Multi-objective optimization for model predictive control.

PubMed

Wojsznis, Willy; Mehta, Ashish; Wojsznis, Peter; Thiele, Dirk; Blevins, Terry

2007-06-01

This paper presents a technique of multi-objective optimization for Model Predictive Control (MPC) where the optimization has three levels of the objective function, in order of priority: handling constraints, maximizing economics, and maintaining control. The greatest weights are assigned dynamically to control or constraint variables that are predicted to be out of their limits. The weights assigned for economics have to out-weigh those assigned for control objectives. Control variables (CV) can be controlled at fixed targets or within one- or two-sided ranges around the targets. Manipulated Variables (MV) can have assigned targets too, which may be predefined values or current actual values. This MV functionality is extremely useful when economic objectives are not defined for some or all the MVs. To achieve this complex operation, handle process outputs predicted to go out of limits, and have a guaranteed solution for any condition, the technique makes use of the priority structure, penalties on slack variables, and redefinition of the constraint and control model. An engineering implementation of this approach is shown in the MPC embedded in an industrial control system. The optimization and control of a distillation column, the standard Shell heavy oil fractionator (HOF) problem, is adequately achieved with this MPC.

Efficient Strategies for Predictive Cell-Level Control of Lithium-Ion Batteries

NASA Astrophysics Data System (ADS)

Xavier, Marcelo A.

This dissertation introduces a set of state-space based model predictive control (MPC) algorithms tailored to a non-zero feedthrough term to account for the ohmic resistance that is inherent to the battery dynamics. MPC is herein applied to the problem of regulating cell-level measures of performance for lithium-ion batteries; the control methodologies are used first to compute a fast charging profile that respects input, output, and state constraints, i.e., input current, terminal voltage, and state of charge for an equivalent circuit model of the battery cell, and extended later to a linearized physics-based reduced-order model. The novelty of this work can summarized as follows: (1) the MPC variants are employed to a physics based reduce-order model in order to make use of the available set of internal electrochemical variables and mitigate internal mechanisms of cell degradation. (e.g., lithium plating); (2) we developed a dual-mode MPC closed-loop paradigm that suits the battery control problem with the objective of reducing computational effort by solving simpler optimization routines and guaranteeing stability; and finally (3) we developed a completely new approach of the use of a predictive control strategy where MPC is employed as a "smart sensor" for power estimation. Results are presented that show the comparative performance of the MPC algorithms for both EMC and PBROM These results highlight that dual-mode MPC can deliver optimal input current profiles by using a shorter horizon while still guaranteeing stability. Additionally, rigorous mathematical developments are presented for the development of the MPC algorithms. The use of MPC as a "smart sensor" presents it self as an appealing method for power estimation, since MPC permits a fully dynamic input profile that is able to achieve performance right at the proper constraint boundaries. Therefore, MPC is expected to produce accurate power limits for each computed sample time when compared to the

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.

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

Dynamical tuning for MPC using population games: A water supply network application.

PubMed

Barreiro-Gomez, Julian; Ocampo-Martinez, Carlos; Quijano, Nicanor

2017-07-01

Model predictive control (MPC) is a suitable strategy for the control of large-scale systems that have multiple design requirements, e.g., multiple physical and operational constraints. Besides, an MPC controller is able to deal with multiple control objectives considering them within the cost function, which implies to determine a proper prioritization for each of the objectives. Furthermore, when the system has time-varying parameters and/or disturbances, the appropriate prioritization might vary along the time as well. This situation leads to the need of a dynamical tuning methodology. This paper addresses the dynamical tuning issue by using evolutionary game theory. The advantages of the proposed method are highlighted and tested over a large-scale water supply network with periodic time-varying disturbances. Finally, results are analyzed with respect to a multi-objective MPC controller that uses static tuning. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal Control of Distributed Energy Resources using Model Predictive Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

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, minimizingmore » 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.« less

Decentralized Fuzzy MPC on Spatial Power Control of a Large PHWR

NASA Astrophysics Data System (ADS)

Liu, Xiangjie; Jiang, Di; Lee, Kwang Y.

2016-08-01

Reliable power control for stabilizing the spatial oscillations is quite important for ensuring the safe operation of a modern pressurized heavy water reactor (PHWR), since these spatial oscillations can cause “flux tilting” in the reactor core. In this paper, a decentralized fuzzy model predictive control (DFMPC) is proposed for spatial control of PHWR. Due to the load dependent dynamics of the nuclear power plant, fuzzy modeling is used to approximate the nonlinear process. A fuzzy Lyapunov function and “quasi-min-max” strategy is utilized in designing the DFMPC, to reduce the conservatism. The plant-wide stability is achieved by the asymptotically positive realness constraint (APRC) for this decentralized MPC. The solving optimization problem is based on a receding horizon scheme involving the linear matrix inequalities (LMIs) technique. Through dynamic simulations, it is demonstrated that the designed DFMPC can effectively suppress spatial oscillations developed in PHWR, and further, shows the advantages over the typical parallel distributed compensation (PDC) control scheme.

A neural network based implementation of an MPC algorithm applied in the control systems of electromechanical plants

NASA Astrophysics Data System (ADS)

Marusak, Piotr M.; Kuntanapreeda, Suwat

2018-01-01

The paper considers application of a neural network based implementation of a model predictive control (MPC) control algorithm to electromechanical plants. Properties of such control plants implicate that a relatively short sampling time should be used. However, in such a case, finding the control value numerically may be too time-consuming. Therefore, the current paper tests the solution based on transforming the MPC optimization problem into a set of differential equations whose solution is the same as that of the original optimization problem. This set of differential equations can be interpreted as a dynamic neural network. In such an approach, the constraints can be introduced into the optimization problem with relative ease. Moreover, the solution of the optimization problem can be obtained faster than when the standard numerical quadratic programming routine is used. However, a very careful tuning of the algorithm is needed to achieve this. A DC motor and an electrohydraulic actuator are taken as illustrative examples. The feasibility and effectiveness of the proposed approach are demonstrated through numerical simulations.

Novel Formulation of Adaptive MPC as EKF Using ANN Model: Multiproduct Semibatch Polymerization Reactor Case Study.

PubMed

Kamesh, Reddi; Rani, Kalipatnapu Yamuna

2017-12-01

In this paper, a novel formulation for nonlinear model predictive control (MPC) has been proposed incorporating the extended Kalman filter (EKF) control concept using a purely data-driven artificial neural network (ANN) model based on measurements for supervisory control. The proposed scheme consists of two modules focusing on online parameter estimation based on past measurements and control estimation over control horizon based on minimizing the deviation of model output predictions from set points along the prediction horizon. An industrial case study for temperature control of a multiproduct semibatch polymerization reactor posed as a challenge problem has been considered as a test bed to apply the proposed ANN-EKFMPC strategy at supervisory level as a cascade control configuration along with proportional integral controller [ANN-EKFMPC with PI (ANN-EKFMPC-PI)]. The proposed approach is formulated incorporating all aspects of MPC including move suppression factor for control effort minimization and constraint-handling capability including terminal constraints. The nominal stability analysis and offset-free tracking capabilities of the proposed controller are proved. Its performance is evaluated by comparison with a standard MPC-based cascade control approach using the same adaptive ANN model. The ANN-EKFMPC-PI control configuration has shown better controller performance in terms of temperature tracking, smoother input profiles, as well as constraint-handling ability compared with the ANN-MPC with PI approach for two products in summer and winter. The proposed scheme is found to be versatile although it is based on a purely data-driven model with online parameter estimation.

MPC1-like Is a Placental Mammal-specific Mitochondrial Pyruvate Carrier Subunit Expressed in Postmeiotic Male Germ Cells.

PubMed

Vanderperre, Benoît; Cermakova, Kristina; Escoffier, Jessica; Kaba, Mayis; Bender, Tom; Nef, Serge; Martinou, Jean-Claude

2016-08-05

Selective transport of pyruvate across the inner mitochondrial membrane by the mitochondrial pyruvate carrier (MPC) is a fundamental step that couples cytosolic and mitochondrial metabolism. The recent molecular identification of the MPC complex has revealed two interacting subunits, MPC1 and MPC2. Although in yeast, an additional subunit, MPC3, can functionally replace MPC2, no alternative MPC subunits have been described in higher eukaryotes. Here, we report for the first time the existence of a novel MPC subunit termed MPC1-like (MPC1L), which is present uniquely in placental mammals. MPC1L shares high sequence, structural, and topological homology with MPC1. In addition, we provide several lines of evidence to show that MPC1L is functionally equivalent to MPC1: 1) when co-expressed with MPC2, it rescues pyruvate import in a MPC-deleted yeast strain; 2) in mammalian cells, it can associate with MPC2 to form a functional carrier as assessed by bioluminescence resonance energy transfer; 3) in MPC1 depleted mouse embryonic fibroblasts, MPC1L rescues the loss of pyruvate-driven respiration and stabilizes MPC2 expression; and 4) MPC1- and MPC1L-mediated pyruvate imports show similar efficiency. However, we show that MPC1L has a highly specific expression pattern and is localized almost exclusively in testis and more specifically in postmeiotic spermatids and sperm cells. This is in marked contrast to MPC1/MPC2, which are ubiquitously expressed throughout the organism. To date, the biological importance of this alternative MPC complex during spermatogenesis in placental mammals remains unknown. Nevertheless, these findings open up new avenues for investigating the structure-function relationship within the MPC complex. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

A comparison of a novel robust decentralised control strategy and MPC for industrial high purity, high recovery, multicomponent distillation.

PubMed

Udugama, Isuru A; Wolfenstetter, Florian; Kirkpatrick, Robert; Yu, Wei; Young, Brent R

2017-07-01

In this work we have developed a novel, robust practical control structure to regulate an industrial methanol distillation column. This proposed control scheme is based on a override control framework and can manage a non-key trace ethanol product impurity specification while maintaining high product recovery. For comparison purposes, a MPC with a discrete process model (based on step tests) was also developed and tested. The results from process disturbance testing shows that, both the MPC and the proposed controller were capable of maintaining both the trace level ethanol specification in the distillate (X D ) and high product recovery (β). Closer analysis revealed that the MPC controller has a tighter X D control, while the proposed controller was tighter in β control. The tight X D control allowed the MPC to operate at a higher X D set point (closer to the 10ppm AA grade methanol standard), allowing for savings in energy usage. Despite the energy savings of the MPC, the proposed control scheme has lower installation and running costs. An economic analysis revealed a multitude of other external economic and plant design factors, that should be considered when making a decision between the two controllers. In general, we found relatively high energy costs favour MPC. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A Discrete-Time Average Model Based Predictive Control for Quasi-Z-Source Inverter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Yushan; Abu-Rub, Haitham; Xue, Yaosuo

A discrete-time average model-based predictive control (DTA-MPC) is proposed for a quasi-Z-source inverter (qZSI). As a single-stage inverter topology, the qZSI regulates the dc-link voltage and the ac output voltage through the shoot-through (ST) duty cycle and the modulation index. Several feedback strategies have been dedicated to produce these two control variables, among which the most popular are the proportional–integral (PI)-based control and the conventional model-predictive control (MPC). However, in the former, there are tradeoffs between fast response and stability; the latter is robust, but at the cost of high calculation burden and variable switching frequency. Moreover, they require anmore » elaborated design or fine tuning of controller parameters. The proposed DTA-MPC predicts future behaviors of the ST duty cycle and modulation signals, based on the established discrete-time average model of the quasi-Z-source (qZS) inductor current, the qZS capacitor voltage, and load currents. The prediction actions are applied to the qZSI modulator in the next sampling instant, without the need of other controller parameters’ design. A constant switching frequency and significantly reduced computations are achieved with high performance. Transient responses and steady-state accuracy of the qZSI system under the proposed DTA-MPC are investigated and compared with the PI-based control and the conventional MPC. Simulation and experimental results verify the effectiveness of the proposed approach for the qZSI.« less

A Discrete-Time Average Model Based Predictive Control for Quasi-Z-Source Inverter

DOE PAGES

Liu, Yushan; Abu-Rub, Haitham; Xue, Yaosuo; ...

2017-12-25

A discrete-time average model-based predictive control (DTA-MPC) is proposed for a quasi-Z-source inverter (qZSI). As a single-stage inverter topology, the qZSI regulates the dc-link voltage and the ac output voltage through the shoot-through (ST) duty cycle and the modulation index. Several feedback strategies have been dedicated to produce these two control variables, among which the most popular are the proportional–integral (PI)-based control and the conventional model-predictive control (MPC). However, in the former, there are tradeoffs between fast response and stability; the latter is robust, but at the cost of high calculation burden and variable switching frequency. Moreover, they require anmore » elaborated design or fine tuning of controller parameters. The proposed DTA-MPC predicts future behaviors of the ST duty cycle and modulation signals, based on the established discrete-time average model of the quasi-Z-source (qZS) inductor current, the qZS capacitor voltage, and load currents. The prediction actions are applied to the qZSI modulator in the next sampling instant, without the need of other controller parameters’ design. A constant switching frequency and significantly reduced computations are achieved with high performance. Transient responses and steady-state accuracy of the qZSI system under the proposed DTA-MPC are investigated and compared with the PI-based control and the conventional MPC. Simulation and experimental results verify the effectiveness of the proposed approach for the qZSI.« less

Closed-loop control of artificial pancreatic Beta -cell in type 1 diabetes mellitus using model predictive iterative learning control.

PubMed

Wang, Youqing; Dassau, Eyal; Doyle, Francis J

2010-02-01

A novel combination of iterative learning control (ILC) and model predictive control (MPC), referred to here as model predictive iterative learning control (MPILC), is proposed for glycemic control in type 1 diabetes mellitus. MPILC exploits two key factors: frequent glucose readings made possible by continuous glucose monitoring technology; and the repetitive nature of glucose-meal-insulin dynamics with a 24-h cycle. The proposed algorithm can learn from an individual's lifestyle, allowing the control performance to be improved from day to day. After less than 10 days, the blood glucose concentrations can be kept within a range of 90-170 mg/dL. Generally, control performance under MPILC is better than that under MPC. The proposed methodology is robust to random variations in meal timings within +/-60 min or meal amounts within +/-75% of the nominal value, which validates MPILC's superior robustness compared to run-to-run control. Moreover, to further improve the algorithm's robustness, an automatic scheme for setpoint update that ensures safe convergence is proposed. Furthermore, the proposed method does not require user intervention; hence, the algorithm should be of particular interest for glycemic control in children and adolescents.

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.

Enhanced pid vs model predictive control applied to bldc motor

NASA Astrophysics Data System (ADS)

Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.

2018-01-01

BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.

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. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Robust model predictive control for optimal continuous drug administration.

PubMed

Sopasakis, Pantelis; Patrinos, Panagiotis; Sarimveis, Haralambos

2014-10-01

In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise. A case study on a PBPK model with 7 compartments, constraints on 5 tissues and a variable drug concentration set-point illustrates the efficiency of the methodology in drug dosing control applications. The proposed methodology is also tested in an uncertain setting and proves successful in presence of modelling errors and inaccurate measurements. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

System-wide hybrid MPC-PID control of a continuous pharmaceutical tablet manufacturing process via direct compaction.

PubMed

Singh, Ravendra; Ierapetritou, Marianthi; Ramachandran, Rohit

2013-11-01

The next generation of QbD based pharmaceutical products will be manufactured through continuous processing. This will allow the integration of online/inline monitoring tools, coupled with an efficient advanced model-based feedback control systems, to achieve precise control of process variables, so that the predefined product quality can be achieved consistently. The direct compaction process considered in this study is highly interactive and involves time delays for a number of process variables due to sensor placements, process equipment dimensions, and the flow characteristics of the solid material. A simple feedback regulatory control system (e.g., PI(D)) by itself may not be sufficient to achieve the tight process control that is mandated by regulatory authorities. The process presented herein comprises of coupled dynamics involving slow and fast responses, indicating the requirement of a hybrid control scheme such as a combined MPC-PID control scheme. In this manuscript, an efficient system-wide hybrid control strategy for an integrated continuous pharmaceutical tablet manufacturing process via direct compaction has been designed. The designed control system is a hybrid scheme of MPC-PID control. An effective controller parameter tuning strategy involving an ITAE method coupled with an optimization strategy has been used for tuning of both MPC and PID parameters. The designed hybrid control system has been implemented in a first-principles model-based flowsheet that was simulated in gPROMS (Process System Enterprise). Results demonstrate enhanced performance of critical quality attributes (CQAs) under the hybrid control scheme compared to only PID or MPC control schemes, illustrating the potential of a hybrid control scheme in improving pharmaceutical manufacturing operations. Copyright © 2013 Elsevier B.V. All rights reserved.

Nanostructured DPA-MPC-DPA triblock copolymer gel for controlled drug release of ketoprofen and spironolactone.

PubMed

Azmy, Bahaa; Standen, Guy; Kristova, Petra; Flint, Andrew; Lewis, Andrew L; Salvage, Jonathan P

2017-08-01

Uncontrolled rapid release of drugs can reduce their therapeutic efficacy and cause undesirable toxicity; however, controlled release from reservoir materials helps overcome this issue. The aims of this study were to determine the release profiles of ketoprofen and spironolactone from a pH-responsive self-assembling DPA-MPC-DPA triblock copolymer gel and elucidate underlying physiochemical properties. Drug release profiles from DPA 50 -MPC 250 -DPA 50 gel (pH 7.5), over 32 h (37 °C), were determined using UV-Vis spectroscopy. Nanoparticle size was measured by dynamic light scattering (DLS) and critical micelle concentration (CMC) by pyrene fluorescence. Polymer gel viscosity was examined via rheology, nanoparticle morphology investigated using scanning transmission electron microscopy (STEM) and the gel matrix observed using cryo-scanning electron microscopy (Cryo-SEM). DPA 50 -MPC 250 -DPA 50 copolymer (15% w/v) formed a free-standing gel (pH 7.5) that controlled drug release relative to free drugs. The copolymer possessed a low CMC, nanoparticle size increased with copolymer concentration, and DLS data were consistent with STEM. The gel displayed thermostable viscosity at physiological temperatures, and the gel matrix was a nanostructured aggregation of smaller nanoparticles. The DPA 50 -MPC 250 -DPA 50 copolymer gel could be used as a drug delivery system to provide the controlled drug release of ketoprofen and spironolactone. © 2017 Royal Pharmaceutical Society.

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.

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

NASA Astrophysics Data System (ADS)

Xavier, Marcelo A.; Trimboli, M. Scott

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.

Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties.

PubMed

Wu, Sheng; Jin, Qibing; Zhang, Ridong; Zhang, Junfeng; Gao, Furong

2017-07-01

In this paper, an improved constrained tracking control design is proposed for batch processes under uncertainties. A new process model that facilitates process state and tracking error augmentation with further additional tuning is first proposed. Then a subsequent controller design is formulated using robust stable constrained MPC optimization. Unlike conventional robust model predictive control (MPC), the proposed method enables the controller design to bear more degrees of tuning so that improved tracking control can be acquired, which is very important since uncertainties exist inevitably in practice and cause model/plant mismatches. An injection molding process is introduced to illustrate the effectiveness of the proposed MPC approach in comparison with conventional robust MPC. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Terminal spacecraft rendezvous and capture with LASSO model predictive control

NASA Astrophysics Data System (ADS)

Hartley, Edward N.; Gallieri, Marco; Maciejowski, Jan M.

2013-11-01

The recently investigated ℓasso model predictive control (MPC) is applied to the terminal phase of a spacecraft rendezvous and capture mission. The interaction between the cost function and the treatment of minimum impulse bit is also investigated. The propellant consumption with ℓasso MPC for the considered scenario is noticeably less than with a conventional quadratic cost and control actions are sparser in time. Propellant consumption and sparsity are competitive with those achieved using a zone-based ℓ1 cost function, whilst requiring fewer decision variables in the optimisation problem than the latter. The ℓasso MPC is demonstrated to meet tighter specifications on control precision and also avoids the risk of undesirable behaviours often associated with pure ℓ1 stage costs.

Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts

NASA Astrophysics Data System (ADS)

Tian, Xin; Negenborn, Rudy R.; van Overloop, Peter-Jules; María Maestre, José; Sadowska, Anna; van de Giesen, Nick

2017-11-01

Model Predictive Control (MPC) is one of the most advanced real-time control techniques that has been widely applied to Water Resources Management (WRM). MPC can manage the water system in a holistic manner and has a flexible structure to incorporate specific elements, such as setpoints and constraints. Therefore, MPC has shown its versatile performance in many branches of WRM. Nonetheless, with the in-depth understanding of stochastic hydrology in recent studies, MPC also faces the challenge of how to cope with hydrological uncertainty in its decision-making process. A possible way to embed the uncertainty is to generate an Ensemble Forecast (EF) of hydrological variables, rather than a deterministic one. The combination of MPC and EF results in a more comprehensive approach: Multi-scenario MPC (MS-MPC). In this study, we will first assess the model performance of MS-MPC, considering an ensemble streamflow forecast. Noticeably, the computational inefficiency may be a critical obstacle that hinders applicability of MS-MPC. In fact, with more scenarios taken into account, the computational burden of solving an optimization problem in MS-MPC accordingly increases. To deal with this challenge, we propose the Adaptive Control Resolution (ACR) approach as a computationally efficient scheme to practically reduce the number of control variables in MS-MPC. In brief, the ACR approach uses a mixed-resolution control time step from the near future to the distant future. The ACR-MPC approach is tested on a real-world case study: an integrated flood control and navigation problem in the North Sea Canal of the Netherlands. Such an approach reduces the computation time by 18% and up in our case study. At the same time, the model performance of ACR-MPC remains close to that of conventional MPC.

Robust distributed model predictive control of linear systems with structured time-varying uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Langwen; Xie, Wei; Wang, Jingcheng

2017-11-01

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min-max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arumugasamy, Senthil Kumar; Ahmad, Z.

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 spacemore » 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.« less

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xavier, MA; Trimboli, MS

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 suggestmore » significant performance improvements might be achieved by extending the result to electrochemical models. (C) 2015 Elsevier B.V. All rights reserved.« less

Cooperative Management of a Lithium-Ion Battery Energy Storage Network: A Distributed MPC Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fang, Huazhen; Wu, Di; Yang, Tao

2016-12-12

This paper presents a study of cooperative power supply and storage for a network of Lithium-ion energy storage systems (LiBESSs). We propose to develop a distributed model predictive control (MPC) approach for two reasons. First, able to account for the practical constraints of a LiBESS, the MPC can enable a constraint-aware operation. Second, a distributed management can cope with a complex network that integrates a large number of LiBESSs over a complex communication topology. With this motivation, we then build a fully distributed MPC algorithm from an optimization perspective, which is based on an extension of the alternating direction methodmore » of multipliers (ADMM) method. A simulation example is provided to demonstrate the effectiveness of the proposed algorithm.« less

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

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.

On the study of control effectiveness and computational efficiency of reduced Saint-Venant model in model predictive control of open channel flow

NASA Astrophysics Data System (ADS)

Xu, M.; van Overloop, P. J.; van de Giesen, N. C.

2011-02-01

Model predictive control (MPC) of open channel flow is becoming an important tool in water management. The complexity of the prediction model has a large influence on the MPC application in terms of control effectiveness and computational efficiency. The Saint-Venant equations, called SV model in this paper, and the Integrator Delay (ID) model are either accurate but computationally costly, or simple but restricted to allowed flow changes. In this paper, a reduced Saint-Venant (RSV) model is developed through a model reduction technique, Proper Orthogonal Decomposition (POD), on the SV equations. The RSV model keeps the main flow dynamics and functions over a large flow range but is easier to implement in MPC. In the test case of a modeled canal reach, the number of states and disturbances in the RSV model is about 45 and 16 times less than the SV model, respectively. The computational time of MPC with the RSV model is significantly reduced, while the controller remains effective. Thus, the RSV model is a promising means to balance the control effectiveness and computational efficiency.

