were assigned randomly to combined CBT (n = 16), CT (n = 15), or BT (n = 16). Results As predicted, compared to participants with less intrusiveness, those with dimensionally more intrusiveness responded with greater GAD symptom reduction to BT than to CBT at posttreatment and greater change to BT than to CT or CBT across all follow-up points. Similarly, those with more dominance responded better to BT compared to CT and CBT at all follow-up points. Additionally, being overly nurturant at baseline was associated with GAD symptoms at baseline, post, and all follow-up time-points regardless of therapy condition. Conclusions Generally anxious individuals with domineering and intrusive problems associated with higher need for control may respond better to experiential behavioral interventions than to cognitive interventions, which may be perceived as a direct challenge of their perceptions. PMID:28077221

  13. Serious injury prediction algorithm based on large-scale data and under-triage control.

    PubMed

    Nishimoto, Tetsuya; Mukaigawa, Kosuke; Tominaga, Shigeru; Lubbe, Nils; Kiuchi, Toru; Motomura, Tomokazu; Matsumoto, Hisashi

    2017-01-01

    The present study was undertaken to construct an algorithm for an advanced automatic collision notification system based on national traffic accident data compiled by Japanese police. While US research into the development of a serious-injury prediction algorithm is based on a logistic regression algorithm using the National Automotive Sampling System/Crashworthiness Data System, the present injury prediction algorithm was based on comprehensive police data covering all accidents that occurred across Japan. The particular focus of this research is to improve the rescue of injured vehicle occupants in traffic accidents, and the present algorithm assumes the use of an onboard event data recorder data from which risk factors such as pseudo delta-V, vehicle impact location, seatbelt wearing or non-wearing, involvement in a single impact or multiple impact crash and the occupant's age can be derived. As a result, a simple and handy algorithm suited for onboard vehicle installation was constructed from a sample of half of the available police data. The other half of the police data was applied to the validation testing of this new algorithm using receiver operating characteristic analysis. An additional validation was conducted using in-depth investigation of accident injuries in collaboration with prospective host emergency care institutes. The validated algorithm, named the TOYOTA-Nihon University algorithm, proved to be as useful as the US URGENCY and other existing algorithms. Furthermore, an under-triage control analysis found that the present algorithm could achieve an under-triage rate of less than 10% by setting a threshold of 8.3%.

  14. Perceived Academic Control and Academic Emotions Predict Undergraduate University Student Success: Examining Effects on Dropout Intention and Achievement.

    PubMed

    Respondek, Lisa; Seufert, Tina; Stupnisky, Robert; Nett, Ulrike E

    2017-01-01

    The present study addressed concerns over the high risk of university students' academic failure. It examined how perceived academic control and academic emotions predict undergraduate students' academic success, conceptualized as both low dropout intention and high achievement (indicated by GPA). A cross-sectional survey was administered to 883 undergraduate students across all disciplines of a German STEM orientated university. The study additionally compared freshman students (N = 597) vs. second-year students (N = 286). Using structural equation modeling, for the overall sample of undergraduate students we found that perceived academic control positively predicted enjoyment and achievement, as well as negatively predicted boredom and anxiety. The prediction of dropout intention by perceived academic control was fully mediated via anxiety. When taking perceived academic control into account, we found no specific impact of enjoyment or boredom on the intention to dropout and no specific impact of all three academic emotions on achievement. The multi-group analysis showed, however, that perceived academic control, enjoyment, and boredom among second-year students had a direct relationship with dropout intention. A major contribution of the present study was demonstrating the important roles of perceived academic control and anxiety in undergraduate students' academic success. Concerning corresponding institutional support and future research, the results suggested distinguishing incoming from advanced undergraduate students.

  15. Perceived Academic Control and Academic Emotions Predict Undergraduate University Student Success: Examining Effects on Dropout Intention and Achievement

    PubMed Central

    Respondek, Lisa; Seufert, Tina; Stupnisky, Robert; Nett, Ulrike E.

    2017-01-01

    The present study addressed concerns over the high risk of university students' academic failure. It examined how perceived academic control and academic emotions predict undergraduate students' academic success, conceptualized as both low dropout intention and high achievement (indicated by GPA). A cross-sectional survey was administered to 883 undergraduate students across all disciplines of a German STEM orientated university. The study additionally compared freshman students (N = 597) vs. second-year students (N = 286). Using structural equation modeling, for the overall sample of undergraduate students we found that perceived academic control positively predicted enjoyment and achievement, as well as negatively predicted boredom and anxiety. The prediction of dropout intention by perceived academic control was fully mediated via anxiety. When taking perceived academic control into account, we found no specific impact of enjoyment or boredom on the intention to dropout and no specific impact of all three academic emotions on achievement. The multi-group analysis showed, however, that perceived academic control, enjoyment, and boredom among second-year students had a direct relationship with dropout intention. A major contribution of the present study was demonstrating the important roles of perceived academic control and anxiety in undergraduate students' academic success. Concerning corresponding institutional support and future research, the results suggested distinguishing incoming from advanced undergraduate students. PMID:28326043

  16. Reduction of computer usage costs in predicting unsteady aerodynamic loadings caused by control surface motions: Analysis and results

    NASA Technical Reports Server (NTRS)

    Rowe, W. S.; Sebastian, J. D.; Petrarca, J. R.

    1979-01-01

    Results of theoretical and numerical investigations conducted to develop economical computing procedures were applied to an existing computer program that predicts unsteady aerodynamic loadings caused by leading and trailing edge control surface motions in subsonic compressible flow. Large reductions in computing costs were achieved by removing the spanwise singularity of the downwash integrand and evaluating its effect separately in closed form. Additional reductions were obtained by modifying the incremental pressure term that account for downwash singularities at control surface edges. Accuracy of theoretical predictions of unsteady loading at high reduced frequencies was increased by applying new pressure expressions that exactly satisified the high frequency boundary conditions of an oscillating control surface. Comparative computer result indicated that the revised procedures provide more accurate predictions of unsteady loadings as well as providing reduction of 50 to 80 percent in computer usage costs.

  17. Reduction of computer usage costs in predicting unsteady aerodynamic loadings caused by control surface motion. Addendum to computer program description

    NASA Technical Reports Server (NTRS)

    Rowe, W. S.; Petrarca, J. R.

    1980-01-01

    Changes to be made that provide increased accuracy and increased user flexibility in prediction of unsteady loadings caused by control surface motions are described. Analysis flexibility is increased by reducing the restrictions on the location of the downwash stations relative to the leading edge and the edges of the control surface boundaries. Analysis accuracy is increased in predicting unsteady loading for high Mach number analysis conditions through use of additional chordwise downwash stations. User guideline are presented to enlarge analysis capabilities of unusual wing control surface configurations. Comparative results indicate that the revised procedures provide accurate predictions of unsteady loadings as well as providing reductions of 40 to 75 percent in computer usage cost required by previous versions of this program.

  18. Improved PI-PD control design using predictive functional optimization for temperature model of a fluidized catalytic cracking unit.

    PubMed

    Zou, Hongbo; Li, Haisheng

    2017-03-01

    Proportional-integral-derivative (PID) control is widely used in industry because of its simple structure and convenient implementation. However, PID control is suitable for small time delay systems; while if too large delay is encountered, PID control may not obtain the desired performance. Proportional-integral-proportional-derivative (PI-PD) control is a modified of PID control and can get improved control performance; however, due to the complex controller parameter tuning, the PI-PD control is used in a limited scope. Inspired by the advantage of predictive functional control (PFC), a new PI-PD control design using PFC optimization is proposed in this paper. The proposed method not only inherits the advantage of PFC, which does well in coping with the time delay, but also has the same structure as the PI-PD controller. The proposed method is tested on the preheated temperature control of crude oil in a fluidized catalytic cracking unit. The results show that the proposed controller improves control performance compared with typical PID control and PI-PD control.

  19. Improving Computational Efficiency of Model Predictive Control Genetic Algorithms for Real-Time Decision Support

    NASA Astrophysics Data System (ADS)

    Minsker, B. S.; Zimmer, A. L.; Ostfeld, A.; Schmidt, A.

    2014-12-01

    Enabling real-time decision support, particularly under conditions of uncertainty, requires computationally efficient algorithms that can rapidly generate recommendations. In this paper, a suite of model predictive control (MPC) genetic algorithms are developed and tested offline to explore their value for reducing CSOs during real-time use in a deep-tunnel sewer system. MPC approaches include the micro-GA, the probability-based compact GA, and domain-specific GA methods that reduce the number of decision variable values analyzed within the sewer hydraulic model, thus reducing algorithm search space. Minimum fitness and constraint values achieved by all GA approaches, as well as computational times required to reach the minimum values, are compared to large population sizes with long convergence times. Optimization results for a subset of the Chicago combined sewer system indicate that genetic algorithm variations with coarse decision variable representation, eventually transitioning to the entire range of decision variable values, are most efficient at addressing the CSO control problem. Although diversity-enhancing micro-GAs evaluate a larger search space and exhibit shorter convergence times, these representations do not reach minimum fitness and constraint values. The domain-specific GAs prove to be the most efficient and are used to test CSO sensitivity to energy costs, CSO penalties, and pressurization constraint values. The results show that CSO volumes are highly dependent on the tunnel pressurization constraint, with reductions of 13% to 77% possible with less conservative operational strategies. Because current management practices may not account for varying costs at CSO locations and electricity rate changes in the summer and winter, the sensitivity of the results is evaluated for variable seasonal and diurnal CSO penalty costs and electricity-related system maintenance costs, as well as different sluice gate constraint levels. These findings indicate

  20. Is excessive running predictive of degenerative hip disease? Controlled study of former elite athletes.

    PubMed Central

    Marti, B.; Knobloch, M.; Tschopp, A.; Jucker, A.; Howald, H.