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.

Zone model predictive control: a strategy to minimize hyper- and hypoglycemic events.

PubMed

Grosman, Benyamin; Dassau, Eyal; Zisser, Howard C; Jovanovic, Lois; Doyle, Francis J

2010-07-01

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. An artificial pancreatic beta-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 beta-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. Zone-MPC succeeds in maintaining glycemic responses closer to euglycemia compared to the "optimal" open-loop treatment in te three

MPC Design for Rapid Pump-Attenuation and Expedited Hyperglycemia Response to Treat T1DM with an Artificial Pancreas

PubMed Central

Gondhalekar, Ravi; Dassau, Eyal; Doyle, Francis J.

2016-01-01

The design of a Model Predictive Control (MPC) strategy for the closed-loop operation of an Artificial Pancreas (AP) for treating Type 1 Diabetes Mellitus (T1DM) is considered in this paper. The contribution of this paper is to propose two changes to the usual structure of the MPC problems typically considered for control of an AP. The first proposed change is to replace the symmetric, quadratic input cost function with an asymmetric, quadratic function, allowing negative control inputs to be penalized less than positive ones. This facilitates rapid pump-suspensions in response to predicted hypoglycemia, while simultaneously permitting the design of a conservative response to hyperglycemia. The second proposed change is to penalize the velocity of the predicted glucose level, where this velocity penalty is based on a cost function that is again asymmetric, but additionally state-dependent. This facilitates the accelerated response to acute, persistent hyperglycemic events, e.g., as induced by unannounced meals. The novel functionality is demonstrated by numerical examples, and the efficacy of the proposed MPC strategy verified using the University of Padova/Virginia metabolic simulator. PMID:28479660

A fast implementation of MPC-based motion cueing algorithms for mid-size road vehicle motion simulators

NASA Astrophysics Data System (ADS)

Bruschetta, M.; Maran, F.; Beghi, A.

2017-06-01

The use of dynamic driving simulators is constantly increasing in the automotive community, with applications ranging from vehicle development to rehab and driver training. The effectiveness of such devices is related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform. Such strategies are called motion cueing algorithms (MCAs). In recent years several MCAs based on model predictive control (MPC) techniques have been proposed. The main drawback associated with the use of MPC is its computational burden, that may limit their application to high performance dynamic simulators. In the paper, a fast, real-time implementation of an MPC-based MCA for 9 DOF, high performance platform is proposed. Effectiveness of the approach in managing the available working area is illustrated by presenting experimental results from an implementation on a real device with a 200 Hz control frequency.

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.

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.

Robust PBPK/PD-Based Model Predictive Control of Blood Glucose.

PubMed

Schaller, Stephan; Lippert, Jorg; Schaupp, Lukas; Pieber, Thomas R; Schuppert, Andreas; Eissing, Thomas

2016-07-01

Automated glucose control (AGC) has not yet reached the point where it can be applied clinically [3]. Challenges are accuracy of subcutaneous (SC) glucose sensors, physiological lag times, and both inter- and intraindividual variability. To address above issues, we developed a novel scheme for MPC that can be applied to AGC. An individualizable generic whole-body physiology-based pharmacokinetic and dynamics (PBPK/PD) model of the glucose, insulin, and glucagon metabolism has been used as the predictive kernel. The high level of mechanistic detail represented by the model takes full advantage of the potential of MPC and may make long-term prediction possible as it captures at least some relevant sources of variability [4]. Robustness against uncertainties was increased by a control cascade relying on proportional-integrative derivative-based offset control. The performance of this AGC scheme was evaluated in silico and retrospectively using data from clinical trials. This analysis revealed that our approach handles sensor noise with a MARD of 10%-14%, and model uncertainties and disturbances. The results suggest that PBPK/PD models are well suited for MPC in a glucose control setting, and that their predictive power in combination with the integrated database-driven (a priori individualizable) model framework will help overcome current challenges in the development of AGC systems. This study provides a new, generic, and robust mechanistic approach to AGC using a PBPK platform with extensive a priori (database) knowledge for individualization.

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.

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.

Robust model predictive control for multi-step short range spacecraft rendezvous

NASA Astrophysics Data System (ADS)

Zhu, Shuyi; Sun, Ran; Wang, Jiaolong; Wang, Jihe; Shao, Xiaowei

2018-07-01

This work presents a robust model predictive control (MPC) approach for the multi-step short range spacecraft rendezvous problem. During the specific short range phase concerned, the chaser is supposed to be initially outside the line-of-sight (LOS) cone. Therefore, the rendezvous process naturally includes two steps: the first step is to transfer the chaser into the LOS cone and the second step is to transfer the chaser into the aimed region with its motion confined within the LOS cone. A novel MPC framework named after Mixed MPC (M-MPC) is proposed, which is the combination of the Variable-Horizon MPC (VH-MPC) framework and the Fixed-Instant MPC (FI-MPC) framework. The M-MPC framework enables the optimization for the two steps to be implemented jointly rather than to be separated factitiously, and its computation workload is acceptable for the usually low-power processors onboard spacecraft. Then considering that disturbances including modeling error, sensor noise and thrust uncertainty may induce undesired constraint violations, a robust technique is developed and it is attached to the above M-MPC framework to form a robust M-MPC approach. The robust technique is based on the chance-constrained idea, which ensures that constraints can be satisfied with a prescribed probability. It improves the robust technique proposed by Gavilan et al., because it eliminates the unnecessary conservativeness by explicitly incorporating known statistical properties of the navigation uncertainty. The efficacy of the robust M-MPC approach is shown in a simulation study.

Application of strainrange partitioning to the prediction of MPC creep-fatigue data for 2 1/4 Cr-1Mo steel

NASA Technical Reports Server (NTRS)

Saltsman, J. F.; Halford, G. R.

1976-01-01

Strainrange partitioning is used to predict the long time cyclic lives of the metal properties council (MPC) creep-fatigue interspersion and cyclic creep-rupture tests conducted with annealed 2 1/4 Cr-1Mo steel. Observed lives agree with predicted lives within factors of two. The strainrange partitioning life relations used for the long time predictions were established from short time creep-fatigue data generated at NASA-Lewis on the same heat of material.

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.

Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces

PubMed Central

Kamrunnahar, M.; Dias, N. S.; Schiff, S. J.

2013-01-01

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. PMID:21267657

Model Predictive Control of HVAC Systems: Implementation and Testing at the University of California, Merced

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haves, Phillip; Hencey, Brandon; Borrell, Francesco

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 themore » 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

Predictive Rotation Profile Control for the DIII-D Tokamak

NASA Astrophysics Data System (ADS)

Wehner, W. P.; Schuster, E.; Boyer, M. D.; Walker, M. L.; Humphreys, D. A.

2017-10-01

Control-oriented modeling and model-based control of the rotation profile are employed to build a suitable control capability for aiding rotation-related physics studies at DIII-D. To obtain a control-oriented model, a simplified version of the momentum balance equation is combined with empirical representations of the momentum sources. The control approach is rooted in a Model Predictive Control (MPC) framework to regulate the rotation profile while satisfying constraints associated with the desired plasma stored energy and/or βN limit. Simple modifications allow for alternative control objectives, such as maximizing the plasma rotation while maintaining a specified input torque. Because the MPC approach can explicitly incorporate various types of constraints, this approach is well suited to a variety of control objectives, and therefore serves as a valuable tool for experimental physics studies. Closed-loop TRANSP simulations are presented to demonstrate the effectiveness of the control approach. Supported by the US DOE under DE-SC0010661 and DE-FC02-04ER54698.

Non-fragile observer-based output feedback control for polytopic uncertain system under distributed model predictive control approach

NASA Astrophysics Data System (ADS)

Zhu, Kaiqun; Song, Yan; Zhang, Sunjie; Zhong, Zhaozhun

2017-07-01

In this paper, a non-fragile observer-based output feedback control problem for the polytopic uncertain system under distributed model predictive control (MPC) approach is discussed. By decomposing the global system into some subsystems, the computation complexity is reduced, so it follows that the online designing time can be saved.Moreover, an observer-based output feedback control algorithm is proposed in the framework of distributed MPC to deal with the difficulties in obtaining the states measurements. In this way, the presented observer-based output-feedback MPC strategy is more flexible and applicable in practice than the traditional state-feedback one. What is more, the non-fragility of the controller has been taken into consideration in favour of increasing the robustness of the polytopic uncertain system. After that, a sufficient stability criterion is presented by using Lyapunov-like functional approach, meanwhile, the corresponding control law and the upper bound of the quadratic cost function are derived by solving an optimisation subject to convex constraints. Finally, some simulation examples are employed to show the effectiveness of the method.

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. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Aperiodic Robust Model Predictive Control for Constrained Continuous-Time Nonlinear Systems: An Event-Triggered Approach.

PubMed

Liu, Changxin; Gao, Jian; Li, Huiping; Xu, Demin

2018-05-01

The event-triggered control is a promising solution to cyber-physical systems, such as networked control systems, multiagent systems, and large-scale intelligent systems. In this paper, we propose an event-triggered model predictive control (MPC) scheme for constrained continuous-time nonlinear systems with bounded disturbances. First, a time-varying tightened state constraint is computed to achieve robust constraint satisfaction, and an event-triggered scheduling strategy is designed in the framework of dual-mode MPC. Second, the sufficient conditions for ensuring feasibility and closed-loop robust stability are developed, respectively. We show that robust stability can be ensured and communication load can be reduced with the proposed MPC algorithm. Finally, numerical simulations and comparison studies are performed to verify the theoretical results.

Short-term Operation of Multi-purpose Reservoir using Model Predictive Control

NASA Astrophysics Data System (ADS)

Uysal, Gokcen; Schwanenberg, Dirk; Alvarado Montero, Rodolfo; Sensoy, Aynur; Arda Sorman, Ali

2017-04-01

Operation of water structures especially with conflicting water supply and flood mitigation objectives is under more stress attributed to growing water demand and changing hydro-climatic conditions. Model Predictive Control (MPC) based optimal control solutions has been successfully applied to different water resources applications. In this study, Feedback Control (FBC) and MPC get combined and an improved joint optimization-simulation operating scheme is proposed. Water supply and flood control objectives are fulfilled by incorporating the long term water supply objectives into a time-dependent variable guide curve policy whereas the extreme floods are attenuated by means of short-term optimization based on MPC. A final experiment is carried out to assess the lead time performance and reliability of forecasts in a hindcasting experiment with imperfect, perturbed forecasts. The framework is tested in Yuvacık Dam reservoir where the main water supply reservoir of Kocaeli City in the northwestern part of Turkey (the Marmara region) and it requires a challenging gate operation due to restricted downstream flow conditions.

Robust predictive control with optimal load tracking for critical applications. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tse, J.; Bentsman, J.; Miller, N.

1994-09-01

This report derives a multi-input multi-output (MIMO) version of a two-degree-of-freedom receding-horizon control law based on mixed H{sub 2}/H{infinity} minimization. First, the integrand in the frequency domain representation of the MIMO performance criterion is decomposed into disturbance and reference spectra. Then the controller is derived which minimizes the peak of the disturbance spectrum and the integral of the reference spectrum on the unit circle. The resulting two-degree-of-freedom MIMO control strategy, referred to as the minimax predictive multivariable control (MPC), is shown to have worst-case-disturbance-rejection and robust-stability properties superior to those of purely H{sub 2}-optimal controllers, such as Generalized Predictive Controlmore » (GPC), for identical horizons. An attractive feature of the receding horizon structure of MPC is that it can, in ways similar to GPC, directly incorporate input constraints and pre-programmed reference inputs, which are nontrivial tasks in the standard H{infinity} design.« less

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.

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.

Avionic Architecture for Model Predictive Control Application in Mars Sample & Return Rendezvous Scenario

NASA Astrophysics Data System (ADS)

Saponara, M.; Tramutola, A.; Creten, P.; Hardy, J.; Philippe, C.

2013-08-01

Optimization-based control techniques such as Model Predictive Control (MPC) are considered extremely attractive for space rendezvous, proximity operations and capture applications that require high level of autonomy, optimal path planning and dynamic safety margins. Such control techniques require high-performance computational needs for solving large optimization problems. The development and implementation in a flight representative avionic architecture of a MPC based Guidance, Navigation and Control system has been investigated in the ESA R&T study “On-line Reconfiguration Control System and Avionics Architecture” (ORCSAT) of the Aurora programme. The paper presents the baseline HW and SW avionic architectures, and verification test results obtained with a customised RASTA spacecraft avionics development platform from Aeroflex Gaisler.

Model predictive control of attitude maneuver of a geostationary flexible satellite based on genetic algorithm

NASA Astrophysics Data System (ADS)

TayyebTaher, M.; Esmaeilzadeh, S. Majid

2017-07-01

This article presents an application of Model Predictive Controller (MPC) to the attitude control of a geostationary flexible satellite. SIMO model has been used for the geostationary satellite, using the Lagrange equations. Flexibility is also included in the modelling equations. The state space equations are expressed in order to simplify the controller. Naturally there is no specific tuning rule to find the best parameters of an MPC controller which fits the desired controller. Being an intelligence method for optimizing problem, Genetic Algorithm has been used for optimizing the performance of MPC controller by tuning the controller parameter due to minimum rise time, settling time, overshoot of the target point of the flexible structure and its mode shape amplitudes to make large attitude maneuvers possible. The model included geosynchronous orbit environment and geostationary satellite parameters. The simulation results of the flexible satellite with attitude maneuver shows the efficiency of proposed optimization method in comparison with LQR optimal controller.

Multivariable model predictive control design of reactive distillation column for Dimethyl Ether production

NASA Astrophysics Data System (ADS)

Wahid, A.; Putra, I. G. E. P.

2018-03-01

Dimethyl ether (DME) as an alternative clean energy has attracted a growing attention in the recent years. DME production via reactive distillation has potential for capital cost and energy requirement savings. However, combination of reaction and distillation on a single column makes reactive distillation process a very complex multivariable system with high non-linearity of process and strong interaction between process variables. This study investigates a multivariable model predictive control (MPC) based on two-point temperature control strategy for the DME reactive distillation column to maintain the purities of both product streams. The process model is estimated by a first order plus dead time model. The DME and water purity is maintained by controlling a stage temperature in rectifying and stripping section, respectively. The result shows that the model predictive controller performed faster responses compared to conventional PI controller that are showed by the smaller ISE values. In addition, the MPC controller is able to handle the loop interactions well.

PHENIX Muon Piston Calorimeter (MPC) APD and Prototype MPC Extension (MPC-EX) Tests

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lajoie, John

2013-06-20

This is a technical scope of work (TSW) between the Fermi National Accelerator Laboratory (Fermilab) and the experimenters of Muon Piston Calorimeter Extension (MPC-EX) Collaboration who have committed to participate in beam tests to be carried out during the 2013- 2014 Fermilab Test Beam Facility program.

Experimental evaluation of model predictive control and inverse dynamics control for spacecraft proximity and docking maneuvers

NASA Astrophysics Data System (ADS)

Virgili-Llop, Josep; Zagaris, Costantinos; Park, Hyeongjun; Zappulla, Richard; Romano, Marcello

2018-03-01

An experimental campaign has been conducted to evaluate the performance of two different guidance and control algorithms on a multi-constrained docking maneuver. The evaluated algorithms are model predictive control (MPC) and inverse dynamics in the virtual domain (IDVD). A linear-quadratic approach with a quadratic programming solver is used for the MPC approach. A nonconvex optimization problem results from the IDVD approach, and a nonlinear programming solver is used. The docking scenario is constrained by the presence of a keep-out zone, an entry cone, and by the chaser's maximum actuation level. The performance metrics for the experiments and numerical simulations include the required control effort and time to dock. The experiments have been conducted in a ground-based air-bearing test bed, using spacecraft simulators that float over a granite table.

Finite Control Set Model Predictive Control for Multiple Distributed Generators Microgrids

NASA Astrophysics Data System (ADS)

Babqi, Abdulrahman Jamal

This dissertation proposes two control strategies for AC microgrids that consist of multiple distributed generators (DGs). The control strategies are valid for both grid-connected and islanded modes of operation. In general, microgrid can operate as a stand-alone system (i.e., islanded mode) or while it is connected to the utility grid (i.e., grid connected mode). To enhance the performance of a micrgorid, a sophisticated control scheme should be employed. The control strategies of microgrids can be divided into primary and secondary controls. The primary control regulates the output active and reactive powers of each DG in grid-connected mode as well as the output voltage and frequency of each DG in islanded mode. The secondary control is responsible for regulating the microgrid voltage and frequency in the islanded mode. Moreover, it provides power sharing schemes among the DGs. In other words, the secondary control specifies the set points (i.e. reference values) for the primary controllers. In this dissertation, Finite Control Set Model Predictive Control (FCS-MPC) was proposed for controlling microgrids. FCS-MPC was used as the primary controller to regulate the output power of each DG (in the grid-connected mode) or the voltage of the point of DG coupling (in the islanded mode of operation). In the grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power flow between each DG and the utility grid. In the islanded mode, Voltage Model Predictive Control (VMPC), as the primary control, and droop control, as the secondary control, were employed to control the output voltage of each DG and system frequency. The controller was equipped with a supplementary current limiting technique in order to limit the output current of each DG in abnormal incidents. The control approach also enabled smooth transition between the two modes. The performance of the control strategy was investigated and verified using PSCAD/EMTDC software

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.

Applied Distributed Model Predictive Control for Energy Efficient Buildings and Ramp Metering

NASA Astrophysics Data System (ADS)

Koehler, Sarah Muraoka

Industrial large-scale control problems present an interesting algorithmic design challenge. A number of controllers must cooperate in real-time on a network of embedded hardware with limited computing power in order to maximize system efficiency while respecting constraints and despite communication delays. Model predictive control (MPC) can automatically synthesize a centralized controller which optimizes an objective function subject to a system model, constraints, and predictions of disturbance. Unfortunately, the computations required by model predictive controllers for large-scale systems often limit its industrial implementation only to medium-scale slow processes. Distributed model predictive control (DMPC) enters the picture as a way to decentralize a large-scale model predictive control problem. The main idea of DMPC is to split the computations required by the MPC problem amongst distributed processors that can compute in parallel and communicate iteratively to find a solution. Some popularly proposed solutions are distributed optimization algorithms such as dual decomposition and the alternating direction method of multipliers (ADMM). However, these algorithms ignore two practical challenges: substantial communication delays present in control systems and also problem non-convexity. This thesis presents two novel and practically effective DMPC algorithms. The first DMPC algorithm is based on a primal-dual active-set method which achieves fast convergence, making it suitable for large-scale control applications which have a large communication delay across its communication network. In particular, this algorithm is suited for MPC problems with a quadratic cost, linear dynamics, forecasted demand, and box constraints. We measure the performance of this algorithm and show that it significantly outperforms both dual decomposition and ADMM in the presence of communication delay. The second DMPC algorithm is based on an inexact interior point method which is

Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation.

PubMed

Ławryńczuk, Maciej

2017-03-01

This paper details development of a Model Predictive Control (MPC) algorithm for a boiler-turbine unit, which is a nonlinear multiple-input multiple-output process. The control objective is to follow set-point changes imposed on two state (output) variables and to satisfy constraints imposed on three inputs and one output. In order to obtain a computationally efficient control scheme, the state-space model is successively linearised on-line for the current operating point and used for prediction. In consequence, the future control policy is easily calculated from a quadratic optimisation problem. For state estimation the extended Kalman filter is used. It is demonstrated that the MPC strategy based on constant linear models does not work satisfactorily for the boiler-turbine unit whereas the discussed algorithm with on-line successive model linearisation gives practically the same trajectories as the truly nonlinear MPC controller with nonlinear optimisation repeated at each sampling instant. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A model predictive speed tracking control approach for autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Zhu, Min; Chen, Huiyan; Xiong, Guangming

2017-03-01

This paper presents a novel speed tracking control approach based on a model predictive control (MPC) framework for autonomous ground vehicles. A switching algorithm without calibration is proposed to determine the drive or brake control. Combined with a simple inverse longitudinal vehicle model and adaptive regulation of MPC, this algorithm can make use of the engine brake torque for various driving conditions and avoid high frequency oscillations automatically. A simplified quadratic program (QP) solving algorithm is used to reduce the computational time, and the approach has been applied in a 16-bit microcontroller. The performance of the proposed approach is evaluated via simulations and vehicle tests, which were carried out in a range of speed-profile tracking tasks. With a well-designed system structure, high-precision speed control is achieved. The system can robustly model uncertainty and external disturbances, and yields a faster response with less overshoot than a PI controller.

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

Model Predictive Control of Type 1 Diabetes: An in Silico Trial

PubMed Central

Magni, Lalo; Raimondo, Davide M.; Bossi, Luca; Man, Chiara Dalla; De Nicolao, Giuseppe; Kovatchev, Boris; Cobelli, Claudio

2007-01-01

Background The development of artificial pancreas has received a new impulse from recent technological advancements in subcutaneous continuous glucose monitoring and subcutaneous insulin pump delivery systems. However, the availability of innovative sensors and actuators, although essential, does not guarantee optimal glycemic regulation. Closed-loop control of blood glucose levels still poses technological challenges to the automatic control expert, most notable of which are the inevitable time delays between glucose sensing and insulin actuation. Methods A new in silico model is exploited for both design and validation of a linear model predictive control (MPC) glucose control system. The starting point is a recently developed meal glucose–insulin model in health, which is modified to describe the metabolic dynamics of a person with type 1 diabetes mellitus. The population distribution of the model parameters originally obtained in healthy 204 patients is modified to describe diabetic patients. Individual models of virtual patients are extracted from this distribution. A discrete-time MPC is designed for all the virtual patients from a unique input–output-linearized approximation of the full model based on the average population values of the parameters. The in silico trial simulates 4 consecutive days, during which the patient receives breakfast, lunch, and dinner each day. Results Provided that the regulator undergoes some individual tuning, satisfactory results are obtained even if the control design relies solely on the average patient model. Only the weight on the glucose concentration error needs to be tuned in a quite straightforward and intuitive way. The ability of the MPC to take advantage of meal announcement information is demonstrated. Imperfect knowledge of the amount of ingested glucose causes only marginal deterioration of performance. In general, MPC results in better regulation than proportional integral derivative, limiting significantly

Towards a Semantically-Enabled Control Strategy for Building Simulations: Integration of Semantic Technologies and Model Predictive Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Delgoshaei, Parastoo; Austin, Mark A.; Pertzborn, Amanda J.

State-of-the-art building simulation control methods incorporate physical constraints into their mathematical models, but omit implicit constraints associated with policies of operation and dependency relationships among rules representing those constraints. To overcome these shortcomings, there is a recent trend in enabling the control strategies with inference-based rule checking capabilities. One solution is to exploit semantic web technologies in building simulation control. Such approaches provide the tools for semantic modeling of domains, and the ability to deduce new information based on the models through use of Description Logic (DL). In a step toward enabling this capability, this paper presents a cross-disciplinary data-drivenmore » control strategy for building energy management simulation that integrates semantic modeling and formal rule checking mechanisms into a Model Predictive Control (MPC) formulation. The results show that MPC provides superior levels of performance when initial conditions and inputs are derived from inference-based rules.« less

2-Methacryloyloxyethyl phosphorylcholine (MPC)-polymer suppresses an increase of oral bacteria: a single-blind, crossover clinical trial.

PubMed

Fujiwara, Natsumi; Yumoto, Hiromichi; Miyamoto, Koji; Hirota, Katsuhiko; Nakae, Hiromi; Tanaka, Saya; Murakami, Keiji; Kudo, Yasusei; Ozaki, Kazumi; Miyake, Yoichiro

2018-05-16

The biocompatible 2-methacryloyloxyethyl phosphorylcholine (MPC)-polymers, which mimic a biomembrane, reduce protein adsorption and bacterial adhesion and inhibit cell attachment. The aim of this study is to clarify whether MPC-polymer can suppress the bacterial adherence in oral cavity by a crossover design. We also investigated the number of Fusobacterium nucleatum, which is the key bacterium forming dental plaque, in clinical samples. This study was a randomized, placebo-controlled, single-blind, crossover study, with two treatment periods separated by a 2-week washout period. We conducted clinical trial with 20 healthy subjects to evaluate the effect of 5% MPC-polymer mouthwash after 5 h on oral microflora. PBS was used as a control. The bacterial number in the gargling sample before and after intervention was counted by an electronic bacterial counter and a culture method. DNA amounts of total bacteria and F. nucleatum were examined by q-PCR. The numbers of total bacteria and oral streptcocci after 5 h of 5% MPC-polymer treatment significantly decreased, compared to the control group. Moreover, the DNA amounts of total bacteria and F. nucleatum significantly decreased by 5% MPC-polymer mouthwash. We suggest that MPC-polymer coating in the oral cavity may suppress the oral bacterial adherence. MPC-polymer can be a potent compound for the control of oral microflora to prevent oral infection.

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

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. Copyright © 2015 Elsevier Inc. All rights reserved.

Model Predictive Control of A Matrix-Converter Based Solid State Transformer for Utility Grid Interaction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xue, Yaosuo

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 betweenmore » 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.« less

Exploring forward physics with the PHENIX MPC-EX upgrade

NASA Astrophysics Data System (ADS)

Novitzky, Norbert; Phenix Collaboration

2014-09-01

The MPC-EX detector is a Si-W preshower extension to the existing Muon Piston Calorimeter (MPC) at PHENIX. Located at forward rapidity, 3 . 1 < | η | < 3 . 8 , the MPC-EX consists of eight layers of alternating W absorber and Si minipad sensors. Covering a large range at forward rapidity makes the MPC-EX and MPC ideal to access low-x partons in the A nucleus of p + A collisions. The neutral pion and direct photon are excellent probes to separate between the initial and final state effects of the pA collisions. Isolating the direct photon signal requires the MPC-EX to be able to distinguish single showers from double showers. The single versus double shower separation was tested with an electron beam at the SLAC test beam facility. Results from the test beam data will be presented in this talk. The MPC-EX detector is a Si-W preshower extension to the existing Muon Piston Calorimeter (MPC) at PHENIX. Located at forward rapidity, 3 . 1 < | η | < 3 . 8 , the MPC-EX consists of eight layers of alternating W absorber and Si minipad sensors. Covering a large range at forward rapidity makes the MPC-EX and MPC ideal to access low-x partons in the A nucleus of p + A collisions. The neutral pion and direct photon are excellent probes to separate between the initial and final state effects of the pA collisions. Isolating the direct photon signal requires the MPC-EX to be able to distinguish single showers from double showers. The single versus double shower separation was tested with an electron beam at the SLAC test beam facility. Results from the test beam data will be presented in this talk. Norbert Novitzky for PHENIX collaboration.

Real-time implementation of model predictive control on Maricopa-Stanfield irrigation and drainage district's WM canal

USDA-ARS?s Scientific Manuscript database

Water resources are limited in many agricultural areas. One method to improve the effective use of water is to improve delivery service from irrigation canals. This can be done by applying automatic control methods that control the gates in an irrigation canal. The model predictive control MPC is ...

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.

Prompt Photon Measurements with the PHENIX MPC-EX Detector

NASA Astrophysics Data System (ADS)

Lajoie, John

2012-10-01

The MPC-EX detector is a Si-W preshower extension to the existing PHENIX Muon Piston Calorimeter (MPC). The MPC-EX consists of eight layers of alternating W absorber and Si mini-pad sensors. Covering a large pseudorapidity range, 3.1 < |η| < 3.8, the MPC-EX and MPC access low-x partons in the Au nucleus in d+Au collisions through prompt photon measurements. With the addition of the MPC-EX, the neutral pion reconstruction range extends to energies > 80 GeV, a factor of four improvement over current capabilities. Not only will the MPC-EX strengthen PHENIX's existing forward 0̂ and jet measurements, it also provides the necessary 0̂ rejection to make a prompt photon measurement feasible. With this 0̂ rejection, prompt photon yields at high pT, pT> 3 GeV, can be statistically extracted using a double ratio method. The prompt photon RdAu measured with the MPC-EX will quantify the level of gluon shadowing or saturation in the Au nucleus at low-x, x˜ 10-3, with a projected systematic error band a factor of four smaller than current global fits to current measurements.

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

An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems.

PubMed

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.

OPC care-area feedforwarding to MPC

NASA Astrophysics Data System (ADS)

Dillon, Brian; Peng, Yi-Hsing; Hamaji, Masakazu; Tsunoda, Dai; Muramatsu, Tomoyuki; Ohara, Shuichiro; Zou, Yi; Arnoux, Vincent; Baron, Stanislas; Zhang, Xiaolong

2016-10-01

Demand for mask process correction (MPC) is growing for leading-edge process nodes. MPC was originally intended to correct CD linearity for narrow assist features difficult to resolve on a photomask without any correction, but it has been extended to main features as process nodes have been shrinking. As past papers have observed, MPC shows improvements in photomask fidelity. Using advanced shape and dose corrections could give more improvements, especially at line-ends and corners. However, there is a dilemma on using such advanced corrections on full mask level because it increases data volume and run time. In addition, write time on variable shaped beam (VSB) writers also increases as the number of shots increases. Optical proximity correction (OPC) care-area defines circuit design locations that require high mask fidelity under mask writing process variations such as energy fluctuation. It is useful for MPC to switch its correction strategy and permit the use of advanced mask correction techniques in those local care-areas where they provide maximum wafer benefits. The use of mask correction techniques tailored to localized post-OPC design can result in similar desired level of data volume, run time, and write time. ASML Brion and NCS have jointly developed a method to feedforward the care-area information from Tachyon LMC to NDE-MPC to provide real benefit for improving both mask writing and wafer printing quality. This paper explains the detail of OPC care-area feedforwarding to MPC between ASML Brion and NCS, and shows the results. In addition, improvements on mask and wafer simulations are also shown. The results indicate that the worst process variation (PV) bands are reduced up to 37% for a 10nm tech node metal case.

Deep learning and model predictive control for self-tuning mode-locked lasers

NASA Astrophysics Data System (ADS)

Baumeister, Thomas; Brunton, Steven L.; Nathan Kutz, J.

2018-03-01

Self-tuning optical systems are of growing importance in technological applications such as mode-locked fiber lasers. Such self-tuning paradigms require {\\em intelligent} algorithms capable of inferring approximate models of the underlying physics and discovering appropriate control laws in order to maintain robust performance for a given objective. In this work, we demonstrate the first integration of a {\\em deep learning} (DL) architecture with {\\em model predictive control} (MPC) in order to self-tune a mode-locked fiber laser. Not only can our DL-MPC algorithmic architecture approximate the unknown fiber birefringence, it also builds a dynamical model of the laser and appropriate control law for maintaining robust, high-energy pulses despite a stochastically drifting birefringence. We demonstrate the effectiveness of this method on a fiber laser which is mode-locked by nonlinear polarization rotation. The method advocated can be broadly applied to a variety of optical systems that require robust controllers.

Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles

NASA Astrophysics Data System (ADS)

Siampis, Efstathios; Velenis, Efstathios; Longo, Stefano

2015-11-01

In this paper we propose a constrained optimal control architecture for combined velocity, yaw and sideslip regulation for stabilisation of the vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model are used to find reference steady-state cornering conditions and design two model predictive control (MPC) strategies of different levels of fidelity: one that uses a linearised version of the full vehicle model with the rear wheels' torques as the input, and another one that neglects the wheel dynamics and uses the rear wheels' slips as the input instead. After analysing the relative trade-offs between performance and computational effort, we compare the two MPC strategies against each other and against an unconstrained optimal control strategy in Simulink and Carsim environment.

Prompt photon measurements with PHENIX's MPC-EX detector

NASA Astrophysics Data System (ADS)

Campbell, Sarah; PHENIX Collaboration

2013-08-01

The MPC-EX detector is a Si-W preshower extension to the existing Muon Piston Calorimeter (MPC). The MPC-EX consists of eight layers of alternating W absorber and Si mini-pad sensors. Located at forward rapidity, 3.1 < |η| < 3.8, the MPC and MPC-EX will access low-x partons in the Au nucleus in p+Au collisions and high-x partons in the projectile in polarized p+p collisions. With the addition of the MPC-EX, the neutral pion reconstruction energy range extends to the luminosity limit, energies > 80 GeV, a factor of four improvement over current capabilities. Not only will the MPC-EX strengthen PHENIX's existing forward π0 and jet measurements, it will provide sufficient prompt photon and π0 separation to make a prompt photon measurement possible. Prompt photon yields at high pT, pT > 3 GeV/c, can be statistically extracted using the double ratio method. In transversely polarized p+p collisions, the measurement of the prompt photon single spin asymmetry, AN, will resolve the sign discrepancy between the Sivers and twist-3 extractions of AN. In p+Au collisions, the prompt photon RpAu will quantify the level of gluon saturation in the Au nucleus at low-x, x ~ 10-3, with a projected systematic error band a factor of four smaller than EPS09's current allowable range. The MPC-EX detector will expand our understanding of the gluon nuclear parton distribution functions, providing important information about the initial state of heavy ion collisions, and clarify how the valence parton's transverse momentum and spin correlates to the proton spin.

Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming

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 controlmore » (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.« less

Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming

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 controlmore » (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.« less

A Risk-based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

PubMed Central

Zafra-Cabeza, Ascensión; Rivera, Daniel E.; Collins, Linda M.; Ridao, Miguel A.; Camacho, Eduardo F.

2010-01-01

This paper examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based Model Predictive Control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this paper can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm. PMID:21643450

Intelligent Engine Systems: Adaptive Control

NASA Technical Reports Server (NTRS)

Gibson, Nathan

2008-01-01

We have studied the application of the baseline Model Predictive Control (MPC) algorithm to the control of main fuel flow rate (WF36), variable bleed valve (AE24) and variable stator vane (STP25) control of a simulated high-bypass turbofan engine. Using reference trajectories for thrust and turbine inlet temperature (T41) generated by a simulated new engine, we have examined MPC for tracking these two reference outputs while controlling a deteriorated engine. We have examined the results of MPC control for six different transients: two idle-to-takeoff transients at sea level static (SLS) conditions, one takeoff-to-idle transient at SLS, a Bode power command and reverse Bode power command at 20,000 ft/Mach 0.5, and a reverse Bode transient at 35,000 ft/Mach 0.84. For all cases, our primary focus was on the computational effort required by MPC for varying MPC update rates, control horizons, and prediction horizons. We have also considered the effects of these MPC parameters on the performance of the control, with special emphasis on the thrust tracking error, the peak T41, and the sizes of violations of the constraints on the problem, primarily the booster stall margin limit, which for most cases is the lone constraint that is violated with any frequency.

Improving the Forecast Accuracy of an Ocean Observation and Prediction System by Adaptive Control of the Sensor Network

NASA Astrophysics Data System (ADS)

Talukder, A.; Panangadan, A. V.; Blumberg, A. F.; Herrington, T.; Georgas, N.

2008-12-01

The New York Harbor Observation and Prediction System (NYHOPS) is a real-time, estuarine and coastal ocean observing and modeling system for the New York Harbor and surrounding waters. Real-time measurements from in-situ mobile and stationary sensors in the NYHOPS networks are assimilated into marine forecasts in order to reduce the discrepancy with ground truth. The forecasts are obtained from the ECOMSED hydrodynamic model, a shallow water derivative of the Princeton Ocean Model. Currently, all sensors in the NYHOPS system are operated in a fixed mode with uniform sampling rates. This technology infusion effort demonstrates the use of Model Predictive Control (MPC) to autonomously adapt the operation of both mobile and stationary sensors in response to changing events that are -automatically detected from the ECOMSED forecasts. The controller focuses sensing resources on those regions that are expected to be impacted by the detected events. The MPC approach involves formulating the problem of calculating the optimal sensor parameters as a constrained multi-objective optimization problem. We have developed an objective function that takes into account the spatiotemporal relationship of the in-situ sensor locations and the locations of events detected by the model. Experiments in simulation were carried out using data collected during a freshwater flooding event. The location of the resulting freshwater plume was calculated from the corresponding model forecasts and was used by the MPC controller to derive control parameters for the sensing assets. The operational parameters that are controlled include the sampling rates of stationary sensors, paths of unmanned underwater vehicles (UUVs), and data transfer routes between sensors and the central modeling computer. The simulation experiments show that MPC-based sensor control reduces the RMS error in the forecast by a factor of 380% as compared to uniform sampling. The paths of multiple UUVs were simultaneously

Real-time control of combined surface water quantity and quality: polder flushing.

PubMed

Xu, M; van Overloop, P J; van de Giesen, N C; Stelling, G S

2010-01-01

In open water systems, keeping both water depths and water quality at specified values is critical for maintaining a 'healthy' water system. Many systems still require manual operation, at least for water quality management. When applying real-time control, both quantity and quality standards need to be met. In this paper, an artificial polder flushing case is studied. Model Predictive Control (MPC) is developed to control the system. In addition to MPC, a 'forward estimation' procedure is used to acquire water quality predictions for the simplified model used in MPC optimization. In order to illustrate the advantages of MPC, classical control [Proportional-Integral control (PI)] has been developed for comparison in the test case. The results show that both algorithms are able to control the polder flushing process, but MPC is more efficient in functionality and control flexibility.

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.

Prediction based active ramp metering control strategy with mobility and safety assessment

NASA Astrophysics Data System (ADS)

Fang, Jie; Tu, Lili

2018-04-01

Ramp metering is one of the most direct and efficient motorway traffic flow management measures so as to improve traffic conditions. However, owing to short of traffic conditions prediction, in earlier studies, the impact on traffic flow dynamics of the applied RM control was not quantitatively evaluated. In this study, a RM control algorithm adopting Model Predictive Control (MPC) framework to predict and assess future traffic conditions, which taking both the current traffic conditions and the RM-controlled future traffic states into consideration, was presented. The designed RM control algorithm targets at optimizing the network mobility and safety performance. The designed algorithm is evaluated in a field-data-based simulation. Through comparing the presented algorithm controlled scenario with the uncontrolled scenario, it was proved that the proposed RM control algorithm can effectively relieve the congestion of traffic network with no significant compromises in safety aspect.

Application of model predictive control for optimal operation of wind turbines

NASA Astrophysics Data System (ADS)

Yuan, Yuan; Cao, Pei; Tang, J.

2017-04-01

For large-scale wind turbines, reducing maintenance cost is a major challenge. Model predictive control (MPC) is a promising approach to deal with multiple conflicting objectives using the weighed sum approach. In this research, model predictive control method is applied to wind turbine to find an optimal balance between multiple objectives, such as the energy capture, loads on turbine components, and the pitch actuator usage. The actuator constraints are integrated into the objective function at the control design stage. The analysis is carried out in both the partial load region and full load region, and the performances are compared with those of a baseline gain scheduling PID controller. The application of this strategy achieves enhanced balance of component loads, the average power and actuator usages in partial load region.

32 CFR 635.20 - Military Police Codes (MPC).

Code of Federal Regulations, 2010 CFR

2010-07-01

.... (b) Requests for assignment of a MPC will be included in the planning phase of military operations, exercises, or missions when law enforcement operations are anticipated. The request for a MPC will be submitted as soon as circumstances permit, without jeopardizing the military operation to HQDA, Office of...

Constrained off-line synthesis approach of model predictive control for networked control systems with network-induced delays.

PubMed

Tang, Xiaoming; Qu, Hongchun; Wang, Ping; Zhao, Meng

2015-03-01

This paper investigates the off-line synthesis approach of model predictive control (MPC) for a class of networked control systems (NCSs) with network-induced delays. A new augmented model which can be readily applied to time-varying control law, is proposed to describe the NCS where bounded deterministic network-induced delays may occur in both sensor to controller (S-A) and controller to actuator (C-A) links. Based on this augmented model, a sufficient condition of the closed-loop stability is derived by applying the Lyapunov method. The off-line synthesis approach of model predictive control is addressed using the stability results of the system, which explicitly considers the satisfaction of input and state constraints. Numerical example is given to illustrate the effectiveness of the proposed method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Prompt photon measurements with the PHENIX MPC-EX detector

NASA Astrophysics Data System (ADS)

Campbell, Sarah

2013-04-01

The MPC-EX detector is a preshower extension to PHENIX's Muon Piston Calorimeter (MPC). It consists of eight layers of alternating W absorber and Si mini-pad sensors. Located at forward rapidity, 3.1<|η|<3.8, the MPC and MPC-EX access low-x partons in the Au nucleus in p+Au collisions and high-x partons in the projectile in polarized p+p collisions. With the MPC-EX, photon and ^0 separation extends to E>80 GeV, allowing the measurement of prompt photons using the double ratio method. At forward rapidities, prompt photons are dominated by direct photons produced by quark-gluon Compton scattering. In transversely polarized p+p collisions, the prompt photon single spin asymmetry measurement, AN, will resolve the sign discrepancy between the Sivers and twist-3 extractions of AN. In p+Au collisions, the prompt photon RpAu will quantify the level of gluon saturation in the Au nucleus at low-x, 10-3, with a projected systematic error band a factor of four smaller than EPS09's current allowable range. The MPC-EX detector will expand our understanding of gluon nuclear parton distribution functions, providing information about the initial state of heavy ion collisions, and clarify how valence parton's pT and spin correlate to the proton spin.

Probability-based constrained MPC for structured uncertain systems with state and random input delays

NASA Astrophysics Data System (ADS)

Lu, Jianbo; Li, Dewei; Xi, Yugeng

2013-07-01

This article is concerned with probability-based constrained model predictive control (MPC) for systems with both structured uncertainties and time delays, where a random input delay and multiple fixed state delays are included. The process of input delay is governed by a discrete-time finite-state Markov chain. By invoking an appropriate augmented state, the system is transformed into a standard structured uncertain time-delay Markov jump linear system (MJLS). For the resulting system, a multi-step feedback control law is utilised to minimise an upper bound on the expected value of performance objective. The proposed design has been proved to stabilise the closed-loop system in the mean square sense and to guarantee constraints on control inputs and system states. Finally, a numerical example is given to illustrate the proposed results.

Predictive control strategy of a gas turbine for improvement of combined cycle power plant dynamic performance and efficiency.

PubMed

Mohamed, Omar; Wang, Jihong; Khalil, Ashraf; Limhabrash, Marwan

2016-01-01

This paper presents a novel strategy for implementing model predictive control (MPC) to a large gas turbine power plant as a part of our research progress in order to improve plant thermal efficiency and load-frequency control performance. A generalized state space model for a large gas turbine covering the whole steady operational range is designed according to subspace identification method with closed loop data as input to the identification algorithm. Then the model is used in developing a MPC and integrated into the plant existing control strategy. The strategy principle is based on feeding the reference signals of the pilot valve, natural gas valve, and the compressor pressure ratio controller with the optimized decisions given by the MPC instead of direct application of the control signals. If the set points for the compressor controller and turbine valves are sent in a timely manner, there will be more kinetic energy in the plant to release faster responses on the output and the overall system efficiency is improved. Simulation results have illustrated the feasibility of the proposed application that has achieved significant improvement in the frequency variations and load following capability which are also translated to be improvements in the overall combined cycle thermal efficiency of around 1.1 % compared to the existing one.

Model predictive control of a solar-thermal reactor

NASA Astrophysics Data System (ADS)

Saade Saade, Maria Elizabeth

Solar-thermal reactors represent a promising alternative to fossil fuels because they can harvest solar energy and transform it into storable and transportable fuels. The operation of solar-thermal reactors is restricted by the available sunlight and its inherently transient behavior, which affects the performance of the reactors and limits their efficiency. Before solar-thermal reactors can become commercially viable, they need to be able to maintain a continuous high-performance operation, even in the presence of passing clouds. A well-designed control system can preserve product quality and maintain stable product compositions, resulting in a more efficient and cost-effective operation, which can ultimately lead to scale-up and commercialization of solar thermochemical technologies. In this work, we propose a model predictive control (MPC) system for a solar-thermal reactor for the steam-gasification of biomass. The proposed controller aims at rejecting the disturbances in solar irradiation caused by the presence of clouds. A first-principles dynamic model of the process was developed. The model was used to study the dynamic responses of the process variables and to identify a linear time-invariant model used in the MPC algorithm. To provide an estimation of the disturbances for the control algorithm, a one-minute-ahead direct normal irradiance (DNI) predictor was developed. The proposed predictor utilizes information obtained through the analysis of sky images, in combination with current atmospheric measurements, to produce the DNI forecast. In the end, a robust controller was designed capable of rejecting disturbances within the operating region. Extensive simulation experiments showed that the controller outperforms a finely-tuned multi-loop feedback control strategy. The results obtained suggest that our controller is suitable for practical implementation.

Einstein SSS+MPC observations of Seyfert type galaxies

NASA Technical Reports Server (NTRS)

Holt, S. S.; Turner, T. J.; Mushotzky, R. F.; Weaver, K.

1989-01-01

The X-ray spectra of 27 Seyfert galaxies measured with the Solid State Spectrometer (SSS) onboard the Einstein Observatory is investigated. This new investigation features the utilization of simultaneous data from the Monitor Proportional Counter (MPC) and automatic correction for systematic effects in the SSS. The new results are that the best-fit single power law indices agree with those previously reported, but that soft excesses are inferred for at least 20 percent of the measured spectra. The soft excesses are consistent with either an approximately 0.25 keV black body or Fe-L line emission.

Autonomous formation flight of helicopters: Model predictive control approach

NASA Astrophysics Data System (ADS)

Chung, Hoam

Formation flight is the primary movement technique for teams of helicopters. However, the potential for accidents is greatly increased when helicopter teams are required to fly in tight formations and under harsh conditions. This dissertation proposes that the automation of helicopter formations is a realistic solution capable of alleviating risks. Helicopter formation flight operations in battlefield situations are highly dynamic and dangerous, and, therefore, we maintain that both a high-level formation management system and a distributed coordinated control algorithm should be implemented to help ensure safe formations. The starting point for safe autonomous formation flights is to design a distributed control law attenuating external disturbances coming into a formation, so that each vehicle can safely maintain sufficient clearance between it and all other vehicles. While conventional methods are limited to homogeneous formations, our decentralized model predictive control (MPC) approach allows for heterogeneity in a formation. In order to avoid the conservative nature inherent in distributed MPC algorithms, we begin by designing a stable MPC for individual vehicles, and then introducing carefully designed inter-agent coupling terms in a performance index. Thus the proposed algorithm works in a decentralized manner, and can be applied to the problem of helicopter formations comprised of heterogenous vehicles. Individual vehicles in a team may be confronted by various emerging situations that will require the capability for in-flight reconfiguration. We propose the concept of a formation manager to manage separation, join, and synchronization of flight course changes. The formation manager accepts an operator's commands, information from neighboring vehicles, and its own vehicle states. Inside the formation manager, there are multiple modes and complex mode switchings represented as a finite state machine (FSM). Based on the current mode and collected

Studying Cold Nuclear Matter with the MPC-EX of PHENIX

NASA Astrophysics Data System (ADS)

Grau, Nathan; Phenix Collaboration

2017-09-01

Highly asymmetric collision systems, such as d+Au, provide a unique environment to study cold nuclear matter. Potential measurements range from pinning down the modification of the nuclear wave function, i.e. saturation, to studying final state interactions, i.e. energy loss. The PHENIX experiment has enhanced the muon piston calorimeter (MPC) with a silicon-tungsten preshower, the MPC-EX. With its fine segmentation the MPC-EX extends the photon detection capability at 3 < | η | < 3.8. In this talk we review the current status of the detector, its calibration, and its identification capabilities using the 2016 d+Au dataset. We also discuss the specific physics observables the MPC-EX can measure.