    1989-01-01

    OBJECTIVE--To determine the effects of regular long distance running on the state of the hips in later life. DESIGN--Retrospective study of a cohort of elite athletes and a group of normal, healthy, untrained controls examined 15 years after initial testing. SETTING--Research project at school for physical education and sports. SUBJECTS--27 Former long distance runners (mean age 42), nine former bobsleigh riders (mean age 42), and 23 normal, healthy, untrained men (mean age 35) who had been examined in 1973 and who agreed to re-examination in 1988. MAIN OUTCOME MEASURE--Radiological evidence of degenerative hip disease in 1988. RESULTS--Physiological and exercise characteristics of all subjects had been recorded in 1973, and in 1988 these measurements were repeated together with radiological examination of the hips. An additive radiological index of hip disease based on grades of subchondral sclerosis, osteophyte formation, and joint space narrowing was significantly increased among runners as compared with bobsleigh riders and untrained controls. After adjustment for age the significant effect of type of sports activity remained (p = 0.032). In multivariate analyses age and milage run in 1973 (97 km/week) emerged as independent, significant, and positive predictors of radiological signs of degenerative hip disease in 1988 (p = 0.017 and p = 0.024 respectively). Among runners alone running pace in 1973 rather than milage run was the stronger predictor of subsequent degenerative hip disease. The milage run in 1988 was not particularly predictive of the radiological index, but endurance in 1988 was inversely related to degenerative hip disease seen radiologically. CONCLUSION--Long term, high intensity, high milage running should not be dismissed as a potential risk factor for premature osteoarthritis of the hip. PMID:2504343

  1. Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR).

    PubMed

    Hammer, Eva M; Kaufmann, Tobias; Kleih, Sonja C; Blankertz, Benjamin; Kübler, Andrea

    2014-01-01

    Modulation of sensorimotor rhythms (SMR) was suggested as a control signal for brain-computer interfaces (BCI). Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80-100%) performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCI performance from psychological variables that were assessed prior to the BCI sessions and BCI control was supported with machine-learning techniques. We described two significant psychological predictors, namely the visuo-motor coordination ability and the ability to concentrate on the task. The purpose of the current study was to replicate these results thereby validating these predictors within a neurofeedback based SMR-BCI that involved no machine learning.Thirty-three healthy BCI novices participated in a calibration session and three further neurofeedback training sessions. Two variables were related with mean SMR-BCI performance: (1) a measure for the accuracy of fine motor skills, i.e., a trade for a person's visuo-motor control ability; and (2) subject's "attentional impulsivity". In a linear regression they accounted for almost 20% in variance of SMR-BCI performance, but predictor (1) failed significance. Nevertheless, on the basis of our prior regression model for sensorimotor control ability we could predict current SMR-BCI performance with an average prediction error of M = 12.07%. In more than 50% of the participants, the prediction error was smaller than 10%. Hence, psychological variables played a moderate role in predicting SMR-BCI performance in a neurofeedback approach that involved no machine learning. Future studies are needed to further consolidate (or reject) the present predictors.

  2. A predicted and controlled jøkulhlaup (GLOF) from Harbardsbreen, Norway.

    NASA Astrophysics Data System (ADS)

    Jackson, Miriam; Engeset, Rune; Elvehøy, Hallgeir

    2016-04-01

    A jøkulhlaup (GLOF) from Harbardsbreen, Norway in 2015 was predicted and the runoff of over five million cubic metres of water was released in a controlled manner through a hydropower reservoir. The glacier-dammed lake was investigated in early August and showed evidence (change in water-level, radial crevasses) of movement of water from the lake to subglacial storage. The water level in the glacier-dammed lake was over overburden pressure but the event didn't occur until two weeks later. There have been previous events from this glacier, with annual events between 1996 and 2001 due to glacier thinning. The glacier continued to thin substantially (20m) over the next decade but the next event didn't occur until 2010 when over five million cubic metres of water was released as well as additional runoff due to heavy precipitation and melting. At this time the reservoir couldn't accommodate the extra water and there was flow over the dam. Several glaciers in Norway have had one or several jøkulhlaups in the last fifteen years due to glacier thinning, and at several others, glacier-dammed lakes have appeared for the first time. Hydropower reservoirs are situated downstream from some of these glaciers, so such an event can have a beneficial effect. However, negative glacier mass balance and subsequent glacier thinning is increasing the magnitude and frequency of events.

  3. Dynamic behavioural changes in the Spontaneously Hyperactive Rat: 3. Control by reinforcer rate changes and predictability.

    PubMed

    Williams, Jonathan; Sagvolden, Geir; Taylor, Eric; Sagvolden, Terje

    2009-03-17

    Variable intervals are widely believed to produce steady rates of responding. However, based on the calming effect of unpredictability in attention deficit hyperactivity disorder (ADHD) we hypothesised that an animal model of this disorder, the Spontaneously Hyperactive (or Hypertensive) Rat, would become less active following particularly variable sequences of interval-lengths in a variable interval schedule. From a large dataset of holepokes and tray-reports by rats in a variable interval reinforcement schedule, we extracted numerous short sequences of intervals on the basis of the first, second, and third derivatives of reinforcement timing (i.e. rate, acceleration, and jerk) in recent intervals. Sets of selected intervals were compared with one another to elucidate the effect of these different derivatives on behaviour in the current interval. Results show that SHR are more active after richer recent reinforcement; after decelerating reinforcers; and after predictable reinforcers. The hypothesis is supported. In conclusion, SHR behaviour largely complies with the Extended Temporal Difference model which in turn has been previously validated against published data in ADHD. The Extended TD model therefore is able to account for two species' behaviour in a wide range of experimental paradigms. SHR are similar in several respects to group averages of children with ADHD, except that SHR have reduced variability and perform actions faster than controls. Hyperactivity in the SHR is very dependent on momentary environmentally determined states, which is an important area for future investigation of ADHD.

  4. Predicting childhood effortful control from interactions between early parenting quality and children's dopamine transporter gene haplotypes.

    PubMed

    Li, Yi; Sulik, Michael J; Eisenberg, Nancy; Spinrad, Tracy L; Lemery-Chalfant, Kathryn; Stover, Daryn A; Verrelli, Brian C

    2016-02-01

    Children's observed effortful control (EC) at 30, 42, and 54 months (n = 145) was predicted from the interaction between mothers' observed parenting with their 30-month-olds and three variants of the solute carrier family C6, member 3 (SLC6A3) dopamine transporter gene (single nucleotide polymorphisms in intron8 and intron13, and a 40 base pair variable number tandem repeat [VNTR] in the 3'-untranslated region [UTR]), as well as haplotypes of these variants. Significant moderating effects were found. Children without the intron8-A/intron13-G, intron8-A/3'-UTR VNTR-10, or intron13-G/3'-UTR VNTR-10 haplotypes (i.e., haplotypes associated with the reduced SLC6A3 gene expression and thus lower dopamine functioning) appeared to demonstrate altered levels of EC as a function of maternal parenting quality, whereas children with these haplotypes demonstrated a similar EC level regardless of the parenting quality. Children with these haplotypes demonstrated a trade-off, such that they showed higher EC, relative to their counterparts without these haplotypes, when exposed to less supportive maternal parenting. The findings revealed a diathesis-stress pattern and suggested that different SLC6A3 haplotypes, but not single variants, might represent different levels of young children's sensitivity/responsivity to early parenting.

  5. Cognitive trait anxiety, situational stress, and mental effort predict shifting efficiency: Implications for attentional control theory.

    PubMed

    Edwards, Elizabeth J; Edwards, Mark S; Lyvers, Michael

    2015-06-01

    Attentional control theory (ACT) predicts that trait anxiety and situational stress interact to impair performance on tasks that involve attentional shifting. The theory suggests that anxious individuals recruit additional effort to prevent shortfalls in performance effectiveness (accuracy), with deficits becoming evident in processing efficiency (the relationship between accuracy and time taken to perform the task). These assumptions, however, have not been systematically tested. The relationship between cognitive trait anxiety, situational stress, and mental effort in a shifting task (Wisconsin Card Sorting Task) was investigated in 90 participants. Cognitive trait anxiety was operationalized using questionnaire scores, situational stress was manipulated through ego threat instructions, and mental effort was measured using a visual analogue scale. Dependent variables were performance effectiveness (an inverse proportion of perseverative errors) and processing efficiency (an inverse proportion of perseverative errors divided by response time on perseverative error trials). The predictors were not associated with performance effectiveness; however, we observed a significant 3-way interaction on processing efficiency. At higher mental effort (+1 SD), higher cognitive trait anxiety was associated with poorer efficiency independently of situational stress, whereas at lower effort (-1 SD), this relationship was highly significant and most pronounced for those in the high-stress condition. These results are important because they provide the first systematic test of the relationship between trait anxiety, situational stress, and mental effort on shifting performance. The data are also consistent with the notion that effort moderates the relationship between anxiety and shifting efficiency, but not effectiveness.

  6. Cross-layer active predictive congestion control protocol for wireless sensor networks.

    PubMed

    Wan, Jiangwen; Xu, Xiaofeng; Feng, Renjian; Wu, Yinfeng

    2009-01-01

    In wireless sensor networks (WSNs), there are numerous factors that may cause network congestion problems, such as the many-to-one communication modes, mutual interference of wireless links, dynamic changes of network topology and the memory-restrained characteristics of nodes. All these factors result in a network being more vulnerable to congestion. In this paper, a cross-layer active predictive congestion control scheme (CL-APCC) for improving the performance of networks is proposed. Queuing theory is applied in the CL-APCC to analyze data flows of a single-node according to its memory status, combined with the analysis of the average occupied memory size of local networks. It also analyzes the current data change trends of local networks to forecast and actively adjust the sending rate of the node in the next period. In order to ensure the fairness and timeliness of the network, the IEEE 802.11 protocol is revised based on waiting time, the number of the node's neighbors and the original priority of data packets, which dynamically adjusts the sending priority of the node. The performance of CL-APCC, which is evaluated by extensive simulation experiments. is more efficient in solving the congestion in WSNs. Furthermore, it is clear that the proposed scheme has an outstanding advantage in terms of improving the fairness and lifetime of networks.