Implementation of the MPC and A Operations Monitorying (MOM) System at JSC PO Sevmas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Monogarov, A.; Taranenko, V.; Serov,A

The Material Protection, Control and Accounting (MPC&A) Program has been working since 1994 with nuclear sites in Russia to upgrade the physical protection (PP) and material control and accounting (MC&A) functions at facilities containing weapons usable nuclear material. In early 2001, the MPC&A program initiated the MPC&A Operations Monitoring (MOM) Project to monitor facilities where MPC&A upgrades have been installed to provide increased confidence that personnel are present and vigilant, provide confidence that security procedures are being properly performed and provide additional assurance that nuclear materials have not been stolen. The MOM project began as a pilot project at themore » Moscow State Engineering Physics Institute (MEPhI) and a MOM system was successfully installed in October 2001. Following the success of the MEPhI pilot project, the MPC&A Program expanded the installation of MOM systems to several other Russian facilities, including the JSC 'PO' Sevmash', Severodvinsk, Russia. The MOM system was made operational at Sevmash in September, 2008. This paper will discuss the objectives of the MOM system installed at Sevmash and indicate how the objectives influenced the development of the conceptual design. The paper will also describe activities related to installation of the infrastructure and the MOM system at Sevmash. Experience gained from operation of the system and how the objectives are being met will also be discussed. The paper will describe how the MOM system is used at Sevmash and, in particular, how the data is analyzed. Finally, future activities including potential expansion of the MOM system, operator training, data sharing and analysis, procedure development, repair and maintenance will be included in the paper.« less

Optimal Reservoir Operation using Stochastic Model Predictive Control

NASA Astrophysics Data System (ADS)

Sahu, R.; McLaughlin, D.

2016-12-01

Hydropower operations are typically designed to fulfill contracts negotiated with consumers who need reliable energy supplies, despite uncertainties in reservoir inflows. In addition to providing reliable power the reservoir operator needs to take into account environmental factors such as downstream flooding or compliance with minimum flow requirements. From a dynamical systems perspective, the reservoir operating strategy must cope with conflicting objectives in the presence of random disturbances. In order to achieve optimal performance, the reservoir system needs to continually adapt to disturbances in real time. Model Predictive Control (MPC) is a real-time control technique that adapts by deriving the reservoir release at each decision time from the current state of the system. Here an ensemble-based version of MPC (SMPC) is applied to a generic reservoir to determine both the optimal power contract, considering future inflow uncertainty, and a real-time operating strategy that attempts to satisfy the contract. Contract selection and real-time operation are coupled in an optimization framework that also defines a Pareto trade off between the revenue generated from energy production and the environmental damage resulting from uncontrolled reservoir spills. Further insight is provided by a sensitivity analysis of key parameters specified in the SMPC technique. The results demonstrate that SMPC is suitable for multi-objective planning and associated real-time operation of a wide range of hydropower reservoir systems.

A new model predictive control algorithm by reducing the computing time of cost function minimization for NPC inverter in three-phase power grids.

PubMed

Taheri, Asghar; Zhalebaghi, Mohammad Hadi

2017-11-01

This paper presents a new control strategy based on finite-control-set model-predictive control (FCS-MPC) for Neutral-point-clamped (NPC) three-level converters. Containing some advantages like fast dynamic response, easy inclusion of constraints and simple control loop, makes the FCS-MPC method attractive to use as a switching strategy for converters. However, the large amount of required calculations is a problem in the widespread of this method. In this way, to resolve this problem this paper presents a modified method that effectively reduces the computation load compare with conventional FCS-MPC method and at the same time does not affect on control performance. The proposed method can be used for exchanging power between electrical grid and DC resources by providing active and reactive power compensations. Experiments on three-level converter for three Power Factor Correction (PFC), inductive and capacitive compensation modes verify the good and comparable performance. The results have been simulated using MATLAB/SIMULINK software. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks.

PubMed

Yan, Zheng; Wang, Jun

2014-03-01

This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.

Layer-by-layer assembled magnetic prednisolone microcapsules (MPC) for controlled and targeted drug release at rheumatoid arthritic joints

NASA Astrophysics Data System (ADS)

Prabu, Chakkarapani; Latha, Subbiah; Selvamani, Palanisamy; Ahrentorp, Fredrik; Johansson, Christer; Takeda, Ryoji; Takemura, Yasushi; Ota, Satoshi

2017-04-01

We report here in about the formulation and evaluation of Magnetic Prednisolone Microcapsules (MPC) developed in order to improve the therapeutic efficacy relatively at a low dose than the conventional dosage formulations by means of magnetic drug targeting and thus enhancing bioavailability at the arthritic joints. Prednisolone was loaded to poly (sodium 4-styrenesulfonate) (PSS) doped calcium carbonate microspheres confirmed by the decrease in surface area from 97.48 m2/g to 12.05 of m2/g by BET analysis. Adsorption with oppositely charged polyelectrolytes incorporated with iron oxide nanoparticles was confirmed through zeta analysis. Removal of calcium carbonate core yielded MPC with particle size of 3.48 μm, zeta potential of +29.7 mV was evaluated for its magnetic properties. Functional integrity of MPC was confirmed through FT-IR spectrum. Stability studies were performed at 25 °C±65% relative humidity for 60 days showed no considerable changes. Further the encapsulation efficiency of 63%, loading capacity of 18.2% and drug release of 88.3% for 36 h and its kinetics were also reported. The observed results justify the suitability of MPC for possible applications in the magnetic drug targeting for efficient therapy of rheumatoid arthritis.

Evaluation of the TrueBeam machine performance check (MPC) beam constancy checks for flattened and flattening filter-free (FFF) photon beams.

PubMed

Barnes, Michael P; Greer, Peter B

2017-01-01

Machine Performance Check (MPC) is an automated and integrated image-based tool for verification of beam and geometric performance of the TrueBeam linac. The aims of the study were to evaluate the MPC beam performance tests against current daily quality assurance (QA) methods, to compare MPC performance against more accurate monthly QA tests and to test the sensitivity of MPC to changes in beam performance. The MPC beam constancy checks test the beam output, uniformity, and beam center against the user defined baseline. MPC was run daily over a period of 5 months (n = 115) in parallel with the Daily QA3 device. Additionally, IC Profiler, in-house EPID tests, and ion chamber measurements were performed biweekly and results presented in a form directly comparable to MPC. The sensitivity of MPC was investigated using controlled adjustments of output, beam angle, and beam position steering. Over the period, MPC output agreed with ion chamber to within 0.6%. For an output adjustment of 1.2%, MPC was found to agree with ion chamber to within 0.17%. MPC beam center was found to agree with the in-house EPID method within 0.1 mm. A focal spot position adjustment of 0.4 mm (at isocenter) was measured with MPC beam center to within 0.01 mm. An average systematic offset of 0.5% was measured in the MPC uniformity and agreement of MPC uniformity with symmetry measurements was found to be within 0.9% for all beams. MPC uniformity detected a change in beam symmetry of 1.5% to within 0.3% and 0.9% of IC Profiler for flattened and FFF beams, respectively. © 2016 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

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. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

MPC and ALI: their basis and their comparison

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kennedy, W.E. Jr.; Watson, E.C.

Radiation protection regulations in the United States have evolved from the recommendations of the International Commission on Radiological Protection (ICRP) and the National Council on Radiation Protection and Measurements (NCRP). In 1959, the ICRP issued Publication 2 which contained specific recommendations on dose rate limits, permissible body burdens, metabolic data for radionuclides, and maximum permissible concentrations (MPC) in air or water. Over the next 20 years, new information became available concerning the effects of radiation, the uptake and retention of radionuclides, and the radioactive decay schemes of parent radionuclides. To include this newer information, the ICRP issued Publication 30 inmore » 1978 to supersede Publication 2. One of the secondary limits defined in Publication 30 is the annual limit of intake (ALI). Radionuclide specific ALI values are intended to replace MPC values in determining whether or not ambient air and water concentrations are sufficiently low to maintain the dose to workers within accepted dose rate limits. In this paper, we discuss the derivation of MPC and ALI values, compare inhalation committed dose equivalent factors derived from ICRP Publications 2 and 30, and discuss the practical implications of using either MPC or ALI in determining compliance with occupational exposure limits. 6 references.« less

Implementation of the MPC and A Operations Monitoring (MOM) System at IRT-T FSRE Nuclear Power Institute (NPI)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sitdikov,I.; Zenkov, A.; Tsibulnikov, Y.

The Material Protection, Control and Accounting (MPC&A) Program has been working since 1994 with nuclear sites in Russia to upgrade the physical protection (PP) and material control and accounting (MC&A) functions at facilities containing weapons usable nuclear material. In early 2001, the MPC&A program initiated the MPC&A Operations Monitoring (MOM) Project to monitor facilities where MPC&A upgrades have been installed to provide increased confidence that personnel are present and vigilant, provide confidence that security procedures are being properly performed and provide additional assurance that nuclear materials have not been stolen. The MOM project began as a pilot project at themore » Moscow State Engineering Physics Institute (MEPhI) and a MOM system was successfully installed in October 2001. Following the success of the MEPhI pilot project, the MPC&A Program expanded the installation of MOM systems to several other Russian facilities, including the Nuclear Physics Institute (NPI) in Tomsk. The MOM system was made operational at NPI in October 2004. This paper is focused on the experience gained from operation of this system and the objectives of the MOM system. The paper also describes how the MOM system is used at NPI and, in particular, how the data is analyzed. Finally, potential expansion of the MOM system at NPI is described.« less

Model predictive control of a lean-burn gasoline engine coupled with a passive selective catalytic reduction system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Pingen; Lin, Qinghua; Prikhodko, Vitaly Y.

Lean-burn gasoline engines have demonstrated 10–20% engine efficiency gain over stoichiometric engines and are widely considered as a promising technology for meeting the 54.5 miles-per-gallon (mpg) Corporate Average Fuel Economy standard by 2025. Nevertheless, NOx emissions control for lean-burn gasoline for meeting the stringent EPA Tier 3 emission standards has been one of the main challenges towards the commercialization of highly-efficient lean-burn gasoline engines in the United States. Passive selective catalytic reduction (SCR) systems, which consist of a three-way catalyst and SCR, have demonstrated great potentials of effectively reducing NOx emissions for lean gasoline engines but may cause significant fuelmore » penalty due to ammonia generation via rich engine combustion. The purpose of this study is to develop a model-predictive control (MPC) scheme for a lean-burn gasoline engine coupled with a passive SCR system to minimize the fuel penalty associated with passive SCR operation while satisfying stringent NOx and NH3 emissions requirements. Simulation results demonstrate that the MPC-based control can reduce the fuel penalty by 47.7% in a simulated US06 cycle and 32.0% in a simulated UDDS cycle, compared to the baseline control, while achieving over 96% deNOx efficiency and less than 15 ppm tailpipe ammonia slip. The proposed MPC control can potentially enable high engine efficiency gain for highly-efficient lean-burn gasoline engine while meeting the stringent EPA Tier 3 emission standards.« less

A nonlinear model predictive control formulation for obstacle avoidance in high-speed autonomous ground vehicles in unstructured environments

NASA Astrophysics Data System (ADS)

Liu, Jiechao; Jayakumar, Paramsothy; Stein, Jeffrey L.; Ersal, Tulga

2018-06-01

This paper presents a nonlinear model predictive control (MPC) formulation for obstacle avoidance in high-speed, large-size autono-mous ground vehicles (AGVs) with high centre of gravity (CoG) that operate in unstructured environments, such as military vehicles. The term 'unstructured' in this context denotes that there are no lanes or traffic rules to follow. Existing MPC formulations for passenger vehicles in structured environments do not readily apply to this context. Thus, a new nonlinear MPC formulation is developed to navigate an AGV from its initial position to a target position at high-speed safely. First, a new cost function formulation is used that aims to find the shortest path to the target position, since no reference trajectory exists in unstructured environments. Second, a region partitioning approach is used in conjunction with a multi-phase optimal control formulation to accommodate the complicated forms the obstacle-free region can assume due to the presence of multiple obstacles in the prediction horizon in an unstructured environment. Third, the no-wheel-lift-off condition, which is the major dynamical safety concern for high-speed, high-CoG AGVs, is ensured by limiting the steering angle within a range obtained offline using a 14 degrees-of-freedom vehicle dynamics model. Thus, a safe, high-speed navigation is enabled in an unstructured environment. Simulations of an AGV approaching multiple obstacles are provided to demonstrate the effectiveness of the algorithm.

Dynamic Simulation of Human Gait Model With Predictive Capability.

PubMed

Sun, Jinming; Wu, Shaoli; Voglewede, Philip A

2018-03-01

In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.

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.

STS-35 MS Hoffman operates ASTRO-1 MPC on OV-102's aft flight deck

NASA Image and Video Library

1990-12-10

STS035-12-015 (2-11 Dec 1990) --- Astronaut Jeffrey A. Hoffman, STS 35 mission specialist, uses a manual pointing controller (MPC) for the Astro-1 mission's Instrument Pointing System (IPS). By using the MPC, Hoffman and other crewmembers on Columbia's aft flight deck, were able to command the IPS, located in the cargo bay, to record astronomical data. Hoffman is serving the "Blue" shift which complemented the currently sleeping "Red" shift of crewmembers as the mission collected scientific data on a 24-hour basis. The scene was photographed with a 35mm camera.

A novel auto-tuning PID control mechanism for nonlinear systems.

PubMed

Cetin, Meric; Iplikci, Serdar

2015-09-01

In this paper, a novel Runge-Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

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.

Model Predictive Control-based gait pattern generation for wearable exoskeletons.

PubMed

Wang, Letian; van Asseldonk, Edwin H F; van der Kooij, Herman

2011-01-01

This paper introduces a new method for controlling wearable exoskeletons that do not need predefined joint trajectories. Instead, it only needs basic gait descriptors such as step length, swing duration, and walking speed. End point Model Predictive Control (MPC) is used to generate the online joint trajectories based on these gait parameters. Real-time ability and control performance of the method during the swing phase of gait cycle is studied in this paper. Experiments are performed by helping a human subject swing his leg with different patterns in the LOPES gait trainer. Results show that the method is able to assist subjects to make steps with different step length and step duration without predefined joint trajectories and is fast enough for real-time implementation. Future study of the method will focus on controlling the exoskeletons in the entire gait cycle. © 2011 IEEE

Effect of pH adjustment, homogenization and diafiltration on physicochemical, reconstitution, functional and rheological properties of medium protein milk protein concentrates (MPC70).

PubMed

Meena, Ganga Sahay; Singh, Ashish Kumar; Gupta, Vijay Kumar; Borad, Sanket; Arora, Sumit; Tomar, Sudhir Kumar

2018-04-01

Poor solubility is the major limiting factor in commercial applications of milk protein concentrates (MPC) powders. Retentate treatments such as pH adjustment using disodium phosphate (Na 2 HPO 4 ), also responsible for calcium chelation with homogenization and; its diafiltration with 150 mM NaCl solution were hypothesized to improve the functional properties of treated MPC70 powders. These treatments significantly improved the solubility, heat stability, water binding, dispersibility, bulk density, flowability, buffer index, foaming and emulsifying capacity of treated powders over control. Rheological behaviour of reconstituted MPC solutions was best explained by Herschel Bulkley model. Compared to rough, large globular structures with dents in control; majorly intact, separate, smaller particles of smooth surface, without any aggregation were observed in SEM micrograph of treated powders. Applied treatments are easy, cost-effective and capable to improve functional properties of treated powders that could replace control MPC70 powder in various food applications where protein functionality is of prime importance.

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. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A Framework for Information Theoretic Cooperative Sensing and Predictive Control

DTIC Science & Technology

2012-09-11

Miroslav Barić and Francesco Borelli , Decentralized Robust Control Invariance for a Network of Integrators, Proceeding of American Control...from http: //www.mpc.berkeley.edu. P4 Miroslav Barić and Francesco Borelli , Distributed Averaging with Flow Constraints, Proceeding of American Control

Establishment of mitochondrial pyruvate carrier 1 (MPC1) gene knockout mice with preliminary gene function analyses

PubMed Central

Li, Xiaoli; Li, Yaqing; Han, Gaoyang; Li, Xiaoran; Ji, Yasai; Fan, Zhirui; Zhong, Yali; Cao, Jing; Zhao, Jing; Mariusz, Goscinski; Zhang, Mingzhi; Wen, Jianguo; Nesland, Jahn M.; Suo, Zhenhe

2016-01-01

Pyruvate plays a critical role in the mitochondrial tricarboxylic acid (TCA) cycle, and it is the center product for the synthesis of amino acids, carbohydrates and fatty acids. Pyruvate transported across the inner mitochondrial membrane appears to be essential in anabolic and catabolic intermediary metabolism. The mitochondrial pyruvate carrier (MPC) mounted in the inner membrane of mitochondria serves as the channel to facilitate pyruvate permeating. In mammals, the MPC is formed by two paralogous subunits, MPC1 and MPC2. It is known that complete ablation of MPC2 in mice causes death on the 11th or 12th day of the embryonic period. However, MPC1 deletion and the knowledge of gene function in vivo are lacking. Using the new technology of gene manipulation known as Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated 9 (CRISPR/Cas9) systems, we gained stable MPC1 gene heterozygous mutation mice models, and the heterozygous mutations could be stably maintained in their offsprings. Only one line with homozygous 27 bases deletion in the first exon was established, but no offsprings could be obtained after four months of mating experiments, indicating infertility of the mice with such homozygous deletion. The other line of MPC1 knockout (KO) mice was only heterozygous, which mutated in the first exon with a terminator shortly afterwards. These two lines of MPC1 KO mice showed lower fertility and significantly higher bodyweight in the females. We concluded that heterozygous MPC1 KO weakens fertility and influences the metabolism of glucose and fatty acid and bodyweight in mice. PMID:27835892

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.

The Infrastructure Necessary to Support a Sustainable Material Protection, Control and Accounting (MPC&A) Program in Russia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bachner, Katherine M.; Mladineo, Stephen V.

The NNSA Material Protection, Control, and Accounting (MPC&A) program has been engaged for fifteen years in upgrading the security of nuclear materials in Russia. Part of the effort has been to establish the conditions necessary to ensure the long-term sustainability of nuclear security. A sustainable program of nuclear security requires the creation of an indigenous infrastructure, starting with sustained high level government commitment. This includes organizational development, training, maintenance, regulations, inspections, and a strong nuclear security culture. The provision of modern physical protection, control, and accounting equipment to the Russian Federation alone is not sufficient. Comprehensive infrastructure projects support themore » Russian Federation's ability to maintain the risk reduction achieved through upgrades to the equipment. To illustrate the contributions to security, and challenges of implementation, this paper discusses the history and next steps for an indigenous Tamper Indication Device (TID) program, and a Radiation Portal Monitoring (RPM) program.« less

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.

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.

Advanced modelling, monitoring, and process control of bioconversion systems

NASA Astrophysics Data System (ADS)

Schmitt, Elliott C.

fermentation with diauxic growth. Model predictive control (MPC), an advanced process control strategy, is capable of utilizing nonlinear models and sensor feedback to provide optimal input while ensuring critical process constraints are met. Using the microorganism Saccharomyces cerevisiae, a commonly used microorganism for biofuel production, and work performed with M. thermoacetica, a nonlinear MPC was implemented on a continuous membrane cell-recycle bioreactor (MCRB) for the conversion of glucose to ethanol. The dilution rate was used to control the ethanol productivity of the system will maintaining total substrate conversion above the constraint of 98%. PLS multivariate models for glucose (RMSEP 1.5 g L-1) and ethanol (RMSEP 0.4 g L-1) were robust in predicting concentrations and a mechanistic kinetic model built accurately predicted continuous fermentation behavior. A setpoint trajectory, ranging from 2 - 4.5 g L-1 h-1 for productivity was closely tracked by the fermentation system using Raman measurements and an extended Kalman filter to estimate biomass concentrations. Overall, this work was able to demonstrate an effective approach for real-time monitoring and control of a complex fermentation system.

Closed-Loop Control and Advisory Mode Evaluation of an Artificial Pancreatic β Cell: Use of Proportional–Integral–Derivative Equivalent Model-Based Controllers

PubMed Central

Percival, Matthew W.; Zisser, Howard; Jovanovič, Lois; Doyle, Francis J.

2008-01-01

Background Using currently available technology, it is possible to apply modern control theory to produce a closed-loop artificial β cell. Novel use of established control techniques would improve glycemic control, thereby reducing the complications of diabetes. Two popular controller structures, proportional–integral–derivative (PID) and model predictive control (MPC), are compared first in a theoretical sense and then in two applications. Methods The Bergman model is transformed for use in a PID equivalent model-based controller. The internal model control (IMC) structure, which makes explicit use of the model, is compared with the PID controller structure in the transfer function domain. An MPC controller is then developed as an optimization problem with restrictions on its tuning parameters and is shown to be equivalent to an IMC controller. The controllers are tuned for equivalent performance and evaluated in a simulation study as a closed-loop controller and in an advisory mode scenario on retrospective clinical data. Results Theoretical development shows conditions under which PID and MPC controllers produce equivalent output via IMC. The simulation study showed that the single tuning parameter for the equivalent controllers relates directly to the closed-loop speed of response and robustness, an important result considering system uncertainty. The risk metric allowed easy identification of instances of inadequate control. Results of the advisory mode simulation showed that suitable tuning produces consistently appropriate delivery recommendations. Conclusion The conditions under which PID and MPC are equivalent have been derived. The MPC framework is more suitable given the extensions necessary for a fully closed-loop artificial β cell, such as consideration of controller constraints. Formulation of the control problem in risk space is attractive, as it explicitly addresses the asymmetry of the problem; this is done easily with MPC. PMID:19885240

How accurate is our clinical prediction of "minimal prostate cancer"?

PubMed

Leibovici, Dan; Shikanov, Sergey; Gofrit, Ofer N; Zagaja, Gregory P; Shilo, Yaniv; Shalhav, Arieh L

2013-07-01

Recommendations for active surveillance versus immediate treatment for low risk prostate cancer are based on biopsy and clinical data, assuming that a low volume of well-differentiated carcinoma will be associated with a low progression risk. However, the accuracy of clinical prediction of minimal prostate cancer (MPC) is unclear. To define preoperative predictors for MPC in prostatectomy specimens and to examine the accuracy of such prediction. Data collected on 1526 consecutive radical prostatectomy patients operated in a single center between 2003 and 2008 included: age, body mass index, preoperative prostate-specific antigen level, biopsy Gleason score, clinical stage, percentage of positive biopsy cores, and maximal core length (MCL) involvement. MPC was defined as < 5% of prostate volume involvement with organ-confined Gleason score < or = 6. Univariate and multivariate logistic regression analyses were used to define independent predictors of minimal disease. Classification and Regression Tree (CART) analysis was used to define cutoff values for the predictors and measure the accuracy of prediction. MPC was found in 241 patients (15.8%). Clinical stage, biopsy Gleason's score, percent of positive biopsy cores, and maximal involved core length were associated with minimal disease (OR 0.42, 0.1, 0.92, and 0.9, respectively). Independent predictors of MPC included: biopsy Gleason score, percent of positive cores and MCL (OR 0.21, 095 and 0.95, respectively). CART showed that when the MCL exceeded 11.5%, the likelihood of MPC was 3.8%. Conversely, when applying the most favorable preoperative conditions (Gleason < or = 6, < 20% positive cores, MCL < or = 11.5%) the chance of minimal disease was 41%. Biopsy Gleason score, the percent of positive cores and MCL are independently associated with MPC. While preoperative prediction of significant prostate cancer was accurate, clinical prediction of MPC was incorrect 59% of the time. Caution is necessary when

Decreased Mitochondrial Pyruvate Transport Activity in the Diabetic Heart: ROLE OF MITOCHONDRIAL PYRUVATE CARRIER 2 (MPC2) ACETYLATION.

PubMed

Vadvalkar, Shraddha S; Matsuzaki, Satoshi; Eyster, Craig A; Giorgione, Jennifer R; Bockus, Lee B; Kinter, Caroline S; Kinter, Michael; Humphries, Kenneth M

2017-03-17

Alterations in mitochondrial function contribute to diabetic cardiomyopathy. We have previously shown that heart mitochondrial proteins are hyperacetylated in OVE26 mice, a transgenic model of type 1 diabetes. However, the universality of this modification and its functional consequences are not well established. In this study, we demonstrate that Akita type 1 diabetic mice exhibit hyperacetylation. Functionally, isolated Akita heart mitochondria have significantly impaired maximal (state 3) respiration with physiological pyruvate (0.1 mm) but not with 1.0 mm pyruvate. In contrast, pyruvate dehydrogenase activity is significantly decreased regardless of the pyruvate concentration. We found that there is a 70% decrease in the rate of pyruvate transport in Akita heart mitochondria but no decrease in the mitochondrial pyruvate carriers 1 and 2 (MPC1 and MPC2). The potential role of hyperacetylation in mediating this impaired pyruvate uptake was examined. The treatment of control mitochondria with the acetylating agent acetic anhydride inhibits pyruvate uptake and pyruvate-supported respiration in a similar manner to the pyruvate transport inhibitor α-cyano-4-hydroxycinnamate. A mass spectrometry selective reactive monitoring assay was developed and used to determine that acetylation of lysines 19 and 26 of MPC2 is enhanced in Akita heart mitochondria. Expression of a double acetylation mimic of MPC2 (K19Q/K26Q) in H9c2 cells was sufficient to decrease the maximal cellular oxygen consumption rate. This study supports the conclusion that deficient pyruvate transport activity, mediated in part by acetylation of MPC2, is a contributor to metabolic inflexibility in the diabetic heart. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Short communication: Effect of storage temperature on the solubility of milk protein concentrate 80 (MPC80) treated with NaCl or KCl.

PubMed

Sikand, V; Tong, P S; Walker, J; Wang, T; Rodriguez-Saona, L E

2016-03-01

A previous study in our laboratory showed that addition of 150 mM NaCl or KCl into diafiltration water improved the solubility of freshly made milk protein concentrate 80 (MPC80). In the present study, the objectives were (1) to evaluate the solubility of NaCl- or KCl-treated MPC80 samples kept at varying temperatures and then stored for extensive periods at room temperature (21 °C ± 1 °C); and (2) to determine if MPC80 samples stored at different temperatures and protein conformation can be grouped or categorized together. Freshly manufactured MPC80 samples were untreated (control), processed with NaCl, or processed with KCl. One set of sample bags was stored at 4 °C; second and third sets of bags were kept at 25 °C and 55 °C for 1 mo (31 d) and then transferred to room temperature (21 °C ± 1 °C) storage conditions for 1 yr (365 d). Samples were tested for nitrogen solubility index (NSI) and for protein changes by Fourier-transform infrared (FTIR) spectroscopy. Analysis of variance results for NSI showed 2 significantly different groupings of MPC80 samples. The more soluble group contained samples treated with NaCl or KCl and stored at either 4 °C or 25 °C. These samples had mean NSI >97.5%. The less soluble groups contained all control samples, regardless of storage temperature, and NaCl- or KCl-treated samples stored at 55 °C. These samples had mean NSI from 39.5 to 58%. Within each of these groups (more soluble and less soluble), no significant differences in solubility were detected. Pattern recognition analysis by soft independent modeling of class analogy (SIMCA) was used to assess protein changes during storage by monitoring the amide I and amide II (1,700(-1) to 1,300 cm(-1)) regions. Dominant bands were observed at 1,385 cm(-1) for control, 1,551 cm(-1) for KCl-treated samples, and 1,694 cm(-1) for NaCl-treated samples. Moreover, SIMCA clustered the MPC80 samples stored at 4 °C separately from samples stored at 25 °C and 55 °C. This study

SU-F-T-480: Evaluation of the Role of Varian Machine Performance Check (MPC) in Our Daily QA Routine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Juneja, B; Gao, S; Balter, P

2016-06-15

Purpose: (A) To assess the role of Varian MPC in our daily QA routine, and (B) evaluate the accuracy and precision of MPC. Methods: The MPC was performed weekly, for five months, on a Varian TrueBeam for five photon (6x, 10x, 15x, 6xFFF, and 10xFFF) and electron (6e, 9e, 12e, 16e, and 20e) energies. Output results were compared to those determined with an ionization chamber (TN30001, PTW-Freiburg) in plastic and a daily check device (DQA3, Sun Nuclear). Consistency of the Mechanical measurements over five months was analyzed and compared to monthly IsoCal results. Results: The MPC randomly showed large deviationsmore » (3–7%) that disappeared upon reacquisition. The MPC output closely matched monthly ion chamber and DQA3 measurements. The maximum and mean absolute difference between monthly and MPC was 1.18% and 0.28±0.21% for all energies. The maximum and mean absolute difference between DQA3 and MPC was 3.26% and 0.85±0.61%. The results suggest the MPC is comparable to the DQA3 for measuring output. The DQA3 provides wedge output, flatness, symmetry, and energy constancy checks, which are missing from the current implementation of the MPC. However, the MPC provides additional mechanical tests, such as size of the radiation isocenter (0.33±0.02 mm) and its coincidence with MV and kV isocenters (0.17±0.05 and 0.21±0.03 mm). It also provides positional accuracy of individual jaws (maximum σ, 0.33mm), all the MLC leaves (0.08mm), gantry (0.05°) and collimator (0.13°) rotation angles, and couch positioning (0.11mm) accuracy. MPC mechanical tests could replace our current daily on-board imaging QA routine and provide some additional QA not currently performed. Conclusion: MPC has the potential to be a valuable tool that facilitates reliable daily QA including many mechanical tests that are not currently performed. This system can add to our daily QA, but further development would be needed to fully replace our current Daily QA device.« less

Fault diagnosis and fault-tolerant finite control set-model predictive control of a multiphase voltage-source inverter supplying BLDC motor.

PubMed

Salehifar, Mehdi; Moreno-Equilaz, Manuel

2016-01-01

Due to its fault tolerance, a multiphase brushless direct current (BLDC) motor can meet high reliability demand for application in electric vehicles. The voltage-source inverter (VSI) supplying the motor is subjected to open circuit faults. Therefore, it is necessary to design a fault-tolerant (FT) control algorithm with an embedded fault diagnosis (FD) block. In this paper, finite control set-model predictive control (FCS-MPC) is developed to implement the fault-tolerant control algorithm of a five-phase BLDC motor. The developed control method is fast, simple, and flexible. A FD method based on available information from the control block is proposed; this method is simple, robust to common transients in motor and able to localize multiple open circuit faults. The proposed FD and FT control algorithm are embedded in a five-phase BLDC motor drive. In order to validate the theory presented, simulation and experimental results are conducted on a five-phase two-level VSI supplying a five-phase BLDC motor. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A study on model fidelity for model predictive control-based obstacle avoidance in high-speed autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Liu, Jiechao; Jayakumar, Paramsothy; Stein, Jeffrey L.; Ersal, Tulga

2016-11-01

This paper investigates the level of model fidelity needed in order for a model predictive control (MPC)-based obstacle avoidance algorithm to be able to safely and quickly avoid obstacles even when the vehicle is close to its dynamic limits. The context of this work is large autonomous ground vehicles that manoeuvre at high speed within unknown, unstructured, flat environments and have significant vehicle dynamics-related constraints. Five different representations of vehicle dynamics models are considered: four variations of the two degrees-of-freedom (DoF) representation as lower fidelity models and a fourteen DoF representation with combined-slip Magic Formula tyre model as a higher fidelity model. It is concluded that the two DoF representation that accounts for tyre nonlinearities and longitudinal load transfer is necessary for the MPC-based obstacle avoidance algorithm in order to operate the vehicle at its limits within an environment that includes large obstacles. For less challenging environments, however, the two DoF representation with linear tyre model and constant axle loads is sufficient.

Model predictive control of an air suspension system with damping multi-mode switching damper based on hybrid model

NASA Astrophysics Data System (ADS)

Sun, Xiaoqiang; Yuan, Chaochun; Cai, Yingfeng; Wang, Shaohua; Chen, Long

2017-09-01

This paper presents the hybrid modeling and the model predictive control of an air suspension system with damping multi-mode switching damper. Unlike traditional damper with continuously adjustable damping, in this study, a new damper with four discrete damping modes is applied to vehicle semi-active air suspension. The new damper can achieve different damping modes by just controlling the on-off statuses of two solenoid valves, which makes its damping adjustment more efficient and more reliable. However, since the damping mode switching induces different modes of operation, the air suspension system with the new damper poses challenging hybrid control problem. To model both the continuous/discrete dynamics and the switching between different damping modes, the framework of mixed logical dynamical (MLD) systems is used to establish the system hybrid model. Based on the resulting hybrid dynamical model, the system control problem is recast as a model predictive control (MPC) problem, which allows us to optimize the switching sequences of the damping modes by taking into account the suspension performance requirements. Numerical simulations results demonstrate the efficacy of the proposed control method finally.

Model predictive control-based dynamic coordinate strategy for hydraulic hub-motor auxiliary system of a heavy commercial vehicle

NASA Astrophysics Data System (ADS)

Zeng, Xiaohua; Li, Guanghan; Yin, Guodong; Song, Dafeng; Li, Sheng; Yang, Nannan

2018-02-01

Equipping a hydraulic hub-motor auxiliary system (HHMAS), which mainly consists of a hydraulic variable pump, a hydraulic hub-motor, a hydraulic valve block and hydraulic accumulators, with part-time all-wheel-drive functions improves the power performance and fuel economy of heavy commercial vehicles. The coordinated control problem that occurs when HHMAS operates in the auxiliary drive mode is addressed in this paper; the solution to this problem is the key to the maximization of HHMAS. To achieve a reasonable distribution of the engine power between mechanical and hydraulic paths, a nonlinear control scheme based on model predictive control (MPC) is investigated. First, a nonlinear model of HHMAS with vehicle dynamics and tire slip characteristics is built, and a controller-design-oriented model is simplified. Then, a steady-state feedforward + dynamic MPC feedback controller (FMPC) is designed to calculate the control input sequence of engine torque and hydraulic variable pump displacement. Finally, the controller is tested in the MATLAB/Simulink and AMESim co-simulation platform and the hardware-in-the-loop experiment platform, and its performance is compared with that of the existing proportional-integral-derivative controller and the feedforward controller under the same conditions. Simulation results show that the designed FMPC has the best performance, and control performance can be guaranteed in a real-time environment. Compared with the tracking control error of the feedforward controller, that of the designed FMPC is decreased by 85% and the traction efficiency performance is improved by 23% under a low-friction-surface condition. Moreover, under common road conditions for heavy commercial vehicles, the traction force can increase up to 13.4-15.6%.

Model-based MPC enables curvilinear ILT using either VSB or multi-beam mask writers

NASA Astrophysics Data System (ADS)

Pang, Linyong; Takatsukasa, Yutetsu; Hara, Daisuke; Pomerantsev, Michael; Su, Bo; Fujimura, Aki

2017-07-01

Inverse Lithography Technology (ILT) is becoming the choice for Optical Proximity Correction (OPC) of advanced technology nodes in IC design and production. Multi-beam mask writers promise significant mask writing time reduction for complex ILT style masks. Before multi-beam mask writers become the main stream working tools in mask production, VSB writers will continue to be the tool of choice to write both curvilinear ILT and Manhattanized ILT masks. To enable VSB mask writers for complex ILT style masks, model-based mask process correction (MB-MPC) is required to do the following: 1). Make reasonable corrections for complex edges for those features that exhibit relatively large deviations from both curvilinear ILT and Manhattanized ILT designs. 2). Control and manage both Edge Placement Errors (EPE) and shot count. 3. Assist in easing the migration to future multi-beam mask writer and serve as an effective backup solution during the transition. In this paper, a solution meeting all those requirements, MB-MPC with GPU acceleration, will be presented. One model calibration per process allows accurate correction regardless of the target mask writer.

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

Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

NASA Astrophysics Data System (ADS)

Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

2016-09-01

The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

Physical, thermal and mechanical study of MPC formulated with LG-MgO incorporating Phase Change Materials as admixture

NASA Astrophysics Data System (ADS)

Maldonado-Alameda, A.; Lacasta, A. M.; Giro-Paloma, J.; Chimenos, J. M.; Formosa, J.

2017-10-01

The high environmental impact generated by using of Ordinary Portland Cement (OPC) has lead to the search for alternative materials in the field of civil and building engineering. In addition, there is a tendency to develop cements from industrial by-products, thus reducing pollution and emissions generated by their production. One of the best positioned cements to compete with OPC is Magnesium Phosphate Cement (MPC). The present work studies different dosages of MPC mortars formulated with low-grade MgO by-product (sustainable MPC) incorporating Microencapsulated Phase Change Materials (MPCM) and air entraining additive (AEA) as admixtures (Thermal Sustainable MPC) to improve the thermal behaviour of the material. The aim is developed a new eco-friendly material that leads to reducing energy consumption in buildings. The study is focused on the physical, thermal, and mechanical characterization of TS-MPC mortars to assess their potential use as a thermal prefabricated panel. The results allow to relate the amount of the MPCM and the additive percentage with the thermal and mechanical properties of the TS- MPC. Furthermore, is important to highlight the influence of MPCM not only in the thermal behaviour but also on the increase of the porosity. The experimental results show that the addition of both additives contributes substantially to the improvement of the thermal behaviour of the mortars and converts them on a suitable material to reduce thermal oscillations in buildings.

Improved LTVMPC design for steering control of autonomous vehicle

NASA Astrophysics Data System (ADS)

Velhal, Shridhar; Thomas, Susy

2017-01-01

An improved linear time varying model predictive control for steering control of autonomous vehicle running on slippery road is presented. Control strategy is designed such that the vehicle will follow the predefined trajectory with highest possible entry speed. In linear time varying model predictive control, nonlinear vehicle model is successively linearized at each sampling instant. This linear time varying model is used to design MPC which will predict the future horizon. By incorporating predicted input horizon in each successive linearization the effectiveness of controller has been improved. The tracking performance using steering with front wheel and braking at four wheels are presented to illustrate the effectiveness of the proposed method.

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. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Synthesis, characterisation, and in vitro cellular uptake kinetics of nanoprecipitated poly(2-methacryloyloxyethyl phosphorylcholine)- b-poly(2-(diisopropylamino)ethyl methacrylate) (MPC-DPA) polymeric nanoparticle micelles for nanomedicine applications

NASA Astrophysics Data System (ADS)

Salvage, Jonathan P.; Smith, Tia; Lu, Tao; Sanghera, Amendeep; Standen, Guy; Tang, Yiqing; Lewis, Andrew L.

2016-10-01

Nanoscience offers the potential for great advances in medical technology and therapies in the form of nanomedicine. As such, developing controllable, predictable, and effective, nanoparticle-based therapeutic systems remains a significant challenge. Many polymer-based nanoparticle systems have been reported to date, but few harness materials with accepted biocompatibility. Phosphorylcholine (PC) based biomimetic materials have a long history of successful translation into effective commercial medical technologies. This study investigated the synthesis, characterisation, nanoprecipitation, and in vitro cellular uptake kinetics of PC-based polymeric nanoparticle micelles (PNM) formed by the biocompatible and pH responsive block copolymer poly(2-methacryloyloxyethyl phosphorylcholine)- b-poly(2-(diisopropylamino)ethyl methacrylate) (MPC-DPA). Atom transfer radical polymerisation (ATRP), and gel permeation chromatography (GPC) were used to synthesise and characterise the well-defined MPC100-DPA100 polymer, revealing organic GPC, using evaporative light scatter detection, to be more accurate than aqueous GPC for this application. Subsequent nanoprecipitation investigations utilising photon correlation spectroscopy (PCS) revealed PNM size increased with polymer concentration, and conferred Cryo-stability. PNM diameters ranged from circa 64-69 nm, and increased upon hydrophobic compound loading, circa 65-71 nm, with loading efficiencies of circa 60 % achieved, whilst remaining monodisperse. In vitro studies demonstrated that the PNM were of low cellular toxicity, with colony formation and MTT assays, utilising V79 and 3T3 cells, yielding comparable results. Investigation of the in vitro cellular uptake kinetics revealed rapid, 1 h, cellular uptake of MPC100-DPA100 PNM delivered fluorescent probes, with fluorescence persistence for 48 h. This paper presents the first report of these novel findings, which highlight the potential of the system for nanomedicine application

Mask process correction (MPC) modeling and its application to EUV mask for electron beam mask writer EBM-7000

NASA Astrophysics Data System (ADS)

Kamikubo, Takashi; Ohnishi, Takayuki; Hara, Shigehiro; Anze, Hirohito; Hattori, Yoshiaki; Tamamushi, Shuichi; Bai, Shufeng; Wang, Jen-Shiang; Howell, Rafael; Chen, George; Li, Jiangwei; Tao, Jun; Wiley, Jim; Kurosawa, Terunobu; Saito, Yasuko; Takigawa, Tadahiro

2010-09-01

In electron beam writing on EUV mask, it has been reported that CD linearity does not show simple signatures as observed with conventional COG (Cr on Glass) masks because they are caused by scattered electrons form EUV mask itself which comprises stacked heavy metals and thick multi-layers. To resolve this issue, Mask Process Correction (MPC) will be ideally applicable. Every pattern is reshaped in MPC. Therefore, the number of shots would not increase and writing time will be kept within reasonable range. In this paper, MPC is extended to modeling for correction of CD linearity errors on EUV mask. And its effectiveness is verified with simulations and experiments through actual writing test.

Modelling and multi-parametric control for delivery of anaesthetic agents.

PubMed

Dua, Pinky; Dua, Vivek; Pistikopoulos, Efstratios N

2010-06-01

This article presents model predictive controllers (MPCs) and multi-parametric model-based controllers for delivery of anaesthetic agents. The MPC can take into account constraints on drug delivery rates and state of the patient but requires solving an optimization problem at regular time intervals. The multi-parametric controller has all the advantages of the MPC and does not require repetitive solution of optimization problem for its implementation. This is achieved by obtaining the optimal drug delivery rates as a set of explicit functions of the state of the patient. The derivation of the controllers relies on using detailed models of the system. A compartmental model for the delivery of three drugs for anaesthesia is developed. The key feature of this model is that mean arterial pressure, cardiac output and unconsciousness of the patient can be simultaneously regulated. This is achieved by using three drugs: dopamine (DP), sodium nitroprusside (SNP) and isoflurane. A number of dynamic simulation experiments are carried out for the validation of the model. The model is then used for the design of model predictive and multi-parametric controllers, and the performance of the controllers is analyzed.

Robust coordinated control of a dual-arm space robot

NASA Astrophysics Data System (ADS)

Shi, Lingling; Kayastha, Sharmila; Katupitiya, Jay

2017-09-01

Dual-arm space robots are more capable of implementing complex space tasks compared with single arm space robots. However, the dynamic coupling between the arms and the base will have a serious impact on the spacecraft attitude and the hand motion of each arm. Instead of considering one arm as the mission arm and the other as the balance arm, in this work two arms of the space robot perform as mission arms aimed at accomplishing secure capture of a floating target. The paper investigates coordinated control of the base's attitude and the arms' motion in the task space in the presence of system uncertainties. Two types of controllers, i.e. a Sliding Mode Controller (SMC) and a nonlinear Model Predictive Controller (MPC) are verified and compared with a conventional Computed-Torque Controller (CTC) through numerical simulations in terms of control accuracy and system robustness. Both controllers eliminate the need to linearly parameterize the dynamic equations. The MPC has been shown to achieve performance with higher accuracy than CTC and SMC in the absence of system uncertainties under the condition that they consume comparable energy. When the system uncertainties are included, SMC and CTC present advantageous robustness than MPC. Specifically, in a case where system inertia increases, SMC delivers higher accuracy than CTC and costs the least amount of energy.

Economic Model Predictive Control of Bihormonal Artificial Pancreas System Based on Switching Control and Dynamic R-parameter.

PubMed

Tang, Fengna; Wang, Youqing

2017-11-01

Blood glucose (BG) regulation is a long-term task for people with diabetes. In recent years, more and more researchers have attempted to achieve automated regulation of BG using automatic control algorithms, called the artificial pancreas (AP) system. In clinical practice, it is equally important to guarantee the treatment effect and reduce the treatment costs. The main motivation of this study is to reduce the cure burden. The dynamic R-parameter economic model predictive control (R-EMPC) is chosen to regulate the delivery rates of exogenous hormones (insulin and glucagon). It uses particle swarm optimization (PSO) to optimize the economic cost function and the switching logic between insulin delivery and glucagon delivery is designed based on switching control theory. The proposed method is first tested on the standard subject; the result is compared with the switching PID and the switching MPC. The effect of the dynamic R-parameter on improving the control performance is illustrated by comparing the results of the EMPC and the R-EMPC. Finally, the robustness tests on meal change (size and timing), hormone sensitivity (insulin and glucagon), and subject variability are performed. All results show that the proposed method can improve the control performance and reduce the economic costs. The simulation results verify the effectiveness of the proposed algorithm on improving the tracking performance, enhancing robustness, and reducing economic costs. The method proposed in this study owns great worth in practical application.

Predictive Scheduling for Electric Vehicles Considering Uncertainty of Load and User Behaviors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Bin; Huang, Rui; Wang, Yubo

2016-05-02

Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimizationmore » module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.« less

Direct model-based predictive control scheme without cost function for voltage source inverters with reduced common-mode voltage

NASA Astrophysics Data System (ADS)

Kim, Jae-Chang; Moon, Sung-Ki; Kwak, Sangshin

2018-04-01

This paper presents a direct model-based predictive control scheme for voltage source inverters (VSIs) with reduced common-mode voltages (CMVs). The developed method directly finds optimal vectors without using repetitive calculation of a cost function. To adjust output currents with the CMVs in the range of -Vdc/6 to +Vdc/6, the developed method uses voltage vectors, as finite control resources, excluding zero voltage vectors which produce the CMVs in the VSI within ±Vdc/2. In a model-based predictive control (MPC), not using zero voltage vectors increases the output current ripples and the current errors. To alleviate these problems, the developed method uses two non-zero voltage vectors in one sampling step. In addition, the voltage vectors scheduled to be used are directly selected at every sampling step once the developed method calculates the future reference voltage vector, saving the efforts of repeatedly calculating the cost function. And the two non-zero voltage vectors are optimally allocated to make the output current approach the reference current as close as possible. Thus, low CMV, rapid current-following capability and sufficient output current ripple performance are attained by the developed method. The results of a simulation and an experiment verify the effectiveness of the developed method.

Integrated waste management system costs in a MPC system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Supko, E.M.

1995-12-01

The impact on system costs of including a centralized interim storage facility as part of an integrated waste management system based on multi-purpose canister (MPC) technology was assessed in analyses by Energy Resources International, Inc. A system cost savings of $1 to $2 billion occurs if the Department of Energy begins spent fuel acceptance in 1998 at a centralized interim storage facility. That is, the savings associated with decreased utility spent fuel management costs will be greater than the cost of constructing and operating a centralized interim storage facility.