  7. Assessment of quantitative structure-activity relationship of toxicity prediction models for Korean chemical substance control legislation

    PubMed Central

    Kim, Kwang-Yon; Shin, Seong Eun; No, Kyoung Tai

    2015-01-01

    Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used

  8. Can metabolic control variables of diabetic patients predict their quality of life?

    PubMed

    Dogan, Hakan; Harman, Ece; Kocoglu, Hakan; Sargin, Gokhan

    2016-01-01

    The type and the complexity of regimen aimed at achieving better glycemic control may impact patient's health-related quality of life (HRQoL) in diabetic patients. But, the relationship between HbA1c levels of diabetic patients and their HRQoL is not clear. Our study aims to determine whether metabolic control variables can predict HRQoL or not and also the impact of hypertension (HT) on HRQoL in type II diabetic patients. A total of 469 patients with type II diabetes and 134 control subjects were studied. Medical Outcomes Study Short-Form-General Health Survey (SF-36) questionnaire was used as a health survey tool to measure the QoL of patients in the study. SF-36 includes 8 individual subscales and two summary scales (physical component summary [PCS] and mental component summary [MCS]). Age, gender, fasting blood glucose, postprandial blood glucose, HbA1c, high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), triglyceride, total cholesterol, Apolipoprotein B (apoB), non-HDL-C, and body mass index values of the subjects were recorded. For statistical evaluation, SPSS (Statistical Package for the Social Sciences) 15 under Windows 7 was used. MCS values of patients group were statistically lower than control group (P < .05). There was no significant difference in PCS values between groups (P > .05). Diabetic patients with HT had significantly lower PCS and MCS values than those without HT. In addition, there was a negative correlation between HbA1c level and PCS and MCS values (P < .05). Hypertensive diabetic patients had significantly higher fasting blood glucose, postprandial blood glucose, HbA1c, HDL-C, LDL-C, total cholesterol, and body mass index values than hypertensive control subjects (P < .05). Normotensive diabetic patients also had significantly lower PCS value than normotensive control subjects (P < .05). But, MCS value was not different between groups (P > .05). PCS values in diabetic male patients were significantly

  9. A Temporal Predictive Code for Voice Motor Control: Evidence from ERP and Behavioral Responses to Pitch-shifted Auditory Feedback

    PubMed Central

    Behroozmand, Roozbeh; Sangtian, Stacey; Korzyukov, Oleg; Larson, Charles R.

    2016-01-01

    The predictive coding model suggests that voice motor control is regulated by a process in which the mismatch (error) between feedforward predictions and sensory feedback is detected and used to correct vocal motor behavior. In this study, we investigated how predictions about timing of pitch perturbations in voice auditory feedback would modulate ERP and behavioral responses during vocal production. We designed six counterbalanced blocks in which a +100 cents pitch-shift stimulus perturbed voice auditory feedback during vowel sound vocalizations. In three blocks, there was a fixed delay (500, 750 or 1000 ms) between voice and pitch-shift stimulus onset (predictable), whereas in the other three blocks, stimulus onset delay was randomized between 500, 750 and 1000 ms (unpredictable). We found that subjects produced compensatory (opposing) vocal responses that started at 80 ms after the onset of the unpredictable stimuli. However, for predictable stimuli, subjects initiated vocal responses at 20 ms before and followed the direction of pitch shifts in voice feedback. Analysis of ERPs showed that the amplitudes of the N1 and P2 components were significantly reduced in response to predictable compared with unpredictable stimuli. These findings indicate that predictions about temporal features of sensory feedback can modulate vocal motor behavior. In the context of the predictive coding model, temporally-predictable stimuli are learned and reinforced by the internal feedforward system, and as indexed by the ERP suppression, the sensory feedback contribution is reduced for their processing. These findings provide new insights into the neural mechanisms of vocal production and motor control. PMID:26835556

  10. A temporal predictive code for voice motor control: Evidence from ERP and behavioral responses to pitch-shifted auditory feedback.

    PubMed

    Behroozmand, Roozbeh; Sangtian, Stacey; Korzyukov, Oleg; Larson, Charles R

    2016-04-01

    The predictive coding model suggests that voice motor control is regulated by a process in which the mismatch (error) between feedforward predictions and sensory feedback is detected and used to correct vocal motor behavior. In this study, we investigated how predictions about timing of pitch perturbations in voice auditory feedback would modulate ERP and behavioral responses during vocal production. We designed six counterbalanced blocks in which a +100 cents pitch-shift stimulus perturbed voice auditory feedback during vowel sound vocalizations. In three blocks, there was a fixed delay (500, 750 or 1000 ms) between voice and pitch-shift stimulus onset (predictable), whereas in the other three blocks, stimulus onset delay was randomized between 500, 750 and 1000 ms (unpredictable). We found that subjects produced compensatory (opposing) vocal responses that started at 80 ms after the onset of the unpredictable stimuli. However, for predictable stimuli, subjects initiated vocal responses at 20 ms before and followed the direction of pitch shifts in voice feedback. Analysis of ERPs showed that the amplitudes of the N1 and P2 components were significantly reduced in response to predictable compared with unpredictable stimuli. These findings indicate that predictions about temporal features of sensory feedback can modulate vocal motor behavior. In the context of the predictive coding model, temporally-predictable stimuli are learned and reinforced by the internal feedforward system, and as indexed by the ERP suppression, the sensory feedback contribution is reduced for their processing. These findings provide new insights into the neural mechanisms of vocal production and motor control.

  11. Prediction and uncertainty in associative learning: examining controlled and automatic components of learned attentional biases.

    PubMed

    Luque, David; Vadillo, Miguel A; Le Pelley, Mike E; Beesley, Tom

    2017-08-01

    It has been suggested that attention is guided by two factors that operate during associative learning: a predictiveness principle, by which attention is allocated to the best predictors of outcomes, and an uncertainty principle, by which attention is allocated to learn about the less known features of the environment. Recent studies have shown that predictiveness-driven attention can operate rapidly and in an automatic way to exploit known relationships. The corresponding characteristics of uncertainty-driven attention, on the other hand, remain unexplored. In two experiments we examined whether both predictiveness and uncertainty modulate attentional processing in an adaptation of the dot probe task. This task provides a measure of automatic orientation to cues during associative learning. The stimulus onset asynchrony of the probe display was manipulated in order to explore temporal characteristics of predictiveness- and uncertainty-driven attentional effects. Results showed that the predictive status of cues determined selective attention, with faster attentional capture to predictive than to non-predictive cues. In contrast, the level of uncertainty slowed down responses to the probe regardless of the predictive status of the cues. Both predictiveness- and uncertainty-driven attentional effects were very rapid (at 250 ms from cue onset) and were automatically activated.

  12. Implementation of PFC (Predictive Functional Control) in a PLC (Programmable Logic Controller) for a HVAC (Heating, Ventilation and Air Conditioning) system

    NASA Astrophysics Data System (ADS)

    Kreutz, M.; Richalet, J.; Mocha, K.; Haber, R.

    2014-12-01

    HVAC systems of industrial buildings consume a lot of energy. Therefore it is important to know the performance of these systems and strategies to optimize the hardware and the control. Tackling the temperature control of the HVAC system promises quick savings by tuning the control within specified tolerance limits, which mostly can be done by low investment. This paper mainly deals with the implementation strategy of a new controller in a PLC using the predictive functional control for temperature control. The different stages of the implementation from the simulation over the SCL code till to the real-time operation are presented. A bumpless switch between the PI(D) and the PFC control was realized, as well.

  13. Encoderless Model Predictive Control of Doubly-Fed Induction Generators in Variable-Speed Wind Turbine Systems

    NASA Astrophysics Data System (ADS)

    Abdelrahem, Mohamed; Hackl, Christoph; Kennel, Ralph

    2016-09-01

    In this paper, an encoderless finite-control-set model predictive control (FCS-MPC) strategy for doubly-fed induction generators (DFIGs) based on variable-speed wind turbine systems (WTSs) is proposed. According to the FCS-MPC concept, the discrete states of the power converter are taken into account and the future converter performance is predicted for each sampling period. Subsequently, the voltage vector that minimizes a predefined cost function is selected to be applied in the next sampling instant. Furthermore, a model reference adaptive system (MRAS) observer is used to estimate the rotor speed and position of the DFIG. Estimation and control performance of the proposed encoderless control method are validated by simulation results for all operation conditions. Moreover, the performance of the MRAS observer is tested under variations of the DFIG parameters.

  14. Use of Plant Hydraulic Theory to Predict Ecosystem Fluxes Across Mountainous Gradients in Environmental Controls and Insect Disturbances

    NASA Astrophysics Data System (ADS)

    Ewers, B. E.; Pendall, E.; Reed, D. E.; Barnard, H. R.; Whitehouse, F.; Frank, J. M.; Massman, W. J.; Brooks, P. D.; Biederman, J. A.; Harpold, A. A.; Naithani, K. J.; Mitra, B.; Mackay, D. S.; Norton, U.; Borkhuu, B.

    2011-12-01

    While mountainous areas are critical for providing numerous ecosystem benefits at the regional scale, the strong gradients in environmental controls make predictions difficult. A key part of the problem is quantifying and predicting the feedback between mountain gradients and plant function which then controls ecosystem cycling. The emerging theory of plant hydraulics provides a rigorous yet simple platform from which to generate testable hypotheses and predictions of ecosystem pools and fluxes. Plant hydraulic theory predicts that plant controls over carbon, water, energy and nutrient fluxes can be derived from the limitation of plant water transport from the soil through xylem and out of stomata. In addition, the limit to plant water transport can be predicted by combining plant structure (e.g. xylem diameters or root-to-shoot ratios) and plant function (response of stomatal conductance to vapor pressure deficit or root vulnerability to cavitation). We evaluate the predictions of the plant hydraulic theory by testing it against data from a mountain gradient encompassing sagebrush steppe through subalpine forests (2700 to 3400 m). We further test the theory by predicting the carbon, water and nutrient exchanges from several coniferous trees in the same gradient that are dying from xylem dysfunction caused by blue-stain fungi carried by bark beetles. The common theme of both of these data sets is a change in water limitation caused by either changing precipitation along the mountainous gradient or lack of access to soil water from xylem-occluding fungi. Across all of the data sets which range in scale from individual plants to hillslopes, the data fit the predictions of plant hydraulic theory. Namely, there was a proportional tradeoff between the reference canopy stomatal conductance to water vapor and the sensitivity of that conductance to vapor pressure deficit that quantitatively fits the predictions of plant hydraulic theory. Incorporating this result into

  15. Effects of ozone prediction accuracy and choice of chemical mechanism on NMHC control requirements as calculated using EKMA. [Nonmethane hydrocarbon control

    SciTech Connect

    Montz, A.C.