Obstacle avoidance handling and mixed integer predictive control for space robots

NASA Astrophysics Data System (ADS)

Zong, Lijun; Luo, Jianjun; Wang, Mingming; Yuan, Jianping

2018-04-01

This paper presents a novel obstacle avoidance constraint and a mixed integer predictive control (MIPC) method for space robots avoiding obstacles and satisfying physical limits during performing tasks. Firstly, a novel kind of obstacle avoidance constraint of space robots, which needs the assumption that the manipulator links and the obstacles can be represented by convex bodies, is proposed by limiting the relative velocity between two closest points which are on the manipulator and the obstacle, respectively. Furthermore, the logical variables are introduced into the obstacle avoidance constraint, which have realized the constraint form is automatically changed to satisfy different obstacle avoidance requirements in different distance intervals between the space robot and the obstacle. Afterwards, the obstacle avoidance constraint and other system physical limits, such as joint angle ranges, the amplitude boundaries of joint velocities and joint torques, are described as inequality constraints of a quadratic programming (QP) problem by using the model predictive control (MPC) method. To guarantee the feasibility of the obtained multi-constraint QP problem, the constraints are treated as soft constraints and assigned levels of priority based on the propositional logic theory, which can realize that the constraints with lower priorities are always firstly violated to recover the feasibility of the QP problem. Since the logical variables have been introduced, the optimization problem including obstacle avoidance and system physical limits as prioritized inequality constraints is termed as MIPC method of space robots, and its computational complexity as well as possible strategies for reducing calculation amount are analyzed. Simulations of the space robot unfolding its manipulator and tracking the end-effector's desired trajectories with the existence of obstacles and physical limits are presented to demonstrate the effectiveness of the proposed obstacle avoidance

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.

DEVELOPMENT, INSTALLATION AND OPERATION OF THE MPC&A OPERATIONS MONITORING (MOM) SYSTEM AT THE JOINT INSTITUTE FOR NUCLEAR RESEARCH (JINR) DUBNA, RUSSIA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kartashov,V.V.; Pratt,W.; Romanov, Y.A.

The Material Protection, Control and Accounting (MPC&A) Operations Monitoring (MOM) systems handling at the International Intergovernmental Organization - Joint Institute for Nuclear Research (JINR) is described in this paper. Category I nuclear material (plutonium and uranium) is used in JINR research reactors, facilities and for scientific and research activities. A monitoring system (MOM) was installed at JINR in April 2003. The system design was based on a vulnerability analysis, which took into account the specifics of the Institute. The design and installation of the MOM system was a collaborative effort between JINR, Brookhaven National Laboratory (BNL) and the U.S. Departmentmore » of Energy (DOE). Financial support was provided by DOE through BNL. The installed MOM system provides facility management with additional assurance that operations involving nuclear material (NM) are correctly followed by the facility personnel. The MOM system also provides additional confidence that the MPC&A systems continue to perform effectively.« less

Experimental Validation of L1 Adaptive Control: Rohrs' Counterexample in Flight

NASA Technical Reports Server (NTRS)

Xargay, Enric; Hovakimyan, Naira; Dobrokhodov, Vladimir; Kaminer, Issac; Kitsios, Ioannis; Cao, Chengyu; Gregory, Irene M.; Valavani, Lena

2010-01-01

The paper presents new results on the verification and in-flight validation of an L1 adaptive flight control system, and proposes a general methodology for verification and validation of adaptive flight control algorithms. The proposed framework is based on Rohrs counterexample, a benchmark problem presented in the early 80s to show the limitations of adaptive controllers developed at that time. In this paper, the framework is used to evaluate the performance and robustness characteristics of an L1 adaptive control augmentation loop implemented onboard a small unmanned aerial vehicle. Hardware-in-the-loop simulations and flight test results confirm the ability of the L1 adaptive controller to maintain stability and predictable performance of the closed loop adaptive system in the presence of general (artificially injected) unmodeled dynamics. The results demonstrate the advantages of L1 adaptive control as a verifiable robust adaptive control architecture with the potential of reducing flight control design costs and facilitating the transition of adaptive control into advanced flight control systems.

Adaptive MPC based on MIMO ARX-Laguerre model.

PubMed

Ben Abdelwahed, Imen; Mbarek, Abdelkader; Bouzrara, Kais

2017-03-01

This paper proposes a method for synthesizing an adaptive predictive controller using a reduced complexity model. This latter is given by the projection of the ARX model on Laguerre bases. The resulting model is entitled MIMO ARX-Laguerre and it is characterized by an easy recursive representation. The adaptive predictive control law is computed based on multi-step-ahead finite-element predictors, identified directly from experimental input/output data. The model is tuned in each iteration by an online identification algorithms of both model parameters and Laguerre poles. The proposed approach avoids time consuming numerical optimization algorithms associated with most common linear predictive control strategies, which makes it suitable for real-time implementation. The method is used to synthesize and test in numerical simulations adaptive predictive controllers for the CSTR process benchmark. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Predictive fault-tolerant control of an all-thruster satellite in 6-DOF motion via neural network model updating

NASA Astrophysics Data System (ADS)

Tavakoli, M. M.; Assadian, N.

2018-03-01

The problem of controlling an all-thruster spacecraft in the coupled translational-rotational motion in presence of actuators fault and/or failure is investigated in this paper. The nonlinear model predictive control approach is used because of its ability to predict the future behavior of the system. The fault/failure of the thrusters changes the mapping between the commanded forces to the thrusters and actual force/torque generated by the thruster system. Thus, the basic six degree-of-freedom kinetic equations are separated from this mapping and a set of neural networks are trained off-line to learn the kinetic equations. Then, two neural networks are attached to these trained networks in order to learn the thruster commands to force/torque mappings on-line. Different off-nominal conditions are modeled so that neural networks can detect any failure and fault, including scale factor and misalignment of thrusters. A simple model of the spacecraft relative motion is used in MPC to decrease the computational burden. However, a precise model by the means of orbit propagation including different types of perturbation is utilized to evaluate the usefulness of the proposed approach in actual conditions. The numerical simulation shows that this method can successfully control the all-thruster spacecraft with ON-OFF thrusters in different combinations of thruster fault and/or failure.

ASASSN-15oi: a rapidly evolving, luminous tidal disruption event at 216 Mpc

NASA Astrophysics Data System (ADS)

Holoien, T. W.-S.; Kochanek, C. S.; Prieto, J. L.; Grupe, D.; Chen, Ping; Godoy-Rivera, D.; Stanek, K. Z.; Shappee, B. J.; Dong, Subo; Brown, J. S.; Basu, U.; Beacom, J. F.; Bersier, D.; Brimacombe, J.; Carlson, E. K.; Falco, E.; Johnston, E.; Madore, B. F.; Pojmanski, G.; Seibert, M.

2016-12-01

We present ground-based and Swift photometric and spectroscopic observations of the tidal disruption event (TDE) ASASSN-15oi, discovered at the centre of 2MASX J20390918-3045201 (d ≃ 216 Mpc) by the All-Sky Automated Survey for SuperNovae. The source peaked at a bolometric luminosity of L ≃ 1.3 × 1044 erg s-1 and radiated a total energy of E ≃ 6.6 × 1050 erg over the first ˜3.5 months of observations. The early optical/UV emission of the source can be fit by a blackbody with temperature increasing from T ˜ 2 × 104 K to T ˜ 4 × 104 K while the luminosity declines from L ≃ 1.3 × 1044 erg s-1 to L ≃ 2.3 × 1043 erg s-1, requiring the photosphere to be shrinking rapidly. The optical/UV luminosity decline during this period is most consistent with an exponential decline, L∝ e^{-(t-t_0)/τ}, with τ ≃ 46.5 d for t0 ≃ 57241.6 (MJD), while a power-law decline of L ∝ (t - t0)-α with t0 ≃ 57 212.3 and α = 1.62 provides a moderately worse fit. ASASSN-15oi also exhibits roughly constant soft X-ray emission that is significantly weaker than the optical/UV emission. Spectra of the source show broad helium emission lines and strong blue continuum emission in early epochs, although these features fade rapidly and are not present ˜3 months after discovery. The early spectroscopic features and colour evolution of ASASSN-15oi are consistent with a TDE, but the rapid spectral evolution is unique among optically selected TDEs.

Health technology assessment review: Computerized glucose regulation in the intensive care unit - how to create artificial control

PubMed Central

2009-01-01

Current care guidelines recommend glucose control (GC) in critically ill patients. To achieve GC, many ICUs have implemented a (nurse-based) protocol on paper. However, such protocols are often complex, time-consuming, and can cause iatrogenic hypoglycemia. Computerized glucose regulation protocols may improve patient safety, efficiency, and nurse compliance. Such computerized clinical decision support systems (Cuss) use more complex logic to provide an insulin infusion rate based on previous blood glucose levels and other parameters. A computerized CDSS for glucose control has the potential to reduce overall workload, reduce the chance of human cognitive failure, and improve glucose control. Several computer-assisted glucose regulation programs have been published recently. In order of increasing complexity, the three main types of algorithms used are computerized flowcharts, Proportional-Integral-Derivative (PID), and Model Predictive Control (MPC). PID is essentially a closed-loop feedback system, whereas MPC models the behavior of glucose and insulin in ICU patients. Although the best approach has not yet been determined, it should be noted that PID controllers are generally thought to be more robust than MPC systems. The computerized Cuss that are most likely to emerge are those that are fully a part of the routine workflow, use patient-specific characteristics and apply variable sampling intervals. PMID:19849827

Overnight closed-loop insulin delivery with model predictive control: assessment of hypoglycemia and hyperglycemia risk using simulation studies.

PubMed

Wilinska, Malgorzata E; Budiman, Erwin S; Taub, Marc B; Elleri, Daniela; Allen, Janet M; Acerini, Carlo L; Dunger, David B; Hovorka, Roman

2009-09-01

Hypoglycemia and hyperglycemia during closed-loop insulin delivery based on subcutaneous (SC) glucose sensing may arise due to (1) overdosing and underdosing of insulin by control algorithm and (2) difference between plasma glucose (PG) and sensor glucose, which may be transient (kinetics origin and sensor artifacts) or persistent (calibration error [CE]). Using in silico testing, we assessed hypoglycemia and hyperglycemia incidence during over-night closed loop. Additionally, a comparison was made against incidence observed experimentally during open-loop single-night in-clinic studies in young people with type 1 diabetes mellitus (T1DM) treated by continuous SC insulin infusion. Simulation environment comprising 18 virtual subjects with T1DM was used to simulate overnight closed-loop study with a model predictive control (MPC) algorithm. A 15 h experiment started at 17:00 and ended at 08:00 the next day. Closed loop commenced at 21:00 and continued for 11 h. At 18:00, protocol included meal (50 g carbohydrates) accompanied by prandial insulin. The MPC algorithm advised on insulin infusion every 15 min. Sensor glucose was obtained by combining model-calculated noise-free interstitial glucose with experimentally derived transient and persistent sensor artifacts associated with FreeStyle Navigator (FSN). Transient artifacts were obtained from FSN sensor pairs worn by 58 subjects with T1DM over 194 nighttime periods. Persistent difference due to FSN CE was quantified from 585 FSN sensor insertions, yielding 1421 calibration sessions from 248 subjects with diabetes. Episodes of severe (PG < or = 36 mg/dl) and significant (PG < or = 45 mg/dl) hypoglycemia and significant hyperglycemia (PG > or = 300 mg/dl) were extracted from 18,000 simulated closed-loop nights. Severe hypoglycemia was not observed when FSN CE was less than 45%. Hypoglycemia and hyperglycemia incidence during open loop was assessed from 21 overnight studies in 17 young subjects with T1DM (8 males; 13

Control and Optimization of Electric Ship Propulsion Systems with Hybrid Energy Storage

NASA Astrophysics Data System (ADS)

Hou, Jun

Electric ships experience large propulsion-load fluctuations on their drive shaft due to encountered waves and the rotational motion of the propeller, affecting the reliability of the shipboard power network and causing wear and tear. This dissertation explores new solutions to address these fluctuations by integrating a hybrid energy storage system (HESS) and developing energy management strategies (EMS). Advanced electric propulsion drive concepts are developed to improve energy efficiency, performance and system reliability by integrating HESS, developing advanced control solutions and system integration strategies, and creating tools (including models and testbed) for design and optimization of hybrid electric drive systems. A ship dynamics model which captures the underlying physical behavior of the electric ship propulsion system is developed to support control development and system optimization. To evaluate the effectiveness of the proposed control approaches, a state-of-the-art testbed has been constructed which includes a system controller, Li-Ion battery and ultra-capacitor (UC) modules, a high-speed flywheel, electric motors with their power electronic drives, DC/DC converters, and rectifiers. The feasibility and effectiveness of HESS are investigated and analyzed. Two different HESS configurations, namely battery/UC (B/UC) and battery/flywheel (B/FW), are studied and analyzed to provide insights into the advantages and limitations of each configuration. Battery usage, loss analysis, and sensitivity to battery aging are also analyzed for each configuration. In order to enable real-time application and achieve desired performance, a model predictive control (MPC) approach is developed, where a state of charge (SOC) reference of flywheel for B/FW or UC for B/UC is used to address the limitations imposed by short predictive horizons, because the benefits of flywheel and UC working around high-efficiency range are ignored by short predictive horizons. Given

Extending the modeling of the anisotropic galaxy power spectrum to k = 0.4 hMpc-1

NASA Astrophysics Data System (ADS)

Hand, Nick; Seljak, Uroš; Beutler, Florian; Vlah, Zvonimir

2017-10-01

We present a model for the redshift-space power spectrum of galaxies and demonstrate its accuracy in describing the monopole, quadrupole, and hexadecapole of the galaxy density field down to scales of k = 0.4 hMpc-1. The model describes the clustering of galaxies in the context of a halo model and the clustering of the underlying halos in redshift space using a combination of Eulerian perturbation theory and N-body simulations. The modeling of redshift-space distortions is done using the so-called distribution function approach. The final model has 13 free parameters, and each parameter is physically motivated rather than a nuisance parameter, which allows the use of well-motivated priors. We account for the Finger-of-God effect from centrals and both isolated and non-isolated satellites rather than using a single velocity dispersion to describe the combined effect. We test and validate the accuracy of the model on several sets of high-fidelity N-body simulations, as well as realistic mock catalogs designed to simulate the BOSS DR12 CMASS data set. The suite of simulations covers a range of cosmologies and galaxy bias models, providing a rigorous test of the level of theoretical systematics present in the model. The level of bias in the recovered values of f σ8 is found to be small. When including scales to k = 0.4 hMpc-1, we find 15-30% gains in the statistical precision of f σ8 relative to k = 0.2 hMpc-1 and a roughly 10-15% improvement for the perpendicular Alcock-Paczynski parameter α⊥. Using the BOSS DR12 CMASS mocks as a benchmark for comparison, we estimate an uncertainty on f σ8 that is ~10-20% larger than other similar Fourier-space RSD models in the literature that use k <= 0.2 hMpc-1, suggesting that these models likely have a too-limited parametrization.

Closed-loop insulin delivery during pregnancy complicated by type 1 diabetes.

PubMed

Murphy, Helen R; Elleri, Daniela; Allen, Janet M; Harris, Julie; Simmons, David; Rayman, Gerry; Temple, Rosemary; Dunger, David B; Haidar, Ahmad; Nodale, Marianna; Wilinska, Malgorzata E; Hovorka, Roman

2011-02-01

This study evaluated closed-loop insulin delivery with a model predictive control (MPC) algorithm during early (12-16 weeks) and late gestation (28-32 weeks) in pregnant women with type 1 diabetes. Ten women with type 1 diabetes (age 31 years, diabetes duration 19 years, BMI 24.1 kg/m(2), booking A1C 6.9%) were studied over 24 h during early (14.8 weeks) and late pregnancy (28.0 weeks). A nurse adjusted the basal insulin infusion rate from continuous glucose measurements (CGM), fed into the MPC algorithm every 15 min. Mean glucose and time spent in target (63-140 mg/dL), hyperglycemic (>140 to ≥ 180 mg/dL), and hypoglycemic (<63 to ≤ 50 mg/dL) were calculated using plasma and sensor glucose measurements. Linear mixed-effects models were used to compare glucose control during early and late gestation. During closed-loop insulin delivery, median (interquartile range) plasma glucose levels were 117 (100.8-154.8) mg/dL in early and 126 (109.8-140.4) mg/dL in late gestation (P = 0.72). The overnight mean (interquartile range) plasma glucose time in target was 84% (50-100%) in early and 100% (94-100%) in late pregnancy (P = 0.09). Overnight mean (interquartile range) time spent hyperglycemic (>140 mg/dL) was 7% (0-40%) in early and 0% (0-6%) in late pregnancy (P = 0.25) and hypoglycemic (<63 mg/dL) was 0% (0-3%) and 0% (0-0%), respectively (P = 0.18). Postprandial glucose control, glucose variability, insulin infusion rates, and CGM sensor accuracy were no different in early or late pregnancy. MPC algorithm performance was maintained throughout pregnancy, suggesting that overnight closed-loop insulin delivery could be used safely during pregnancy. More work is needed to achieve optimal postprandial glucose control.

Sentinel-3 Mission Performance Center: paving the way of high-quality controlled data

NASA Astrophysics Data System (ADS)

Bruniquel, Jerome; Féménias, Pierre; Goryl, Philippe; Bonekamp, Hans

2015-04-01

As part of the Sentinel-3 mission and in order to ensure the highest quality of products, ESA and EUMETSAT set up the Sentinel-3 Mission Performance Centre (S-3 MPC). This facility is part of the Payload Data Ground Segment (PDGS) and aims at controlling the quality of all generated products, from L0 to L2. The S-3 MPC is composed of a Coordinating Centre (CC), where the core infrastructure is hosted, which is in charge of the main routine activities (especially the quality control of data) and the overall service management. Expert Support Laboratories (ESLs) are involved in calibration and validation activities and provide specific assessment of the products (e.g., analysis of trends, ad hoc analysis of anomalies, etc.). The S-3 MPC interacts with the Processing Archiving Centers (PACs) and the marine centre at EUMETSAT. The S-3 MPC service contract is currently carried out by 23-partners consortium led by ACRI-ST, France. The S-3 MPC contract was kick-offed in September 2014 with a first set-up phase of 12 months. After the launch of S3-A (planned before end of 2015), the S-3 MPC will start its second phase to support commissioning activities. Then a routine operation phase of up to 5 years will begin, including the commissioning activities related to S3-B. The main S-3 MPC activities are: - Calibration: to update on-board and on-ground configuration data in order to meet product quality requirements. - Validation: to assess, by independent means with respect to the methods and tools used for calibration, the quality of the generated data products. Validation functions provide feedback to calibration and data processors corrective and perfective maintenance activities. - Verification: to confirm that the specified requirements on a system have been satisfied. - Quality Control: to routinely monitor the status of the sensor and to check if the derived products (Level 0, Level 1 and Level 2) meet the quality requirements along mission lifetime. - Algorithm

Development of Regulatory Documents for Creation (Upgrade) of Physical Protection Systems under the Russian/American MPC&A Program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Izmaylov, Alexandr V.; Babkin, Vladimir; Kurov, Valeriy

2009-10-07

The development of new or the upgrade of existing physical protection systems (PPS) for nuclear facilities involves a multi-step and multidimensional process. The process consists of conceptual design, design, and commissioning stages. The activities associated with each of these stages are governed by Russian government and agency regulations. To ensure a uniform approach to development or upgrading of PPS at Russian nuclear facilities, the development of a range of regulatory and methodological documents is necessary. Some issues of PPS development are covered by the regulatory documents developed by Rosatom, as well as other Russian agencies with nuclear facilities under theirmore » control. This regulatory development has been accomplished as part of the U.S.-Russian MPC&A cooperation or independently by the Russian Federation. While regulatory coverage is extensive, there are a number of issues such as vulnerability analysis, effectiveness assessment, upgrading PPS, and protection of information systems for PPS that require additional regulations be developed. This paper reports on the status of regulatory coverage for PPS development or upgrade, and outlines a new approach to regulatory document development. It describes the evolutionary process of regulatory development through experience gained in the design, development and implementation of PPS as well as experience gained through the cooperative efforts of Russian and U.S. experts involved the development of MPC&A regulations.« less

Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

NASA Astrophysics Data System (ADS)

Maljaars, E.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J. M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A. A.; Vu, N. M. T.; The EUROfusion MST1-team; The TCV-team

2017-12-01

The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.

Near-Optimal Tracking Control of Mobile Robots Via Receding-Horizon Dual Heuristic Programming.

PubMed

Lian, Chuanqiang; Xu, Xin; Chen, Hong; He, Haibo

2016-11-01

Trajectory tracking control of wheeled mobile robots (WMRs) has been an important research topic in control theory and robotics. Although various tracking control methods with stability have been developed for WMRs, it is still difficult to design optimal or near-optimal tracking controller under uncertainties and disturbances. In this paper, a near-optimal tracking control method is presented for WMRs based on receding-horizon dual heuristic programming (RHDHP). In the proposed method, a backstepping kinematic controller is designed to generate desired velocity profiles and the receding horizon strategy is used to decompose the infinite-horizon optimal control problem into a series of finite-horizon optimal control problems. In each horizon, a closed-loop tracking control policy is successively updated using a class of approximate dynamic programming algorithms called finite-horizon dual heuristic programming (DHP). The convergence property of the proposed method is analyzed and it is shown that the tracking control system based on RHDHP is asymptotically stable by using the Lyapunov approach. Simulation results on three tracking control problems demonstrate that the proposed method has improved control performance when compared with conventional model predictive control (MPC) and DHP. It is also illustrated that the proposed method has lower computational burden than conventional MPC, which is very beneficial for real-time tracking control.

Extending the modeling of the anisotropic galaxy power spectrum to k = 0.4 h Mpc{sup −1}

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hand, Nick; Seljak, Uroš; Beutler, Florian

We present a model for the redshift-space power spectrum of galaxies and demonstrate its accuracy in describing the monopole, quadrupole, and hexadecapole of the galaxy density field down to scales of k = 0.4 h Mpc{sup −1}. The model describes the clustering of galaxies in the context of a halo model and the clustering of the underlying halos in redshift space using a combination of Eulerian perturbation theory and N -body simulations. The modeling of redshift-space distortions is done using the so-called distribution function approach. The final model has 13 free parameters, and each parameter is physically motivated rather thanmore » a nuisance parameter, which allows the use of well-motivated priors. We account for the Finger-of-God effect from centrals and both isolated and non-isolated satellites rather than using a single velocity dispersion to describe the combined effect. We test and validate the accuracy of the model on several sets of high-fidelity N -body simulations, as well as realistic mock catalogs designed to simulate the BOSS DR12 CMASS data set. The suite of simulations covers a range of cosmologies and galaxy bias models, providing a rigorous test of the level of theoretical systematics present in the model. The level of bias in the recovered values of f σ{sub 8} is found to be small. When including scales to k = 0.4 h Mpc{sup −1}, we find 15-30% gains in the statistical precision of f σ{sub 8} relative to k = 0.2 h Mpc{sup −1} and a roughly 10–15% improvement for the perpendicular Alcock-Paczynski parameter α{sub ⊥}. Using the BOSS DR12 CMASS mocks as a benchmark for comparison, we estimate an uncertainty on f σ{sub 8} that is ∼10–20% larger than other similar Fourier-space RSD models in the literature that use k ≤ 0.2 h Mpc{sup −1}, suggesting that these models likely have a too-limited parametrization.« less

DEMOGRAPHICS OF BULGE TYPES WITHIN 11 Mpc AND IMPLICATIONS FOR GALAXY EVOLUTION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fisher, David B.; Drory, Niv, E-mail: dbfisher@astro.umd.edu

2011-06-01

We present an inventory of galaxy bulge types (elliptical galaxy, classical bulge, pseudobulge, and bulgeless galaxy) in a volume-limited sample within the local 11 Mpc sphere using Spitzer 3.6 {mu}m and Hubble Space Telescope data. We find that whether counting by number, star formation rate, or stellar mass, the dominant galaxy type in the local universe has pure disk characteristics (either hosting a pseudobulge or being bulgeless). Galaxies that contain either a pseudobulge or no bulge combine to account for over 80% of the number of galaxies above a stellar mass of 10{sup 9} M{sub sun}. Classical bulges and ellipticalmore » galaxies account for {approx}1/4, and disks for {approx}3/4 of the stellar mass in the local 11 Mpc. About 2/3 of all star formation in the local volume takes place in galaxies with pseudobulges. Looking at the fraction of galaxies with different bulge types as a function of stellar mass, we find that the frequency of classical bulges strongly increases with stellar mass, and comes to dominate above 10{sup 10.5} M{sub sun}. Galaxies with pseudobulges dominate at 10{sup 9.5}-10{sup 10.5} M{sub sun}. Yet lower-mass galaxies are most likely to be bulgeless. If pseudobulges are not a product of mergers, then the frequency of pseudobulges in the local universe poses a challenge for galaxy evolution models.« less

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.