    1984-07-01

    Two issues important in the evaluation of results obtained when using the Emprical Kinetic Modeling Approach (EKMA) to determine nonmethane hydrocarbon (NMHC) control requirements are addressed. As used here NMHC is considered synonymous with reactive volatile organic compounds (RVOC). The first issue is the effect that the accuracy of the ozone prediction has on the calculation of NMHC emission reduction requirements. The second issue is the effect that the use of various chemical mechanisms have on the calculation of NMHC emission reduction requirements. Control requirements calculated with different mechanisms have different sensitivities to NMHC emissions and to other sources of hydrocarbons. EKMA diagrams generated using the computer code OZIPP are used to examine the effects of accuracy and chemical mechanism selection. The conclusions that may be drawn from the study include: comparing two calculations of control requirements for the same case, the analysis with the lower peak ozone value prediction will trend to have the lower control requirement; the more sensitive ozone is to NMHC changes, as described by the chemical mechanism, the lower the control requirements will tend to be; the two tendencies identified may be additive or compensating; and in the cases examined, use of the Carbon Bond mechanism results in predictions of lower control requirements.

  16. Dicer deficiency reveals microRNAs predicted to control gene expression in the developing adrenal cortex.

    PubMed

    Krill, Kenneth T; Gurdziel, Katherine; Heaton, Joanne H; Simon, Derek P; Hammer, Gary D

    2013-05-01

    MicroRNAs (miRNAs) are small, endogenous, non-protein-coding RNAs that are an important means of posttranscriptional gene regulation. Deletion of Dicer, a key miRNA processing enzyme, is embryonic lethal in mice, and tissue-specific Dicer deletion results in developmental defects. Using a conditional knockout model, we generated mice lacking Dicer in the adrenal cortex. These Dicer-knockout (KO) mice exhibited perinatal mortality and failure of the adrenal cortex during late gestation between embryonic day 16.5 (E16.5) and E18.5. Further study of Dicer-KO adrenals demonstrated a significant loss of steroidogenic factor 1-expressing cortical cells that was histologically evident as early as E16.5 coincident with an increase in p21 and cleaved-caspase 3 staining in the cortex. However, peripheral cortical proliferation persisted in KO adrenals as assessed by staining of proliferating cell nuclear antigen. To further characterize the embryonic adrenals from Dicer-KO mice, we performed microarray analyses for both gene and miRNA expression on purified RNA isolated from control and KO adrenals of E15.5 and E16.5 embryos. Consistent with the absence of Dicer and the associated loss of miRNA-mediated mRNA degradation, we observed an up-regulation of a small subset of adrenal transcripts in Dicer-KO mice, most notably the transcripts coded by the genes Nr6a1 and Acvr1c. Indeed, several miRNAs, including let-7, miR-34c, and miR-21, that are predicted to target these genes for degradation, were also markedly down-regulated in Dicer-KO adrenals. Together these data suggest a role for miRNA-mediated regulation of a subset of genes that are essential for normal adrenal growth and homeostasis.

  17. Positron Emission Tomography (PET) Evaluation After Initial Chemotherapy and Radiation Therapy Predicts Local Control in Rhabdomyosarcoma

    SciTech Connect

    Dharmarajan, Kavita V.; Wexler, Leonard H.; Gavane, Somali; Fox, Josef J.; Schoder, Heiko; Tom, Ashlyn K.; Price, Alison N.; Meyers, Paul A.; Wolden, Suzanne L.

    2012-11-15

    Purpose: 18-fluorodeoxyglucose positron emission tomography (PET) is already an integral part of staging in rhabdomyosarcoma. We investigated whether primary-site treatment response characterized by serial PET imaging at specific time points can be correlated with local control. Patients and Methods: We retrospectively examined 94 patients with rhabdomyosarcoma who received initial chemotherapy 15 weeks (median) before radiotherapy and underwent baseline, preradiation, and postradiation PET. Baseline PET standardized uptake values (SUVmax) and the presence or absence of abnormal uptake (termed PET-positive or PET-negative) both before and after radiation were examined for the primary site. Local relapse-free survival (LRFS) was calculated according to baseline SUVmax, PET-positive status, and PET-negative status by the Kaplan-Meier method, and comparisons were tested with the log-rank test. Results: The median patient age was 11 years. With 3-year median follow-up, LRFS was improved among postradiation PET-negative vs PET-positive patients: 94% vs 75%, P=.02. By contrast, on baseline PET, LRFS was not significantly different for primary-site SUVmax {<=}7 vs >7 (median), although the findings suggested a trend toward improved LRFS: 96% for SUVmax {<=}7 vs 79% for SUVmax >7, P=.08. Preradiation PET also suggested a statistically insignificant trend toward improved LRFS for PET-negative (97%) vs PET-positive (81%) patients (P=.06). Conclusion: Negative postradiation PET predicted improved LRFS. Notably, 77% of patients with persistent postradiation uptake did not experience local failure, suggesting that these patients could be closely followed up rather than immediately referred for intervention. Negative baseline and preradiation PET findings suggested statistically insignificant trends toward improved LRFS. Additional study may further understanding of relationships between PET findings at these time points and outcome in rhabdomyosarcoma.

  18. Novel Genetic Analysis for Case-Control Genome-Wide Association Studies: Quantification of Power and Genomic Prediction Accuracy

    PubMed Central

    Lee, Sang Hong; Wray, Naomi R.

    2013-01-01

    Genome-wide association studies (GWAS) are routinely conducted for both quantitative and binary (disease) traits. We present two analytical tools for use in the experimental design of GWAS. Firstly, we present power calculations quantifying power in a unified framework for a range of scenarios. In this context we consider the utility of quantitative scores (e.g. endophenotypes) that may be available on cases only or both cases and controls. Secondly, we consider, the accuracy of prediction of genetic risk from genome-wide SNPs and derive an expression for genomic prediction accuracy using a liability threshold model for disease traits in a case-control design. The expected values based on our derived equations for both power and prediction accuracy agree well with observed estimates from simulations. PMID:23977056

  19. On the incremental validity of irrational beliefs to predict subjective well-being while controlling for personality factors.

    PubMed

    Spörrle, Matthias; Strobel, Maria; Tumasjan, Andranik

    2010-11-01

    This research examines the incremental validity of irrational thinking as conceptualized by Albert Ellis to predict diverse aspects of subjective well-being while controlling for the influence of personality factors. Rational-emotive behavior therapy (REBT) argues that irrational beliefs result in maladaptive emotions leading to reduced well-being. Although there is some early scientific evidence for this relation, it has never been investigated whether this connection would still persist when statistically controlling for the Big Five personality factors, which were consistently found to be important determinants of well-being. Regression analyses revealed significant incremental validity of irrationality over personality factors when predicting life satisfaction, but not when predicting subjective happiness. Results are discussed with respect to conceptual differences between these two aspects of subjective well-being.

  20. Semiparametric methods for evaluating the covariate-specific predictiveness of continuous markers in matched case-control studies

    PubMed Central

    Huang, Y.; Pepe, M. S.

    2010-01-01

    Summary To assess the value of a continuous marker in predicting the risk of a disease, a graphical tool called the predictiveness curve has been proposed. It characterizes the marker’s predictiveness, or capacity to risk stratify the population by displaying the distribution of risk endowed by the marker. Methods for making inference about the curve and for comparing curves in a general population have been developed. However, knowledge about a marker’s performance in the general population only is not enough. Since a marker’s effect on the risk model and its distribution can both differ across subpopulations, its predictiveness may vary when applied to different subpopulations. Moreover, information about the predictiveness of a marker conditional on baseline covariates is valuable for individual decision making about having the marker measured or not. Therefore, to fully realize the usefulness of a risk prediction marker, it is important to study its performance conditional on covariates. In this article, we propose semiparametric methods for estimating covariate-specific predictiveness curves for a continuous marker. Unmatched and matched case-control study designs are accommodated. We illustrate application of the methodology by evaluating serum creatinine as a predictor of risk of renal artery stenosis. PMID:21562626

  1. Image Discrimination Predictions of a Single Channel Model with Contrast Gain Control

    NASA Technical Reports Server (NTRS)

    Ahumada, Albert J., Jr.; Null, Cynthia H.

    1995-01-01

    Image discrimination models predict the number of just-noticeable-differences between two images. We report the predictions of a single channel model with contrast masking for a range of standard discrimination experiments. Despite its computational simplicity, this model has performed as well as a multiple channel model in an object detection task.

  2. Intentions and Trait Self-Control Predict Fruit and Vegetable Consumption during the Transition to First-Year University

    ERIC Educational Resources Information Center

    Tomasone, Jennifer R.; Meikle, Natasha; Bray, Steven R.

    2015-01-01

    Objective: To examine the independent and combined effects of Theory of Planned Behavior (TPB) variables and trait self-control (TSC) in the prediction of fruit and vegetable consumption (FVC) among first-year university students. Participants: Seventy-six first-year undergraduate university students. Methods: In their first week of class…

  3. Predicted and flight test results of the performance, stability and control of the space shuttle from reentry to landing

    NASA Technical Reports Server (NTRS)

    Kirsten, P. W.; Richardson, D. F.; Wilson, C. M.

    1983-01-01

    Aerodynaic performance, stability and control data obtained from the first five reentries of the Space Shuttle orbiter are given. Flight results are compared to pedicted data from Mach 26.4 to Mach 0.4. Differences between flight and predicted data as well as probable causes for the discrepancies are given.

  4. Predicting Participation in Group Parenting Education in an Australian Sample: The Role of Attitudes, Norms, and Control Factors

    ERIC Educational Resources Information Center

    White, Katherine M.; Wellington, Larne

    2009-01-01

    We examined the theory of planned behavior (TPB) in predicting intentions to participate in group parenting education. One hundred and seventy-six parents (138 mothers and 38 fathers) with a child under 12 years completed TPB items assessing attitude, subjective norms, perceived behavioral control (PBC), and two additional social influence…

  5. Model Predictive Control techniques with application to photovoltaic, DC Microgrid, and a multi-sourced hybrid energy system

    NASA Astrophysics Data System (ADS)

    Shadmand, Mohammad Bagher

    Renewable energy sources continue to gain popularity. However, two major limitations exist that prevent widespread adoption: availability and variability of the electricity generated and the cost of the equipment. The focus of this dissertation is Model Predictive Control (MPC) for optimal sized photovoltaic (PV), DC Microgrid, and multi-sourced hybrid energy systems. The main considered applications are: maximum power point tracking (MPPT) by MPC, droop predictive control of DC microgrid, MPC of grid-interaction inverter, MPC of a capacitor-less VAR compensator based on matrix converter (MC). This dissertation firstly investigates a multi-objective optimization technique for a hybrid distribution system. The variability of a high-penetration PV scenario is also studied when incorporated into the microgrid concept. Emerging (PV) technologies have enabled the creation of contoured and conformal PV surfaces; the effect of using non-planar PV modules on variability is also analyzed. The proposed predictive control to achieve maximum power point for isolated and grid-tied PV systems speeds up the control loop since it predicts error before the switching signal is applied to the converter. The low conversion efficiency of PV cells means we want to ensure always operating at maximum possible power point to make the system economical. Thus the proposed MPPT technique can capture more energy compared to the conventional MPPT techniques from same amount of installed solar panel. Because of the MPPT requirement, the output voltage of the converter may vary. Therefore a droop control is needed to feed multiple arrays of photovoltaic systems to a DC bus in microgrid community. Development of a droop control technique by means of predictive control is another application of this dissertation. Reactive power, denoted as Volt Ampere Reactive (VAR), has several undesirable consequences on AC power system network such as reduction in power transfer capability and increase in

  6. A double-loop structure in the adaptive generalized predictive control algorithm for control of robot end-point contact force.

    PubMed

    Wen, Shuhuan; Zhu, Jinghai; Li, Xiaoli; Chen, Shengyong

    2014-09-01

    Robot force control is an essential issue in robotic intelligence. There is much high uncertainty when robot end-effector contacts with the environment. Because of the environment stiffness effects on the system of the robot end-effector contact with environment, the adaptive generalized predictive control algorithm based on quantitative feedback theory is designed for robot end-point contact force system. The controller of the internal loop is designed on the foundation of QFT to control the uncertainty of the system. An adaptive GPC algorithm is used to design external loop controller to improve the performance and the robustness of the system. Two closed loops used in the design approach realize the system׳s performance and improve the robustness. The simulation results show that the algorithm of the robot end-effector contacting force control system is effective.

  7. Use of uncertainty polytope to describe constraint processes with uncertain time-delay for robust model predictive control applications.

    PubMed

    Huang, Gongsheng; Wang, Shengwei

    2009-10-01

    This paper studies the application of robust model predictive control (MPC) in a constraint process suffering from time-delay uncertainty. The process is described using a transfer function and sampled into a discrete model for computer control design. A polytope is firstly developed to describe the uncertain discrete model due to the process's time-delay uncertainty. Based on the proposed description, a linear matrix inequality (LMI) based MPC algorithm is employed and modified to design a robust controller for such a constraint process. In case studies, the effect of time-delay uncertainty on the control performance of a standard MPC algorithm is investigated, and the proposed description and the modified control algorithm are validated in the temperature control of a typical air-handling unit.

  8. Adjoint Method and Predictive Control for 1-D Flow in NASA Ames 11-Foot Transonic Wind Tunnel

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan; Ardema, Mark

    2006-01-01

    This paper describes a modeling method and a new optimal control approach to investigate a Mach number control problem for the NASA Ames 11-Foot Transonic Wind Tunnel. The flow in the wind tunnel is modeled by the 1-D unsteady Euler equations whose boundary conditions prescribe a controlling action by a compressor. The boundary control inputs to the compressor are in turn controlled by a drive motor system and an inlet guide vane system whose dynamics are modeled by ordinary differential equations. The resulting Euler equations are thus coupled to the ordinary differential equations via the boundary conditions. Optimality conditions are established by an adjoint method and are used to develop a model predictive linear-quadratic optimal control for regulating the Mach number due to a test model disturbance during a continuous pitch

  9. Poor Self-Control and Harsh Punishment in Childhood Prospectively Predict Borderline Personality Symptoms in Adolescent Girls

    PubMed Central

    Hallquist, Michael N.; Hipwell, Alison E.; Stepp, Stephanie D.

    2015-01-01

    Developmental theories of borderline personality disorder (BPD) propose that harsh, invalidating parenting of a child with poor self-control and heightened negative emotionality often leads to a coercive cycle of parent-child transactions that increase risk for BPD symptoms such as emotion dysregulation. Although parenting practices and child temperament have previously been linked with BPD, less is known about the prospective influences of caregiver and child characteristics. Using annual longitudinal data from the Pittsburgh Girls Study (n = 2450), our study examined how reciprocal influences among harsh parenting, self-control, and negative emotionality between ages 5 and 14 predicted the development of BPD symptoms in adolescent girls ages 14 to 17. Consistent with developmental theories, we found that harsh punishment, poor self-control, and negative emotionality predicted BPD symptom severity at age 14. Only worsening self-control between ages 12 and 14, however, predicted growth in BPD symptoms from 14 to 17. Furthermore, the effects of harsh punishment and poor self-control on age 14 BPD symptoms were partially mediated by their earlier reciprocal effects on each other between ages 5 and 14. Our findings underscore the need to address both child and parental contributions to dysfunctional transactions in order to stem the development of BPD symptoms. Moreover, problems with self-regulation in early adolescence may indicate heightened risk for subsequent BPD. Altogether, these results increase our understanding of developmental trajectories associated with BPD symptoms in adolescent girls. PMID:25961815

  10. Poor self-control and harsh punishment in childhood prospectively predict borderline personality symptoms in adolescent girls.

    PubMed

    Hallquist, Michael N; Hipwell, Alison E; Stepp, Stephanie D

    2015-08-01

    Developmental theories of borderline personality disorder (BPD) propose that harsh, invalidating parenting of a child with poor self-control and heightened negative emotionality often leads to a coercive cycle of parent-child transactions that increase risk for BPD symptoms such as emotion dysregulation. Although parenting practices and child temperament have previously been linked with BPD, less is known about the prospective influences of caregiver and child characteristics. Using annual longitudinal data from the Pittsburgh Girls Study (n = 2,450), our study examined how reciprocal influences among harsh parenting, self-control, and negative emotionality between ages 5 and 14 predicted the development of BPD symptoms in adolescent girls ages 14 to 17. Consistent with developmental theories, we found that harsh punishment, poor self-control, and negative emotionality predicted BPD symptom severity at age 14. Only worsening self-control between ages 12 and 14, however, predicted growth in BPD symptoms from 14 to 17. Furthermore, the effects of harsh punishment and poor self-control on age 14 BPD symptoms were partially mediated by their earlier reciprocal effects on each other between ages 5 and 14. Our findings underscore the need to address both child and parental contributions to dysfunctional transactions in order to stem the development of BPD symptoms. Moreover, problems with self-regulation in early adolescence may indicate heightened risk for subsequent BPD. Altogether, these results increase our understanding of developmental trajectories associated with BPD symptoms in adolescent girls.

  11. Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function.

    PubMed

    Gilzenrat, Mark S; Nieuwenhuis, Sander; Jepma, Marieke; Cohen, Jonathan D

    2010-05-01

    An important dimension of cognitive control is the adaptive regulation of the balance between exploitation (pursuing known sources of reward) and exploration (seeking new ones) in response to changes in task utility. Recent studies have suggested that the locus coeruleus-norepinephrine system may play an important role in this function and that pupil diameter can be used to index locus coeruleus activity. On the basis of this, we reasoned that pupil diameter may correlate closely with control state and associated changes in behavior. Specifically, we predicted that increases in baseline pupil diameter would be associated with decreases in task utility and disengagement from the task (exploration), whereas reduced baseline diameter (but increases in task-evoked dilations) would be associated with task engagement (exploitation). Findings in three experiments were consistent with these predictions, suggesting that pupillometry may be useful as an index of both control state and, indirectly, locus coeruleus function.

  12. The minimum requirements of language control: evidence from sequential predictability effects in language switching.

    PubMed

    Declerck, Mathieu; Koch, Iring; Philipp, Andrea M

    2015-03-01

    The current study systematically examined the influence of sequential predictability of languages and concepts on language switching. To this end, 2 language switching paradigms were combined. To measure language switching with a random sequence of languages and/or concepts, we used a language switching paradigm that implements visual cues and stimuli. The other paradigm implements a fixed sequence of languages and/or concepts to measure predictable language switching. Four experiments that used these 2 paradigms showed that switch costs were smaller when both the language and concept were predictably known, whereas no overall switch cost reduction was found when just the language or concept was predictable. These results indicate that knowing both language and concept (i.e., response) can resolve language interference. However, interference resolution does not start solely based on the knowledge of which concept or language one has to produce. We discuss how existent models should be revised to accommodate these results.

  13. Predictive Models and Tools for Assessing Chemicals under the Toxic Substances Control Act (TSCA)

    EPA Pesticide Factsheets

    EPA has developed databases and predictive models to help evaluate the hazard, exposure, and risk of chemicals released to the environment and how workers, the general public, and the environment may be exposed to and affected by them.

  14. An Experimental Evaluation of Generalized Predictive Control for Tiltrotor Aeroelastic Stability Augmentation in Airplane Mode of Flight

    NASA Technical Reports Server (NTRS)

    Kvaternik, Raymond G.; Piatak, David J.; Nixon, Mark W.; Langston, Chester W.; Singleton, Jeffrey D.; Bennett, Richard L.; Brown, Ross K.