Closed-Loop Insulin Delivery During Pregnancy Complicated by Type 1 Diabetes

PubMed Central

Murphy, Helen R.; Elleri, Daniela; Allen, Janet M.; Harris, Julie; Simmons, David; Rayman, Gerry; Temple, Rosemary; Dunger, David B.; Haidar, Ahmad; Nodale, Marianna; Wilinska, Malgorzata E.; Hovorka, Roman

2011-01-01

OBJECTIVE This study evaluated closed-loop insulin delivery with a model predictive control (MPC) algorithm during early (12–16 weeks) and late gestation (28–32 weeks) in pregnant women with type 1 diabetes. RESEARCH DESIGN AND METHODS Ten women with type 1 diabetes (age 31 years, diabetes duration 19 years, BMI 24.1 kg/m2, booking A1C 6.9%) were studied over 24 h during early (14.8 weeks) and late pregnancy (28.0 weeks). A nurse adjusted the basal insulin infusion rate from continuous glucose measurements (CGM), fed into the MPC algorithm every 15 min. Mean glucose and time spent in target (63–140 mg/dL), hyperglycemic (>140 to ≥180 mg/dL), and hypoglycemic (<63 to ≤50 mg/dL) were calculated using plasma and sensor glucose measurements. Linear mixed-effects models were used to compare glucose control during early and late gestation. RESULTS During closed-loop insulin delivery, median (interquartile range) plasma glucose levels were 117 (100.8–154.8) mg/dL in early and 126 (109.8–140.4) mg/dL in late gestation (P = 0.72). The overnight mean (interquartile range) plasma glucose time in target was 84% (50–100%) in early and 100% (94–100%) in late pregnancy (P = 0.09). Overnight mean (interquartile range) time spent hyperglycemic (>140 mg/dL) was 7% (0–40%) in early and 0% (0–6%) in late pregnancy (P = 0.25) and hypoglycemic (<63 mg/dL) was 0% (0–3%) and 0% (0–0%), respectively (P = 0.18). Postprandial glucose control, glucose variability, insulin infusion rates, and CGM sensor accuracy were no different in early or late pregnancy. CONCLUSIONS MPC algorithm performance was maintained throughout pregnancy, suggesting that overnight closed-loop insulin delivery could be used safely during pregnancy. More work is needed to achieve optimal postprandial glucose control. PMID:21216859

Anticipating the next meal using meal behavioral profiles: a hybrid model-based stochastic predictive control algorithm for T1DM.

PubMed

Hughes, C S; Patek, S D; Breton, M; Kovatchev, B P

2011-05-01

Automatic control of Type 1 Diabetes Mellitus (T1DM) with subcutaneous (SC) measurement of glucose concentration and subcutaneous (SC) insulin infusion is of great interest within the diabetes technology research community. The main challenge with the so-called "SC-SC" route to control is sensing and actuation delay, which tends to either destabilize the system or inhibit the aggressiveness of the controller in responding to meals and exercise. Model predictive control (MPC) is one strategy for mitigating delay, where optimal insulin infusions can be given in anticipation of future meal disturbances. Unfortunately, exact prior knowledge of meals can only be assured in a clinical environment and uncertainty about when and if meals will arrive could lead to catastrophic outcomes. As a follow-on to our recent paper in the IFAC symposium on Biological and Medical Systems (MCBMS 2009), we develop a control law that can anticipate meals given a probabilistic description of the patient's eating behavior in the form of a random meal (behavioral) profile. Preclinical in silico trials using the oral glucose meal model of Dalla Man et al. show that the control strategy provides a convenient means of accounting for uncertain prior knowledge of meals without compromising patient safety, even in the event that anticipated meals are skipped. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Anticipatory control: A software retrofit for current plant controllers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parthasarathy, S.; Parlos, A.G.; Atiya, A.F.

1993-01-01

The design and simulated testing of an artificial neural network (ANN)-based self-adapting controller for complex process systems are presented in this paper. The proposed controller employs concepts based on anticipatory systems, which have been widely used in the petroleum and chemical industries, and they are slowly finding their way into the power industry. In particular, model predictive control (MPC) is used for the systematic adaptation of the controller parameters to achieve desirable plant performance over the entire operating envelope. The versatile anticipatory control algorithm developed in this study is projected to enhance plant performance and lend robustness to drifts inmore » plant parameters and to modeling uncertainties. This novel technique of integrating recurrent ANNs with a conventional controller structure appears capable of controlling complex, nonlinear, and nonminimum phase process systems. The direct, on-line adaptive control algorithm presented in this paper considers the plant response over a finite time horizon, diminishing the need for manual control or process interruption for controller gain tuning.« less

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.

Erratum: ``The Luminosity Function of IRAS Point Source Catalog Redshift Survey Galaxies'' (ApJ, 587, L89 [2003])

NASA Astrophysics Data System (ADS)

Takeuchi, Tsutomu T.; Yoshikawa, Kohji; Ishii, Takako T.

2004-05-01

We have mentioned that we normalized the parameters for the luminosity function by the Hubble constant H0=100 km s-1 Mpc-1 however, for the characteristic luminosity L* we erroneously normalized it by H0=70 km s-1 Mpc-1. As a result, we have proposed wrong numerical factors for L*. In addition, there is a typographic error in the exponent of equation (6) of the published manuscript. Correct values are as follows: L*=(4.34+/-0.86)×108 h-2 [Lsolar] for equation (4), and L*=(2.50+/-0.44)×109 h-2 [Lsolar] and L*=(9.55+/-0.20)×108 h-2 [Lsolar] for equations (5) and (6), respectively. All the other parameters are correct. The errors have occurred only in the final conversion, and they do not affect our discussions and conclusions at all. We thank P. Ranalli for pointing out the errors.

Simulation analysis of adaptive cruise prediction control

NASA Astrophysics Data System (ADS)

Zhang, Li; Cui, Sheng Min

2017-09-01

Predictive control is suitable for multi-variable and multi-constraint system control.In order to discuss the effect of predictive control on the vehicle longitudinal motion, this paper establishes the expected spacing model by combining variable pitch spacing and the of safety distance strategy. The model predictive control theory and the optimization method based on secondary planning are designed to obtain and track the best expected acceleration trajectory quickly. Simulation models are established including predictive and adaptive fuzzy control. Simulation results show that predictive control can realize the basic function of the system while ensuring the safety. The application of predictive and fuzzy adaptive algorithm in cruise condition indicates that the predictive control effect is better.

Mpc-scale diffuse radio emission in two massive cool-core clusters of galaxies

NASA Astrophysics Data System (ADS)

Sommer, Martin W.; Basu, Kaustuv; Intema, Huib; Pacaud, Florian; Bonafede, Annalisa; Babul, Arif; Bertoldi, Frank

2017-04-01

Radio haloes are diffuse synchrotron sources on scales of ˜1 Mpc that are found in merging clusters of galaxies, and are believed to be powered by electrons re-accelerated by merger-driven turbulence. We present measurements of extended radio emission on similarly large scales in two clusters of galaxies hosting cool cores: Abell 2390 and Abell 2261. The analysis is based on interferometric imaging with the Karl G. Jansky Very Large Array, Very Large Array and Giant Metrewave Radio Telescope. We present detailed radio images of the targets, subtract the compact emission components and measure the spectral indices for the diffuse components. The radio emission in A2390 extends beyond a known sloshing-like brightness discontinuity, and has a very steep in-band spectral slope at 1.5 GHz that is similar to some known ultrasteep spectrum radio haloes. The diffuse signal in A2261 is more extended than in A2390 but has lower luminosity. X-ray morphological indicators, derived from XMM-Newton X-ray data, place these clusters in the category of relaxed or regular systems, although some asymmetric features that can indicate past minor mergers are seen in the X-ray brightness images. If these two Mpc-scale radio sources are categorized as giant radio haloes, they question the common assumption of radio haloes occurring exclusively in clusters undergoing violent merging activity, in addition to commonly used criteria for distinguishing between radio haloes and minihaloes.

A metabolic switch controls intestinal differentiation downstream of Adenomatous polyposis coli (APC).

PubMed

Sandoval, Imelda T; Delacruz, Richard Glenn C; Miller, Braden N; Hill, Shauna; Olson, Kristofor A; Gabriel, Ana E; Boyd, Kevin; Satterfield, Christeena; Remmen, Holly Van; Rutter, Jared; Jones, David A

2017-04-11

Elucidating signaling pathways that regulate cellular metabolism is essential for a better understanding of normal development and tumorigenesis. Recent studies have shown that mitochondrial pyruvate carrier 1 (MPC1) , a crucial player in pyruvate metabolism, is downregulated in colon adenocarcinomas. Utilizing zebrafish to examine the genetic relationship between MPC1 and Adenomatous polyposis coli (APC), a key tumor suppressor in colorectal cancer, we found that apc controls the levels of mpc1 and that knock down of mpc1 recapitulates phenotypes of impaired apc function including failed intestinal differentiation. Exogenous human MPC1 RNA rescued failed intestinal differentiation in zebrafish models of apc deficiency. Our data demonstrate a novel role for apc in pyruvate metabolism and that pyruvate metabolism dictates intestinal cell fate and differentiation decisions downstream of apc .

Photo-assisted generation of phospholipid polymer substrates for regiospecific protein conjugation and control of cell adhesion.

PubMed

Tanaka, Masako; Iwasaki, Yasuhiko

2016-08-01

Novel photo-reactive phospholipid polymers were synthesized for use in the preparation of nonfouling surfaces with protein conjugation capacity. Poly[2-methacryloyloxyethyl phosphorylcholine (MPC)-ran-N-methacryloyl-(l)-tyrosinemethylester (MAT)] (P(MPC/MAT)) was synthesized by conventional radical polymerization, with the MAT units capable of being oxidized by 254nm UV irradiation. Because of this photo-oxidation, active species such as catechol and quinone were alternately generated in the copolymer. A silicon wafer was subjected to surface modification through spin coating of P(MPC/MAT) from an aqueous solution for use as a model substrate. The surface was then irradiated several times with UV light. The thickness of the polymer layers formed on the Si wafers was influenced by various parameters such as polymer concentration, UV irradiation time, and composition of the MAT units in P(MPC/MAT). Oxidized MAT units were advantageous not only for polymer adhesion to a solid surface but also for protein conjugation with the adhered polymers. The amount of protein immobilized on UV-irradiated P(MPC/MAT) was dependent on the composition of the MAT units in the polymer. Furthermore, it was confirmed that protein immobilization on the polymer occurred through the oxidized MAT units because the protein adsorption was significantly reduced upon blocking these units through pretreatment with glycine. Conjugation of regiospecific protein could also be achieved through the use of a photomask. In addition, nonspecific protein adsorption was reduced on the non-irradiated regions whose surface was covered with physisorbed P(MPC/MAT). Therefore, P(MPC/MAT) can be used in the preparation of nonfouling substrates, which enable micrometer-sized manipulation of proteins through photo-irradiation. Function of proteins immobilized on MPC copolymers was also confirmed by cell adhesion test. As such, photo-reactive MPC copolymers are suitable for performing controlled protein conjugation

Feedback brake distribution control for minimum pitch

NASA Astrophysics Data System (ADS)

Tavernini, Davide; Velenis, Efstathios; Longo, Stefano

2017-06-01

The distribution of brake forces between front and rear axles of a vehicle is typically specified such that the same level of brake force coefficient is imposed at both front and rear wheels. This condition is known as 'ideal' distribution and it is required to deliver the maximum vehicle deceleration and minimum braking distance. For subcritical braking conditions, the deceleration demand may be delivered by different distributions between front and rear braking forces. In this research we show how to obtain the optimal distribution which minimises the pitch angle of a vehicle and hence enhances driver subjective feel during braking. A vehicle model including suspension geometry features is adopted. The problem of the minimum pitch brake distribution for a varying deceleration level demand is solved by means of a model predictive control (MPC) technique. To address the problem of the undesirable pitch rebound caused by a full-stop of the vehicle, a second controller is designed and implemented independently from the braking distribution in use. An extended Kalman filter is designed for state estimation and implemented in a high fidelity environment together with the MPC strategy. The proposed solution is compared with the reference 'ideal' distribution as well as another previous feed-forward solution.

Hemorrhage control for laparoscopic hepatectomy: technical details and predictive factors for intraoperative blood loss.

PubMed

Kawaguchi, Yoshikuni; Nomi, Takeo; Fuks, David; Mal, Frederic; Kokudo, Norihiro; Gayet, Brice

2016-06-01

Controlling bleeding during laparoscopic hepatectomy (LH) is technically demanding, but reportedly associated with less estimated blood loss (EBL) than open surgery. The present study aimed to describe and evaluate hemorrhage control techniques during LH and identify predictors of high intraoperative EBL. The data of 438 consecutive patients undergoing LH between 1995 and 2012 were reviewed. Bleeding control was facilitated by the proper use of hemostatic devices and surgical maneuvers unique to LH and by preserving intra-abdominal pressure. EBL was evaluated among three groups of 146 patients in each group: 1995-2006 (group A), 2006-2009 (group B), and 2009-2012 (group C). We also sought factors that predicted EBL ≥800 mL. Mean EBL decreased overtime from groups A to C: group A, 378 ± 619 mL; group B, 293 ± 391 mL; groups C, 257 ± 366 mL; P = 0.127. Transfusion rate was 6.7 % in group A, 5.5 % in group B, and 4.8 % in group C (P = 0.743). Hypertension (odds ratio (OR) 2.82, 95 % confidence interval CI 1.37-5.78; P = 0.006), preoperative chemotherapy (OR 2.55, 95 % CI 1.26-5.31; P = 0.009), resection of posterosuperior segments (OR 3.73, 95 % CI 1.33-12.17; P = 0.012), and major hepatectomy (OR 4.21, 95 % CI 1.64-13.02; P < 0.001) independently predicted high EBL. Improvements in bleeding control techniques over time have reduced EBL during LH. The use of these techniques and an understanding of the predictive factors for high EBL will help surgeons improve outcomes after LH.

Towards energy efficient operation of Heating, Ventilation and Air Conditioning systems via advanced supervisory control design

NASA Astrophysics Data System (ADS)

Oswiecinska, A.; Hibbs, J.; Zajic, I.; Burnham, K. J.

2015-11-01

This paper presents conceptual control solution for reliable and energy efficient operation of heating, ventilation and air conditioning (HVAC) systems used in large volume building applications, e.g. warehouse facilities or exhibition centres. Advanced two-level scalable control solution, designed to extend capabilities of the existing low-level control strategies via remote internet connection, is presented. The high-level, supervisory controller is based on Model Predictive Control (MPC) architecture, which is the state-of-the-art for indoor climate control systems. The innovative approach benefits from using passive heating and cooling control strategies for reducing the HVAC system operational costs, while ensuring that required environmental conditions are met.

An Overview of the Cooperative Effort between the United States Department of Energy and the China Atomic Energy Authority to Enhance MPC&A Inspections for Civil Nuclear Facilities in China

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ahern, Keith; Daming, Liu; Hanley, Tim

The United States Department of Energy, National Nuclear Security Administration (DOE/NNSA) and the China Atomic Energy Authority (CAEA) are cooperating to enhance the domestic regulatory inspections capacity for special nuclear material protection, control and accounting (MPC&A) requirements for civil nuclear facilities in China. This cooperation is conducted under the auspices of the Agreement between the Department of Energy of the United States of America and the State Development and Planning Commission of the People s Republic of China on Cooperation Concerning Peaceful Uses of Nuclear Technology. This initial successful effort was conducted in three phases. Phase I focused on introducingmore » CAEA personnel to DOE and U. S. Nuclear Regulatory Commission inspection methods for U. S. facilities. This phase was completed in January 2008 during meetings in Beijing. Phase II focused on developing physical protection and material control and accounting inspection exercises that enforced U. S. inspection methods identified during Phase 1. Hands on inspection activities were conducted in the United States over a two week period in July 2009. Simulated deficiencies were integrated into the inspection exercises. The U. S. and Chinese participants actively identified and discussed deficiencies noted during the two week training course. The material control and accounting inspection exercises were conducted at the Paducah Gaseous Diffusion Plant (PGDP) in Paducah, KY. The physical protection inspection exercises were conducted at the Oak Ridge National Laboratory (ORNL) in Oak Ridge, TN. Phase III leveraged information provided under Phase I and experience gained under Phase II to develop a formal inspection guide that incorporates a systematic approach to training for Chinese MPC&A field inspectors. Additional hands on exercises that are applicable to Chinese regulations were incorporated into the Phase III training material. Phase III was completed in May 2010 at

Peripheral airway impairment measured by oscillometry predicts loss of asthma control in children.

PubMed

Shi, Yixin; Aledia, Anna S; Galant, Stanley P; George, Steven C

2013-03-01

We previously showed that impulse oscillometry (IOS) indices of peripheral airway function are associated with asthma control in children. However, little data exist on whether dysfunction in the peripheral airways can predict loss of asthma control. We sought to determine the utility of peripheral airway impairment, as measured by IOS, in predicting loss of asthma control in children. Fifty-four children (age, 7-17 years) with controlled asthma were enrolled in the study. Spirometric and IOS indices of airway function were obtained at baseline and at a follow-up visit 8 to 12 weeks later. Physicians who were blinded to the IOS measurements assessed asthma control (National Asthma Education and Prevention Program guidelines) on both visits and prescribed no medication change between visits. Thirty-eight (70%) patients maintained asthma control between 2 visits (group C-C), and 16 patients had asthma that became uncontrolled on the follow-up visit (group C-UC). There was no difference in baseline spirometric results between the C-C and C-UC groups, except for FEV1/forced vital capacity ratio (86% vs 82%, respectively; P < .01). Baseline IOS results, including resistance of the respiratory system at 5 Hz (R5; 6.4 vs 4.3 cm H2O · L(-1) · s), frequency dependence of resistance (difference of R5 and resistance of the respiratory system at 20 Hz [R5-20]; 2.0 vs 0.7 cm H2O · L(-1) · s), and reactance area (13.1 vs 4.1 cm H2O · L(-1)), of group C-UC were significantly higher than those of group C-C (P < .01). Receiver operating characteristic analysis showed baseline R5-20 and reactance area effectively predicted asthma control status at the follow-up visit (area under the curve, 0.91 and 0.90). Children with controlled asthma who have increased peripheral airway IOS indices are at risk of losing asthma control. Copyright © 2012 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

Design-based modeling of magnetically actuated soft diaphragm materials

NASA Astrophysics Data System (ADS)

Jayaneththi, V. R.; Aw, K. C.; McDaid, A. J.

2018-04-01

Magnetic polymer composites (MPC) have shown promise for emerging biomedical applications such as lab-on-a-chip and implantable drug delivery. These soft material actuators are capable of fast response, large deformation and wireless actuation. Existing MPC modeling approaches are computationally expensive and unsuitable for rapid design prototyping and real-time control applications. This paper proposes a macro-scale 1-DOF model capable of predicting force and displacement of an MPC diaphragm actuator. Model validation confirmed both blocked force and displacement can be accurately predicted in a variety of working conditions i.e. different magnetic field strengths, static/dynamic fields, and gap distances. The contribution of this work includes a comprehensive experimental investigation of a macro-scale diaphragm actuator; the derivation and validation of a new phenomenological model to describe MPC actuation; and insights into the proposed model’s design-based functionality i.e. scalability and generalizability in terms of magnetic filler concentration and diaphragm diameter. Due to the lumped element modeling approach, the proposed model can also be adapted to alternative actuator configurations, and thus presents a useful tool for design, control and simulation of novel MPC applications.

75 FR 57841 - List of Approved Spent Fuel Storage Casks: NAC-MPC System, Revision 6, Confirmation of Effective...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-23

... Spent Fuel Storage Casks: NAC-MPC System, Revision 6, Confirmation of Effective Date AGENCY: Nuclear... include Amendment Number 6 to Certificate of Compliance (CoC) Number 1025. DATES: Effective Date: The... regulations at 10 CFR 72.214 to include Amendment No. 6 to CoC No. 1025. Amendment No. 6 changes the...

Strategic Processing and Predictive Inference Generation in L2 Reading

ERIC Educational Resources Information Center

Nahatame, Shingo

2014-01-01

Predictive inference is the anticipation of the likely consequences of events described in a text. This study investigated predictive inference generation during second language (L2) reading, with a focus on the effects of strategy instructions. In this experiment, Japanese university students read several short narrative passages designed to…

Improved fuzzy PID controller design using predictive functional control structure.

PubMed

Wang, Yuzhong; Jin, Qibing; Zhang, Ridong

2017-11-01

In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Stability of distributed MPC in an intersection scenario

NASA Astrophysics Data System (ADS)

Sprodowski, T.; Pannek, J.

2015-11-01

The research topic of autonomous cars and the communication among them has attained much attention in the last years and is developing quickly. Among others, this research area spans fields such as image recognition, mathematical control theory, communication networks, and sensor fusion. We consider an intersection scenario where we divide the shared road space in different cells. These cells form a grid. The cars are modelled as an autonomous multi-agent system based on the Distributed Model Predictive Control algorithm (DMPC). We prove that the overall system reaches stability using Optimal Control for each multi-agent and demonstrate that by numerical results.

Effects of occupational therapy on quality of life of patients with metastatic prostate cancer. A randomized controlled study.

PubMed

Huri, Meral; Huri, Emre; Kayihan, Hulya; Altuntas, Onur

2015-08-01

To evaluate the efficiency of occupational therapy relative to a home program in improving quality of life (QoL) among men who were treated for metastatic prostate cancer (MPC). Fifty-five men were assigned randomly to either the 12-week cognitive behavioral therapy based occupational therapy (OT-CBSM) intervention (treatment group) or a home program (control group) between March 2012 and August 2014 in the Department of Occupational Therapy, Faculty of Health Sciences, Hacettepe University, Ankara, Turkey. The Canadian Occupational Performance Measure (COPM) was used to measure the occupational performance and identify difficulties in daily living activities. The QoL and symptom status were measured by The European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire C30 and its Prostate Cancer Module. A 12-week OT-CBSM intervention including client-centered training of daily living activities, recreational group activities, and cognitive behavioral stress management intervention were applied. The COPM performance and satisfaction scores, which indicate occupational participation and QoL increased statistically in the treatment group in relation to men who were included in the home-program (p less than or equal to 0.05). A 12-week OT-CBSM intervention was effective in improving QoL in men treated for MPC, and these changes were associated significantly with occupational performance.