    2001-01-01

    The results of a joint NASA/Army/Bell Helicopter Textron wind-tunnel test to assess the potential of Generalized Predictive Control (GPC) for actively controlling the swashplate of tiltrotor aircraft to enhance aeroelastic stability in the airplane mode of flight are presented. GPC is an adaptive time-domain predictive control method that uses a linear difference equation to describe the input-output relationship of the system and to design the controller. The test was conducted in the Langley Transonic Dynamics Tunnel using an unpowered 1/5-scale semispan aeroelastic model of the V-22 that was modified to incorporate a GPC-based multi-input multi-output control algorithm to individually control each of the three swashplate actuators. Wing responses were used for feedback. The GPC-based control system was highly effective in increasing the stability of the critical wing mode for all of the conditions tested, without measurable degradation of the damping in the other modes. The algorithm was also robust with respect to its performance in adjusting to rapid changes in both the rotor speed and the tunnel airspeed.

  15. Optimal Predictive Control for Path Following of a Full Drive-by-Wire Vehicle at Varying Speeds

    NASA Astrophysics Data System (ADS)

    SONG, Pan; GAO, Bolin; XIE, Shugang; FANG, Rui

    2017-03-01

    The current research of the global chassis control problem for the full drive-by-wire vehicle focuses on the control allocation (CA) of the four-wheel-distributed traction/braking/steering systems. However, the path following performance and the handling stability of the vehicle can be enhanced a step further by automatically adjusting the vehicle speed to the optimal value. The optimal solution for the combined longitudinal and lateral motion control (MC) problem is given. First, a new variable step-size spatial transformation method is proposed and utilized in the prediction model to derive the dynamics of the vehicle with respect to the road, such that the tracking errors can be explicitly obtained over the prediction horizon at varying speeds. Second, a nonlinear model predictive control (NMPC) algorithm is introduced to handle the nonlinear coupling between any two directions of the vehicular planar motion and computes the sequence of the optimal motion states for following the desired path. Third, a hierarchical control structure is proposed to separate the motion controller into a NMPC based path planner and a terminal sliding mode control (TSMC) based path follower. As revealed through off-line simulations, the hierarchical methodology brings nearly 1700% improvement in computational efficiency without loss of control performance. Finally, the control algorithm is verified through a hardware in-the-loop simulation system. Double-lane-change (DLC) test results show that by using the optimal predictive controller, the root-mean-square (RMS) values of the lateral deviations and the orientation errors can be reduced by 41% and 30%, respectively, comparing to those by the optimal preview acceleration (OPA) driver model with the non-preview speed-tracking method. Additionally, the average vehicle speed is increased by 0.26 km/h with the peak sideslip angle suppressed to 1.9°. This research proposes a novel motion controller, which provides the full drive

  16. Individual differences in brainstem and basal ganglia structure predict postural control and balance loss in young and older adults.

    PubMed

    Boisgontier, Matthieu P; Cheval, Boris; Chalavi, Sima; van Ruitenbeek, Peter; Leunissen, Inge; Levin, Oron; Nieuwboer, Alice; Swinnen, Stephan P

    2017-02-01

    It remains unclear which specific brain regions are the most critical for human postural control and balance, and whether they mediate the effect of age. Here, associations between postural performance and corticosubcortical brain regions were examined in young and older adults using multiple structural imaging and linear mixed models. Results showed that of the regions involved in posture, the brainstem was the strongest predictor of postural control and balance: lower brainstem volume predicted larger center of pressure deviation and higher odds of balance loss. Analyses of white and gray matter in the brainstem showed that the pedunculopontine nucleus area appeared to be critical for postural control in both young and older adults. In addition, the brainstem mediated the effect of age on postural control, underscoring the brainstem's fundamental role in aging. Conversely, lower basal ganglia volume predicted better postural performance, suggesting an association between greater neural resources in the basal ganglia and greater movement vigor, resulting in exaggerated postural adjustments. Finally, results showed that practice, shorter height and heavier weight (i.e., higher body mass index), higher total physical activity, and larger ankle active (but not passive) range of motion were predictive of more stable posture, irrespective of age.

  17. Self-control Predicts Exercise Behavior by Force of Habit, a Conceptual Replication of Adriaanse et al. (2014)

    PubMed Central

    Gillebaart, Marleen; Adriaanse, Marieke A.

    2017-01-01

    A recent study suggests that habits play a mediating role in the association between trait self-control and eating behavior, supporting a notion of effortless processes in trait self-control (Adriaanse et al., 2014). We conceptually replicated this research in the area of exercise behavior, hypothesizing that these associations would generalize to other self-control related behaviors. Sufficient exercise is essential for several health and well-being outcomes, and therefore many people intend to exercise. However, the majority of the population does not actually exercise to a sufficient or intended extent, due to competing temptations and short-term goals. This conflict makes exercise a typical example of a self-control dilemma. A within-subjects survey study was conducted to test associations between trait self-control, habit strength, and exercise behavior. Participants were recruited at a local gym. Results demonstrated that trait self-control predicted exercise behavior. Mediation analysis revealed that the association between self-control and exercise was mediated by stronger exercise habits, replicating findings by Adriaanse et al. (2014). These results highlight the relevance of self-control in the domain of exercise. In addition, they add to a growing body of knowledge on the underlying mechanisms of trait self-control on behavior that point to habit—rather than effortful impulse inhibition—as a potential key to self-control success. PMID:28243217

  18. Demand response-enabled model predictive HVAC load control in buildings using real-time electricity pricing

    NASA Astrophysics Data System (ADS)

    Avci, Mesut

    A practical cost and energy efficient model predictive control (MPC) strategy is proposed for HVAC load control under dynamic real-time electricity pricing. The MPC strategy is built based on a proposed model that jointly minimizes the total energy consumption and hence, cost of electricity for the user, and the deviation of the inside temperature from the consumer's preference. An algorithm that assigns temperature set-points (reference temperatures) to price ranges based on the consumer's discomfort tolerance index is developed. A practical parameter prediction model is also designed for mapping between the HVAC load and the inside temperature. The prediction model and the produced temperature set-points are integrated as inputs into the MPC controller, which is then used to generate signal actions for the AC unit. To investigate and demonstrate the effectiveness of the proposed approach, a simulation based experimental analysis is presented using real-life pricing data. An actual prototype for the proposed HVAC load control strategy is then built and a series of prototype experiments are conducted similar to the simulation studies. The experiments reveal that the MPC strategy can lead to significant reductions in overall energy consumption and cost savings for the consumer. Results suggest that by providing an efficient response strategy for the consumers, the proposed MPC strategy can enable the utility providers to adopt efficient demand management policies using real-time pricing. Finally, a cost-benefit analysis is performed to display the economic feasibility of implementing such a controller as part of a building energy management system, and the payback period is identified considering cost of prototype build and cost savings to help the adoption of this controller in the building HVAC control industry.

  19. Model predictive direct power control for active power decoupled single-phase quasi-Z -source inverter

    SciTech Connect

    Liu, Yushan; Ge, Baoming; Abu-Rub, Haitham; Sun, Hexu; Peng, Fang Zheng; Xue, Yaosuo

    2016-06-14

    In this study, the active power filter (APF) that consists of a half-bridge leg and an ac capacitor is integrated in the single-phase quasi-Z-source inverter (qZSI) in this paper to avoid the second harmonic power flowing into the dc side. The capacitor of APF buffers the second harmonic power of the load, and the ac capacitor allows highly pulsating ac voltage, so that the capacitances of both dc and ac sides can be small. A model predictive direct power control (DPC) is further proposed to achieve the purpose of this newtopology through predicting the capacitor voltage of APF at each sampling period and ensuring the APF power to track the second harmonic power of single-phase qZSI. Simulation and experimental results verify the model predictive DPC for the APF-integrated single-phase qZSI.

  20. Model predictive direct power control for active power decoupled single-phase quasi-Z -source inverter

    DOE PAGES

    Liu, Yushan; Ge, Baoming; Abu-Rub, Haitham; ...

    2016-06-14

    In this study, the active power filter (APF) that consists of a half-bridge leg and an ac capacitor is integrated in the single-phase quasi-Z-source inverter (qZSI) in this paper to avoid the second harmonic power flowing into the dc side. The capacitor of APF buffers the second harmonic power of the load, and the ac capacitor allows highly pulsating ac voltage, so that the capacitances of both dc and ac sides can be small. A model predictive direct power control (DPC) is further proposed to achieve the purpose of this newtopology through predicting the capacitor voltage of APF at eachmore » sampling period and ensuring the APF power to track the second harmonic power of single-phase qZSI. Simulation and experimental results verify the model predictive DPC for the APF-integrated single-phase qZSI.« less

  1. A study of the prediction of cruise noise and laminar flow control noise criteria for subsonic air transports

    NASA Technical Reports Server (NTRS)

    Swift, G.; Mungur, P.

    1979-01-01

    General procedures for the prediction of component noise levels incident upon airframe surfaces during cruise are developed. Contributing noise sources are those associated with the propulsion system, the airframe and the laminar flow control (LFC) system. Transformation procedures from the best prediction base of each noise source to the transonic cruise condition are established. Two approaches to LFC/acoustic criteria are developed. The first is a semi-empirical extension of the X-21 LFC/acoustic criteria to include sensitivity to the spectrum and directionality of the sound field. In the second, the more fundamental problem of how sound excites boundary layer disturbances is analyzed by deriving and solving an inhomogeneous Orr-Sommerfeld equation in which the source terms are proportional to the production and dissipation of sound induced fluctuating vorticity. Numerical solutions are obtained and compared with corresponding measurements. Recommendations are made to improve and validate both the cruise noise prediction methods and the LFC/acoustic criteria.

  2. Prediction of carbamazepine in sewage treatment plant effluents and its implications for control strategies of pharmaceutical aquatic contamination.

    PubMed

    Zhang, Yongjun; Geissen, Sven-Uwe

    2010-09-01

    Carbamazepine (CBZ) is one of the most frequently detected pharmaceutical active compounds (PhACs) in water bodies. In this study, its concentrations in the STP effluents in 68 countries/regions are predicted with a model based on the sale volume, the water consumption, the disposal rate, the excretion rate, and the removal efficiency by the sewage system. The prediction results demonstrate a global aquatic contamination of CBZ. However, the contamination is unbalanced: most of developed economics have a concentration above 500 ng L(-1). The prediction model also provides some implications on the control strategies of pharmaceutical aquatic contamination, including upgrading STPs, urine separation, waste pharmaceuticals collection, environmentally labeling pharmaceuticals, and green pharmacy. Those strategies are discussed in the context of currently available literature information.

  3. Nomogram to predict the number of oocytes retrieved in controlled ovarian stimulation

    PubMed Central

    Moon, Kyoung Yong; Kim, Hoon; Lee, Joong Yeup; Lee, Jung Ryeol; Jee, Byung Chul; Suh, Chang Suk; Kim, Ki Chul; Lee, Won Don; Lim, Jin Ho

    2016-01-01

    Objective Ovarian reserve tests are commonly used to predict ovarian response in infertile patients undergoing ovarian stimulation. Although serum markers such as basal follicle-stimulating hormone (FSH) or random anti-Müllerian hormone (AMH) level and ultrasonographic markers (antral follicle count, AFC) are good predictors, no single test has proven to be the best predictor. In this study, we developed appropriate equations and novel nomograms to predict the number of oocytes that will be retrieved using patients' age, serum levels of basal FSH and AMH, and AFC. Methods We analyzed a database containing clinical and laboratory information of 141 stimulated in vitro fertilization (IVF) cycles performed at a university-based hospital between September 2009 and December 2013. We used generalized linear models for prediction of the number of oocytes. Results Age, basal serum FSH level, serum AMH level, and AFC were significantly related to the number of oocytes retrieved according to the univariate and multivariate analyses. The equations that predicted the number of oocytes retrieved (log scale) were as follows: model (1) 3.21–0.036×(age)+0.089×(AMH), model (2) 3.422–0.03×(age)–0.049×(FSH)+0.08×(AMH), model (3) 2.32–0.017×(age)+0.039×(AMH)+0. 03×(AFC), model (4) 2.584–0.015×(age)–0.035×(FSH)+0.038×(AMH)+0.026×(AFC). model 4 showed the best performance. On the basis of these variables, we developed nomograms to predict the number of oocytes that can be retrieved. Conclusion Our nomograms helped predict the number of oocytes retrieved in stimulated IVF cycles. PMID:27358830

  4. Adaptive predictive control using neural network for a class of pure-feedback systems in discrete time.

    PubMed

    Ge, Shuzhi Sam; Yang, Chenguang; Lee, Tong Heng

    2008-09-01

    In this paper, adaptive neural network (NN) control is investigated for a class of nonlinear pure-feedback discrete-time systems. By using prediction functions of future states, the pure-feedback system is transformed into an n-step-ahead predictor, based on which state feedback NN control is synthesized. Next, by investigating the relationship between outputs and states, the system is transformed into an input-output predictor model, and then, output feedback control is constructed. To overcome the difficulty of nonaffine appearance of the control input, implicit function theorem is exploited in the control design and NN is employed to approximate the unknown function in the control. In both state feedback and output feedback control, only a single NN is used and the controller singularity is completely avoided. The closed-loop system achieves semiglobal uniform ultimate boundedness (SGUUB) stability and the output tracking error is made within a neighborhood around zero. Simulation results are presented to show the effectiveness of the proposed control approach.

  5. Application of Multivariable Model Predictive Advanced Control for a 2×310T/H CFB Boiler Unit

    NASA Astrophysics Data System (ADS)

    Weijie, Zhao; Zongllao, Dai; Rong, Gou; Wengan, Gong

    When a CFB boiler is in automatic control, there are strong interactions between various process variables and inverse response characteristics of bed temperature control target. Conventional Pill control strategy cannot deliver satisfactory control demand. Kalman wave filter technology is used to establish a non-linear combustion model, based on the CFB combustion characteristics of bed fuel inventory, heating values, bed lime inventory and consumption. CFB advanced combustion control utilizes multivariable model predictive control technology to optimize primary and secondary air flow, bed temperature, air flow, fuel flow and heat flux. In addition to providing advanced combustion control to 2×310t/h CFB+1×100MW extraction condensing turbine generator unit, the control also provides load allocation optimization and advanced control for main steam pressure, combustion and temperature. After the successful implementation, under 10% load change, main steam pressure varied less than ±0.07MPa, temperature less than ±1°C, bed temperature less than ±4°C, and air flow (O2) less than ±0.4%.

  6. The Brief Self-Control Scale Predicts Jail Inmates’ Recidivism, Substance Dependence, and Post-Release Adjustment

    PubMed Central

    2015-01-01

    Previous research finds that self-control is positively associated with adaptive and negatively associated with maladaptive behavior. However, most previous studies employ cross-sectional designs, low-risk samples, and limited assessments of self-control. This study of 553 jail inmates examined the relationship of a valid measure of self-control (Brief Self-Control Scale; BSCS) completed upon incarceration with behavior before, during, and one year after incarceration. After controlling for positive impression management (PIM), self-control was negatively related to substance misuse, suicidality, risky sex, and criminal history prior to incarceration and post-release illegal substance misuse, recidivism, and positive adjustment. Lower self-control predicted increases in substance dependence at post-release compared to pre-incarceration. Self-control was not related to misbehavior during incarceration, nor alcohol use or HIV-risk behavior one year post-release. Results were consistent as a function of age, race, and gender. This study supports self-control as an important risk and protective factor in a sample of criminal offenders. PMID:24345712

  7. Sensor-model prediction, monitoring and in-situ control of liquid RTM advanced fiber architecture composite processing

    NASA Technical Reports Server (NTRS)

    Kranbuehl, D.; Kingsley, P.; Hart, S.; Loos, A.; Hasko, G.; Dexter, B.

    1992-01-01

    In-situ frequency dependent electromagnetic sensors (FDEMS) and the Loos resin transfer model have been used to select and control the processing properties of an epoxy resin during liquid pressure RTM impregnation and cure. Once correlated with viscosity and degree of cure the FDEMS sensor monitors and the RTM processing model predicts the reaction advancement of the resin, viscosity and the impregnation of the fabric. This provides a direct means for predicting, monitoring, and controlling the liquid RTM process in-situ in the mold throughout the fabrication process and the effects of time, temperature, vacuum and pressure. Most importantly, the FDEMS-sensor model system has been developed to make intelligent decisions, thereby automating the liquid RTM process and removing the need for operator direction.

  8. Correlating observed odds ratios from lung cancer case-control studies to SNP functional scores predicted by bioinformatic tools

    PubMed Central

    Zhu, Yong; Hoffman, Aaron; Wu, Xifeng; Zhang, Heping; Zhang, Yawei; Leaderer, Derek; Zheng, Tongzhang

    2008-01-01

    Bioinformatic tools are widely utilized to predict functional single nucleotide polymorphisms (SNPs) for genotyping in molecular epidemiological studies. However, the extent to which these approaches are mirrored by epidemiological findings has not been fully explored. In this study, we first surveyed SNPs examined in case-control studies of lung cancer, the most extensively-studied cancer type. We then computed SNP functional scores using four popular bioinformatics tools: SIFT, PolyPhen, SNPs3D, and PMut, and determined their predictive potential using the odds ratios (ORs) reported. Spearman’s correlation coefficient (r) for the association with SNP score from SIFT, PolyPhen, SNPs3D, and PMut, and the summary ORs were r = −0.36 (p = 0.007), r = 0.25 (p = 0.068), r = −0.20 (p = 0.205), and r = −0.12 (p = 0.370) respectively. By creating a combined score using information from all four tools we were able to achieve a correlation coefficient of r = 0.51 (p < 0.001). These results indicate that scores of predicted functionality could explain a certain fraction of the lung cancer risk detected in genetic association studies and more accurate predictions may be obtained by combining information from a variety of tools. Our findings suggest that bioinformatic tools are useful in predicting SNP functionality and may facilitate future genetic epidemiological studies. PMID:18191955

  9. Markov Model Predicts Changes in STH Prevalence during Control Activities Even with a Reduced Amount of Baseline Information

    PubMed Central

    Montresor, Antonio; Deol, Arminder; à Porta, Natacha; Lethanh, Nam; Jankovic, Dina

    2016-01-01

    Background Estimating the reduction in levels of infection during implementation of soil-transmitted helminth (STH) control programmes is important to measure their performance and to plan interventions. Markov modelling techniques have been used with some success to predict changes in STH prevalence following treatment in Viet Nam. The model is stationary and to date, the prediction has been obtained by calculating the transition probabilities between the different classes of intensity following the first year of drug distribution and assuming that these remain constant in subsequent years. However, to run this model longitudinal parasitological data (including intensity of infection) are required for two consecutive years from at least 200 individuals. Since this amount of data is not often available from STH control programmes, the possible application of the model in control programme is limited. The present study aimed to address this issue by adapting the existing Markov model to allow its application when a more limited amount of data is available and to test the predictive capacities of these simplified models. Method We analysed data from field studies conducted with different combination of three parameters: (i) the frequency of drug administration; (ii) the drug distributed; and (iii) the target treatment population (entire population or school-aged children only). This analysis allowed us to define 10 sets of standard transition probabilities to be used to predict prevalence changes when only baseline data are available (simplified model 1). We also formulated three equations (one for each STH parasite) to calculate the predicted prevalence of the different classes of intensity from the total prevalence. These equations allowed us to design a simplified model (SM2) to obtain predictions when the classes of intensity at baseline were not known. To evaluate the performance of the simplified models, we collected data from the scientific literature on

  10. The Minimum Requirements of Language Control: Evidence from Sequential Predictability Effects in Language Switching

    ERIC Educational Resources Information Center

    Declerck, Mathieu; Koch, Iring; Philipp, Andrea M.

    2015-01-01

    The current study systematically examined the influence of sequential predictability of languages and concepts on language switching. To this end, 2 language switching paradigms were combined. To measure language switching with a random sequence of languages and/or concepts, we used a language switching paradigm that implements visual cues and…

  11. Individual differences in self-reported self-control predict successful emotion regulation.

    PubMed

    Paschke, Lena M; Dörfel, Denise; Steimke, Rosa; Trempler, Ima; Magrabi, Amadeus; Ludwig, Vera U; Schubert, Torsten; Stelzel, Christine; Walter, Henrik

    2016-08-01

    Both self-control and emotion regulation enable individuals to adapt to external circumstances and social contexts, and both are assumed to rely on the overlapping neural resources. Here, we tested whether high self-reported self-control is related to successful emotion regulation on the behavioral and neural level. One hundred eight participants completed three self-control questionnaires and regulated their negative emotions during functional magnetic resonance imaging using reappraisal (distancing). Trait self-control correlated positively with successful emotion regulation both subjectively and neurally, as indicated by online ratings of negative emotions and functional connectivity strength between the amygdala and prefrontal areas, respectively. This stronger overall connectivity of the left amygdala was related to more successful subjective emotion regulation. Comparing amygdala activity over time showed that high self-controllers successfully maintained down-regulation of the left amygdala over time, while low self-controllers failed to down-regulate towards the end of the experiment. This indicates that high self-controllers are better at maintaining a motivated state supporting emotion regulation over time. Our results support assumptions concerning a close relation of self-control and emotion regulation as two domains of behavioral control. They further indicate that individual differences in functional connectivity between task-related brain areas directly relate to differences in trait self-control.

  12. Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

    PubMed

    Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

    2014-05-01

    This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach.

  13. Generalized predictive control for a coupled four tank MIMO system using a continuous-discrete time observer.

    PubMed

    Gouta, Houssemeddine; Hadj Saïd, Salim; Barhoumi, Nabil; M'Sahli, Faouzi

    2017-03-01

    This paper deals with the problem of the observer based control design for a coupled four-tank liquid level system. For this MIMO system's dynamics, motivated by a desire to provide precise and sensorless liquid level control, a nonlinear predictive controller based on a continuous-discrete observer is presented. First, an analytical solution from the model predictive control (MPC) technique is developed for a particular class of nonlinear MIMO systems and its corresponding exponential stability is proven. Then, a high gain observer that runs in continuous-time with an output error correction time that is updated in a mixed continuous-discrete fashion is designed in order to estimate the liquid levels in the two upper tanks. The effectiveness of the designed control schemes are validated by two tests; The first one is maintaining a constant level in the first bottom tank while making the level in the second bottom tank to follow a sinusoidal reference signal. The second test is more difficult and it is made using two trapezoidal reference signals in order to see the decoupling performance of the system's outputs. Simulation and experimental results validate the objective of the paper.

  14. Using Machine Learning to Predict Swine Movements within a Regional Program to Improve Control of Infectious Diseases in the US

    PubMed Central

    Valdes-Donoso, Pablo; VanderWaal, Kimberly; Jarvis, Lovell S.; Wayne, Spencer R.; Perez, Andres M.

    2017-01-01

    Between-farm animal movement is one of the most important factors influencing the spread of infectious diseases in food animals, including in the US swine industry. Understanding the structural network of contacts in a food animal industry is prerequisite to planning for efficient production strategies and for effective disease control measures. Unfortunately, data regarding between-farm animal movements in the US are not systematically collected and thus, such information is often unavailable. In this paper, we develop a procedure to replicate the structure of a network, making use of partial data available, and subsequently use the model developed to predict animal movements among sites in 34 Minnesota counties. First, we summarized two networks of swine producing facilities in Minnesota, then we used a machine learning technique referred to as random forest, an ensemble of independent classification trees, to estimate the probability of pig movements between farms and/or markets sites located in two counties in Minnesota. The model was calibrated and tested by comparing predicted data and observed data in those two counties for which data were available. Finally, the model was used to predict animal movements in sites located across 34 Minnesota counties. Variables that were important in predicting pig movements included between-site distance, ownership, and production type of the sending and receiving farms and/or markets. Using a weighted-kernel approach to describe spatial variation in the centrality measures of the predicted network, we showed that the south-central region of the study area exhibited high aggregation of predicted pig movements. Our results show an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome, which is believed to be transmitted, at least in part, though animal movements. While the correspondence of movements and disease is not a causal test, it suggests that the predicted network may approximate

  15. Using a computer-controlled simulated digestion system to predict the energetic value of corn for ducks.

    PubMed

    Zhao, F; Zhang, L; Mi, B M; Zhang, H F; Hou, S S; Zhang, Z Y

    2014-06-01

    Two experiments were conducted to develop a computer-controlled digestion system to simulate the digestion process of duck for predicting the concentration of ME and the metabolizability of gross energy (GE) in corn. In a calibration experiment, 30 corn-based calibration samples with a previously published ME concentration in 2008 were used to develop the prediction models for in vivo energetic values. The linear relationships were established between in vivo ME concentration and in vitro digestible energy (IVDE) concentration, and between in vivo metabolizability of GE (ME/GE) and in vitro digestibility of GE (IVDE/GE), respectively. In a validation experiment, 6 sources of corn with previously published ME concentration in 2008 randomly selected from the primary corn-growing regions of China were used to validate the prediction models established in the calibration experiment. The results showed that in calibration samples, the IVDE concentration was positively correlated with the AME (r = 0.9419), AMEn (r = 0.9480), TME (r = 0.9403), and TMEn concentration (r = 0.9473). Similarly, the IVDE/GE was positively correlated with the AME/GE (r = 0.95987), AMEn/GE (r = 0.9641), TME/GE (r = 0.9588), and TMEn/GE (r = 0.9637). The coefficient of determination greater than 0.88 and 0.91, and residual SD less than 45 kcal/kg of DM and 1.01% were observed in the prediction models for ME concentrations and ME/GE, respectively. Twenty-nine out of 30 calibration samples showed differences less than 100 kcal/kg of DM and 2.4% between determined and predicted values for 4 ME (AME, AMEn, TME, and TMEn) and for 4 ME/GE (AME/GE, AMEn/GE, TME/GE, and TMEn/GE), respectively. Using prediction models developed from 30 calibration samples, 6 validation samples further showed differences less than 100 kcal/kg of DM and 2% between determined and predicted values for ME and ME/GE, respectively. Therefore, the computer-controlled simulated digestion system can be used to predict the ME and ME

  16. Using Machine Learning to Predict Swine Movements within a Regional Program to Improve Control of Infectious Diseases in the US.

    PubMed

    Valdes-Donoso, Pablo; VanderWaal, Kimberly; Jarvis, Lovell S; Wayne, Spencer R; Perez, Andres M

    2017-01-01

    Between-farm animal movement is one of the most important factors influencing the spread of infectious diseases in food animals, including in the US swine industry. Understanding the structural network of contacts in a food animal industry is prerequisite to planning for efficient production strategies and for effective disease control measures. Unfortunately, data regarding between-farm animal movements in the US are not systematically collected and thus, such information is often unavailable. In this paper, we develop a procedure to replicate the structure of a network, making use of partial data available, and subsequently use the model developed to predict animal movements among sites in 34 Minnesota counties. First, we summarized two networks of swine producing facilities in Minnesota, then we used a machine learning technique referred to as random forest, an ensemble of independent classification trees, to estimate the probability of pig movements between farms and/or markets sites located in two counties in Minnesota. The model was calibrated and tested by comparing predicted data and observed data in those two counties for which data were available. Finally, the model was used to predict animal movements in sites located across 34 Minnesota counties. Variables that were important in predicting pig movements included between-site distance, ownership, and production type of the sending and receiving farms and/or markets. Using a weighted-kernel approach to describe spatial variation in the centrality measures of the predicted network, we showed that the south-central region of the study area exhibited high aggregation of predicted pig movements. Our results show an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome, which is believed to be transmitted, at least in part, though animal movements. While the correspondence of movements and disease is not a causal test, it suggests that the predicted network may approximate

  17. Using a hybrid model to predict solute transfer from initially saturated soil into surface runoff with controlled drainage water.

    PubMed

    Tong, Juxiu; Hu, Bill X; Yang, Jinzhong; Zhu, Yan

    2016-06-01

    The mixing layer theory is not suitable for predicting solute transfer from initially saturated soil to surface runoff water under controlled drainage conditions. By coupling the mixing layer theory model with the numerical model Hydrus-1D, a hybrid solute transfer model has been proposed to predict soil solute transfer from an initially saturated soil into surface water, under controlled drainage water conditions. The model can also consider the increasing ponding water conditions on soil surface before surface runoff. The data of solute concentration in surface runoff and drainage water from a sand experiment is used as the reference experiment. The parameters for the water flow and solute transfer model and mixing layer depth under controlled drainage water condition are identified. Based on these identified parameters, the model is applied to another initially saturated sand experiment with constant and time-increasing mixing layer depth after surface runoff, under the controlled drainage water condition with lower drainage height at the bottom. The simulation results agree well with the observed data. Study results suggest that the hybrid model can accurately simulate the solute transfer from initially saturated soil into surface runoff under controlled drainage water condition. And it has been found that the prediction with increasing mixing layer depth is better than that with the constant one in the experiment with lower drainage condition. Since lower drainage condition and deeper ponded water depth result in later runoff start time, more solute sources in the mixing layer are needed for the surface water, and larger change rate results in the increasing mixing layer depth.

  18. How Home Gets to School: Parental Control Strategies Predict Children's School Readiness

    ERIC Educational Resources Information Center

    Walker, Aimee Kleisner; MacPhee, David

    2011-01-01

    At-risk families' control style (autonomy support and coercive control) was examined in relation to children's school readiness; children's social skills and mastery motivation were hypothesized mediating variables. In two different, low-income samples from diverse ethnic backgrounds, one preschool sample recruited from Head Start (N = 199) and a…

  19. Prediction of Children's Empathy-Related Responding from Their Effortful Control and Parents' Expressivity

    ERIC Educational Resources Information Center

    Valiente, Carlos; Eisenberg, Nancy; Fabes, Richard A.; Shepard, Stephanie A.; Cumberland, Amanda; Losoya, Sandra H.

    2004-01-01

    In this study, the linear and interactive relations of children's effortful control and parents' emotional expressivity to children's empathy-related responses were examined. Participants were 214 children, 4.5 to 8 years old. Children's effortful control was negatively related to their personal distress and was positively related to their…

  20. High Self-Control Predicts More Positive Emotions, Better Engagement, and Higher Achievement in School

    ERIC Educational Resources Information Center

    King, Ronnel B.; Gaerlan, Marianne Jennifer M.

    2014-01-01

    The control-value theory of academic emotions has emerged as a useful framework for studying the antecedents and consequences of different emotions in school. This framework focuses on the role of control-related and value-related appraisals as proximal antecedents of emotions. In this study, we take an individual differences approach to examine…