Marine Corps Amphibious Combat Vehicle (ACV) and Marine Personnel Carrier (MPC): Background and Issues for Congress

DTIC Science & Technology

2016-09-09

amphibious like an AAV, EFV, or the ACV but instead would be required to have a swim capability for inland waterways such as rivers, lakes , and other...operations. On June 14, 2013, Marine leadership put the MPC program “on ice ” due to budgetary pressures but suggested the program might be resurrected... lakes , and other water obstacles such as shore-to-shore operations in the littorals. Because of a perceived amphibious “redundancy,” some have

Adaptive neural network motion control for aircraft under uncertainty conditions

NASA Astrophysics Data System (ADS)

Efremov, A. V.; Tiaglik, M. S.; Tiumentsev, Yu V.

2018-02-01

We need to provide motion control of modern and advanced aircraft under diverse uncertainty conditions. This problem can be solved by using adaptive control laws. We carry out an analysis of the capabilities of these laws for such adaptive systems as MRAC (Model Reference Adaptive Control) and MPC (Model Predictive Control). In the case of a nonlinear control object, the most efficient solution to the adaptive control problem is the use of neural network technologies. These technologies are suitable for the development of both a control object model and a control law for the object. The approximate nature of the ANN model was taken into account by introducing additional compensating feedback into the control system. The capabilities of adaptive control laws under uncertainty in the source data are considered. We also conduct simulations to assess the contribution of adaptivity to the behavior of the system.

Venous Thrombosis Risk after Cast Immobilization of the Lower Extremity: Derivation and Validation of a Clinical Prediction Score, L-TRiP(cast), in Three Population-Based Case–Control Studies

PubMed Central

Nemeth, Banne; van Adrichem, Raymond A.; van Hylckama Vlieg, Astrid; Bucciarelli, Paolo; Martinelli, Ida; Baglin, Trevor; Rosendaal, Frits R.; le Cessie, Saskia; Cannegieter, Suzanne C.

2015-01-01

) with 784 cases and 523 controls included between March 2003 and December 2008 and (2) the Milan study, a population-based case–control study with 2,117 cases and 2,088 controls selected between December 1993 and December 2010 at the Thrombosis Center, Fondazione IRCCS Ca’ Granda–Ospedale Maggiore Policlinico, Milan, Italy. The full model consisted of 32 predictors, including three genetic factors and six biomarkers. For this model, an AUC of 0.85 (95% CI 0.77–0.92) was found in individuals with plaster cast immobilization of the lower extremity. The AUC for the restricted model (containing 11 predictors, including two genetic factors and one biomarker) was 0.84 (95% CI 0.77–0.92). The clinical model (consisting of 14 environmental predictors) resulted in an AUC of 0.77 (95% CI 0.66–0.87). The clinical model was converted into a risk score, the L-TRiP(cast) score (Leiden–Thrombosis Risk Prediction for patients with cast immobilization score), which showed an AUC of 0.76 (95% CI 0.66–0.86). Validation in the THE-VTE study data resulted in an AUC of 0.77 (95% CI 0.58–0.96) for the L-TRiP(cast) score. Validation in the Milan study resulted in an AUC of 0.93 (95% CI 0.86–1.00) for the full model, an AUC of 0.92 (95% CI 0.76–0.87) for the restricted model, and an AUC of 0.96 (95% CI 0.92–0.99) for the clinical model. The L-TRiP(cast) score resulted in an AUC of 0.95 (95% CI 0.91–0.99). Major limitations of this study were that information on thromboprophylaxis was not available for patients who had plaster cast immobilization of the lower extremity and that blood was drawn 3 mo after the thrombotic event. Conclusions These results show that information on environmental risk factors, coagulation factors, and genetic determinants in patients with plaster casts leads to high accuracy in the prediction of VTE risk. In daily practice, the clinical model may be the preferred model as its factors are most easy to determine, while the model still has good

Marine Corps Amphibious Combat Vehicle (ACV) and Marine Personnel Carrier (MPC): Background and Issues for Congress

DTIC Science & Technology

2017-03-08

amphibious like an AAV, EFV, or the ACV but instead would be required to have a swim capability for inland waterways such as rivers, lakes , and other...operations. On June 14, 2013, Marine leadership put the MPC program “on ice ” due to budgetary pressures but suggested the program might be resurrected some...EFV, or the ACV but instead would be required to have a swim 1 capability for inland waterways such as rivers, lakes , and other water obstacles

Aircraft Trajectories Computation-Prediction-Control (La Trajectoire de l’Avion Calcul-Prediction-Controle). Volume 2

DTIC Science & Technology

1990-05-01

faire atterrir las a~ronefs sans recourir de faqon systimatique aux attentes habituelles; un de leurs coll~gues ayant contribu6 At la recherche de la...applicable to or usable for the management of the flows of aircraft and the control of individual flights, the integration of control phases over...February 1976. AIR TRAFFIC MANAGEMENT : Civil/Military Systems and Technologies Guidance and Control Symposium, Copenhagen, Denmark, 9-12 October 1979. AGARD

Galaxy And Mass Assembly (GAMA): blue spheroids within 87 Mpc

NASA Astrophysics Data System (ADS)

Mahajan, Smriti; Drinkwater, Michael J.; Driver, S.; Hopkins, A. M.; Graham, Alister W.; Brough, S.; Brown, Michael J. I.; Holwerda, B. W.; Owers, Matt S.; Pimbblet, Kevin A.

2018-03-01

In this paper, we test if nearby blue spheroid (BSph) galaxies may become the progenitors of star-forming spiral galaxies or passively evolving elliptical galaxies. Our sample comprises 428 galaxies of various morphologies in the redshift range 0.002 < z < 0.02 (8-87 Mpc) with panchromatic data from the Galaxy and Mass Assembly survey. We find that BSph galaxies are structurally (mean effective surface brightness, effective radius) very similar to their passively evolving red counterparts. However, their star formation and other properties such as colour, age, and metallicity are more like star-forming spirals than spheroids (ellipticals and lenticulars). We show that BSph galaxies are statistically distinguishable from other spheroids as well as spirals in the multidimensional space mapped by luminosity-weighted age, metallicity, dust mass, and specific star formation rate. We use H I data to reveal that some of the BSphs are (further) developing their discs, hence their blue colours. They may eventually become spiral galaxies - if sufficient gas accretion occurs - or more likely fade into low-mass red galaxies.

Resonant Frequency Control For the PIP-II Injector Test RFQ: Control Framework and Initial Results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edelen, A. L.; Biedron, S. G.; Milton, S. V.

For the PIP-II Injector Test (PI-Test) at Fermilab, a four-vane radio frequency quadrupole (RFQ) is designed to accelerate a 30-keV, 1-mA to 10-mA, H- beam to 2.1 MeV under both pulsed and continuous wave (CW) RF operation. The available headroom of the RF amplifiers limits the maximum allowable detuning to 3 kHz, and the detuning is controlled entirely via thermal regulation. Fine control over the detuning, minimal manual intervention, and fast trip recovery is desired. In addition, having active control over both the walls and vanes provides a wider tuning range. For this, we intend to use model predictive controlmore » (MPC). To facilitate these objectives, we developed a dedicated control framework that handles higher-level system decisions as well as executes control calculations. It is written in Python in a modular fashion for easy adjustments, readability, and portability. Here we describe the framework and present the first control results for the PI-Test RFQ under pulsed and CW operation.« less

Prediction of overall survival for metastatic pancreatic cancer: Development and validation of a prognostic nomogram with data from open clinical trial and real-world study.

PubMed

Hang, Junjie; Wu, Lixia; Zhu, Lina; Sun, Zhiqiang; Wang, Ge; Pan, Jingjing; Zheng, Suhua; Xu, Kequn; Du, Jiadi; Jiang, Hua

2018-06-01

It is necessary to develop prognostic tools of metastatic pancreatic cancer (MPC) for optimizing therapeutic strategies. Thus, we tried to develop and validate a prognostic nomogram of MPC. Data from 3 clinical trials (NCT00844649, NCT01124786, and NCT00574275) and 133 Chinese MPC patients were used for analysis. The former 2 trials were taken as the training cohort while NCT00574275 was used as the validation cohort. In addition, 133 MPC patients treated in China were taken as the testing cohort. Cox regression model was used to investigate prognostic factors in the training cohort. With these factors, we established a nomogram and verified it by Harrell's concordance index (C-index) and calibration plots. Furthermore, the nomogram was externally validated in the validation cohort and testing cohort. In the training cohort (n = 445), performance status, liver metastasis, Carbohydrate antigen 19-9 (CA19-9) log-value, absolute neutrophil count (ANC), and albumin were independent prognostic factors for overall survival (OS). A nomogram was established with these factors to predict OS and survival probabilities. The nomogram showed an acceptable discrimination ability (C-index: .683) and good calibration, and was further externally validated in the validation cohort (n = 273, C-index: .699) and testing cohort (n = 133, C-index: .653).The nomogram total points (NTP) had the potential to stratify patients into 3-risk groups with median OS of 11.7, 7.0 and 3.7 months (P < .001), respectively. In conclusion, the prognostic nomogram with NTP can predict OS for patients with MPC with considerable accuracy. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Centipod WEC, Advanced Controls, Resultant LCOE

DOE Data Explorer

McCall, Alan

2016-02-15

Project resultant LCOE model after implementation of MPC controller. Contains AEP, CBS, model documentation, and LCOE content model. This is meant for comparison with this project's baseline LCOE model.

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.

GHOSTS I: A new faint very isolated dwarf galaxy at D = 12 ± 2 Mpc

DOE Office of Scientific and Technical Information (OSTI.GOV)

Monachesi, Antonela; Bell, Eric F.; Radburn-Smith, David J.

2014-01-10

We report the discovery of a new faint dwarf galaxy, GHOSTS I, using HST/ACS data from one of our GHOSTS (Galaxy Halos, Outer disks, Substructure, Thick disk, and Star clusters) fields. Its detected individual stars populate an approximately 1 mag range of its luminosity function (LF). Using synthetic color-magnitude diagrams (CMDs) to compare with the galaxy's CMD, we find that the colors and magnitudes of GHOSTS I's individual stars are most consistent with being young helium-burning and asymptotic giant branch stars at a distance of ∼12 ± 2 Mpc. Morphologically, GHOSTS I appears to be actively forming stars, so wemore » tentatively classify it as a dwarf irregular (dIrr) galaxy, although future Hubble Space Telescope (HST) observations deep enough to resolve a larger magnitude range in its LF are required to make a more secure classification. GHOSTS I's absolute magnitude is M{sub V}∼−9.85{sub −0.33}{sup +0.40}, making it one of the least luminous dIrr galaxies known, and its metallicity is lower than [Fe/H] = –1.5 dex. The half-light radius of GHOSTS I is 226 ± 38 pc and its ellipticity is 0.47 ± 0.07, similar to Milky Way and M31 dwarf satellites at comparable luminosity. There are no luminous massive galaxies or galaxy clusters within ∼4 Mpc from GHOSTS I that could be considered as its host, making it a very isolated dwarf galaxy in the local universe.« less

Predictive displays for a process-control schematic interface.

PubMed

Yin, Shanqing; Wickens, Christopher D; Helander, Martin; Laberge, Jason C

2015-02-01

Our objective was to examine the extent to which increasing precision of predictive (rate of change) information in process control will improve performance on a simulated process-control task. Predictive displays have been found to be useful in process control (as well as aviation and maritime industries). However, authors of prior research have not examined the extent to which predictive value is increased by increasing predictor resolution, nor has such research tied potential improvements to changes in process control strategy. Fifty nonprofessional participants each controlled a simulated chemical mixture process (honey mixer simulation) that simulated the operations found in process control. Participants in each of five groups controlled with either no predictor or a predictor ranging in the resolution of prediction of the process. Increasing detail resolution generally increased the benefit of prediction over the control condition although not monotonically so. The best overall performance, combining quality and predictive ability, was obtained by the display of intermediate resolution. The two displays with the lowest resolution were clearly inferior. Predictors with higher resolution are of value but may trade off enhanced sensitivity to variable change (lower-resolution discrete state predictor) with smoother control action (higher-resolution continuous predictors). The research provides guidelines to the process-control industry regarding displays that can most improve operator performance.

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.

Optimal Control of a Surge-Mode WEC in Random Waves

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chertok, Allan; Ceberio, Olivier; Staby, Bill

2016-08-30

The objective of this project was to develop one or more real-time feedback and feed-forward (MPC) control algorithms for an Oscillating Surge Wave Converter (OSWC) developed by RME called SurgeWEC™ that leverages recent innovations in wave energy converter (WEC) control theory to maximize power production in random wave environments. The control algorithms synthesized innovations in dynamic programming and nonlinear wave dynamics using anticipatory wave sensors and localized sensor measurements; e.g. position and velocity of the WEC Power Take Off (PTO), with predictive wave forecasting data. The result was an advanced control system that uses feedback or feed-forward data from anmore » array of sensor channels comprised of both localized and deployed sensors fused into a single decision process that optimally compensates for uncertainties in the system dynamics, wave forecasts, and sensor measurement errors.« less

Neural control of fast nonlinear systems--application to a turbocharged SI engine with VCT.

PubMed

Colin, Guillaume; Chamaillard, Yann; Bloch, Gérard; Corde, Gilles

2007-07-01

Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, but coordinated, control modules. Theses modules are based on different control strategies: internal model control (IMC), model predictive control (MPC), and optimal control. It is shown how neural models can be used at different levels and included in the control modules to replace physical models, which are too complex to be online embedded, or to estimate nonmeasured variables. The results obtained from two different test benches show the real-time applicability and good control performance of the proposed methods.

The clustering of z > 7 galaxies: predictions from the BLUETIDES simulation

NASA Astrophysics Data System (ADS)

Bhowmick, Aklant K.; Di Matteo, Tiziana; Feng, Yu; Lanusse, Francois

2018-03-01

We study the clustering of the highest z galaxies (from ˜0.1 to a few tens Mpc scales) using the BLUETIDES simulation and compare it to current observational constraints from Hubble legacy and Hyper Suprime Cam (HSC) fields (at z = 6-7.2). With a box length of 400 Mpc h-1 on each side and 0.7 trillion particles, BLUETIDES is the largest volume high-resolution cosmological hydrodynamic simulation to date ideally suited for studies of high-z galaxies. We find that galaxies with magnitude mUV < 27.7 have a bias (bg) of 8.1 ± 1.2 at z = 8, and typical halo masses MH ≳ 6 × 1010 M⊙. Given the redshift evolution between z = 8 and z = 10 [bg ∝ (1 + z)1.6], our inferred values of the bias and halo masses are consistent with measured angular clustering at z ˜ 6.8 from these brighter samples. The bias of fainter galaxies (in the Hubble legacy field at H160 ≲ 29.5) is 5.9 ± 0.9 at z = 8 corresponding to halo masses MH ≳ 1010 M⊙. We investigate directly the 1-halo term in the clustering and show that it dominates on scales r ≲ 0.1 Mpc h-1 (Θ ≲ 3 arcsec) with non-linear effect at transition scales between the one-halo and two-halo term affecting scales 0.1 Mpc h-1≲ r ≲ 20 Mpc h-1 (3 arcsec ≲ Θ ≲ 90 arcsec). Current clustering measurements probe down to the scales in the transition between one-halo and two-halo regime where non-linear effects are important. The amplitude of the one-halo term implies that occupation numbers for satellites in BLUETIDES are somewhat higher than standard halo occupation distributions adopted in these analyses (which predict amplitudes in the one-halo regime suppressed by a factor 2-3). That possibly implies a higher number of galaxies detected by JWST (at small scales and even fainter magnitudes) observing these fields.

Using OPC technology to support the study of advanced process control.

PubMed

Mahmoud, Magdi S; Sabih, Muhammad; Elshafei, Moustafa

2015-03-01

OPC, originally the Object Linking and Embedding (OLE) for Process Control, brings a broad communication opportunity between different kinds of control systems. This paper investigates the use of OPC technology for the study of distributed control systems (DCS) as a cost effective and flexible research tool for the development and testing of advanced process control (APC) techniques in university research centers. Co-Simulation environment based on Matlab, LabVIEW and TCP/IP network is presented here. Several implementation issues and OPC based client/server control application have been addressed for TCP/IP network. A nonlinear boiler model is simulated as OPC server and OPC client is used for closed loop model identification, and to design a Model Predictive Controller. The MPC is able to control the NOx emissions in addition to drum water level and steam pressure. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Prediction of L70 lumen maintenance and chromaticity for LEDs using extended Kalman filter models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lall, Pradeep; Wei, Junchao; Davis, Lynn

2013-09-30

Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is definedmore » by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying damage using physics-based models. Life

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.

Signal-3L: A 3-layer approach for predicting signal peptides.

PubMed

Shen, Hong-Bin; Chou, Kuo-Chen

2007-11-16

Functioning as an "address tag" that directs nascent proteins to their proper cellular and extracellular locations, signal peptides have become a crucial tool in finding new drugs or reprogramming cells for gene therapy. To effectively and timely use such a tool, however, the first important thing is to develop an automated method for rapidly and accurately identifying the signal peptide for a given nascent protein. With the avalanche of new protein sequences generated in the post-genomic era, the challenge has become even more urgent and critical. In this paper, we have developed a novel method for predicting signal peptide sequences and their cleavage sites in human, plant, animal, eukaryotic, Gram-positive, and Gram-negative protein sequences, respectively. The new predictor is called Signal-3L that consists of three prediction engines working, respectively, for the following three progressively deepening layers: (1) identifying a query protein as secretory or non-secretory by an ensemble classifier formed by fusing many individual OET-KNN (optimized evidence-theoretic K nearest neighbor) classifiers operated in various dimensions of PseAA (pseudo amino acid) composition spaces; (2) selecting a set of candidates for the possible signal peptide cleavage sites of a query secretory protein by a subsite-coupled discrimination algorithm; (3) determining the final cleavage site by fusing the global sequence alignment outcome for each of the aforementioned candidates through a voting system. Signal-3L is featured by high success prediction rates with short computational time, and hence is particularly useful for the analysis of large-scale datasets. Signal-3L is freely available as a web-server at http://chou.med.harvard.edu/bioinf/Signal-3L/ or http://202.120.37.186/bioinf/Signal-3L, where, to further support the demand of the related areas, the signal peptides identified by Signal-3L for all the protein entries in Swiss-Prot databank that do not have signal peptide

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.

Robot trajectory tracking with self-tuning predicted control

NASA Technical Reports Server (NTRS)

Cui, Xianzhong; Shin, Kang G.

1988-01-01

A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.

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.

77 FR 36040 - Watco Holdings, Inc.-Continuance in Control Exemption-San Antonio Central Railroad, L.L.C.

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-15

..., Inc.--Continuance in Control Exemption--San Antonio Central Railroad, L.L.C. Watco Holdings, Inc... Watco to continue in control of San Antonio Central Railroad, L.L.C. (SAC), upon SAC's becoming a Class... exemption in San Antonio Central Railroad, L.L.C.--Lease Exemption-- Port Authority of San Antonio, Docket...

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.

Prediction algorithms for urban traffic control

DOT National Transportation Integrated Search

1979-02-01

The objectives of this study are to 1) review and assess the state-of-the-art of prediction algorithms for urban traffic control in terms of their accuracy and application, and 2) determine the prediction accuracy obtainable by examining the performa...

A real-time control framework for urban water reservoirs operation

NASA Astrophysics Data System (ADS)

Galelli, S.; Goedbloed, A.; Schwanenberg, D.

2012-04-01

Drinking water demand in urban areas is growing parallel to the worldwide urban population, and it is acquiring an increasing part of the total water consumption. Since the delivery of sufficient water volumes in urban areas represents a difficult logistic and economical problem, different metropolitan areas are evaluating the opportunity of constructing relatively small reservoirs within urban areas. Singapore, for example, is developing the so-called 'Four National Taps Strategies', which detects the maximization of water yields from local, urban catchments as one of the most important water sources. However, the peculiar location of these reservoirs can provide a certain advantage from the logistical point of view, but it can pose serious difficulties in their daily management. Urban catchments are indeed characterized by large impervious areas: this results in a change of the hydrological cycle, with decreased infiltration and groundwater recharge, and increased patterns of surface and river discharges, with higher peak flows, volumes and concentration time. Moreover, the high concentrations of nutrients and sediments characterizing urban discharges can cause further water quality problems. In this critical hydrological context, the effective operation of urban water reservoirs must rely on real-time control techniques, which can exploit hydro-meteorological information available in real-time from hydrological and nowcasting models. This work proposes a novel framework for the real-time control of combined water quality and quantity objectives in urban reservoirs. The core of this framework is a non-linear Model Predictive Control (MPC) scheme, which employs the current state of the system, the future discharges furnished by a predictive model and a further model describing the internal dynamics of the controlled sub-system to determine an optimal control sequence over a finite prediction horizon. The main advantage of this scheme stands in its reduced

Predictive Control of Speededness in Adaptive Testing

ERIC Educational Resources Information Center

van der Linden, Wim J.

2009-01-01

An adaptive testing method is presented that controls the speededness of a test using predictions of the test takers' response times on the candidate items in the pool. Two different types of predictions are investigated: posterior predictions given the actual response times on the items already administered and posterior predictions that use the…

76 FR 77889 - Watco Holdings, Inc.-Continuance in Control Exemption-Swan Ranch Railroad, L.L.C.

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-14

... DEPARTMENT OF TRANSPORTATION Surface Transportation Board [Docket No. FD 35575] Watco Holdings, Inc.--Continuance in Control Exemption--Swan Ranch Railroad, L.L.C. Watco Holdings, Inc. (Watco) has filed a verified notice of exemption pursuant to 49 CFR 1180.2(d)(2) to continue in control of Swan Ranch Railroad, L.L.C. (SRR), upon SRR's becoming ...

76 FR 34627 - Proposed Modification of Offshore Airspace Areas: Norton Sound Low, Control 1234L and Control...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-14

...: Norton Sound Low, Control 1234L and Control 1487L; Alaska AGENCY: Federal Aviation Administration (FAA... Low, Control 1234L, and Control 1487L Offshore Airspace Areas in Alaska. The airspace floors would be... there is a requirement to provide Instrument Flight Rules (IFR) en route Air Traffic Control (ATC...

Predictive Hyperglycemia and Hypoglycemia Minimization: In-Home Evaluation of Safety, Feasibility, and Efficacy in Overnight Glucose Control in Type 1 Diabetes.

PubMed

Spaic, Tamara; Driscoll, Marsha; Raghinaru, Dan; Buckingham, Bruce A; Wilson, Darrell M; Clinton, Paula; Chase, H Peter; Maahs, David M; Forlenza, Gregory P; Jost, Emily; Hramiak, Irene; Paul, Terri; Bequette, B Wayne; Cameron, Faye; Beck, Roy W; Kollman, Craig; Lum, John W; Ly, Trang T

2017-03-01

The objective of this study was to determine the safety, feasibility, and efficacy of a predictive hyperglycemia and hypoglycemia minimization (PHHM) system compared with predictive low-glucose insulin suspension (PLGS) alone in overnight glucose control. A 42-night trial was conducted in 30 individuals with type 1 diabetes in the age range 15-45 years. Participants were randomly assigned each night to either PHHM or PLGS and were blinded to the assignment. The system suspended the insulin pump on both the PHHM and PLGS nights for predicted hypoglycemia but delivered correction boluses for predicted hyperglycemia on PHHM nights only. The primary outcome was the percentage of time spent in a sensor glucose range of 70-180 mg/dL during the overnight period. The addition of automated insulin delivery with PHHM increased the time spent in the target range (70-180 mg/dL) from 71 ± 10% during PLGS nights to 78 ± 10% during PHHM nights ( P < 0.001). The average morning blood glucose concentration improved from 163 ± 23 mg/dL after PLGS nights to 142 ± 18 mg/dL after PHHM nights ( P < 0.001). Various sensor-measured hypoglycemic outcomes were similar on PLGS and PHHM nights. All participants completed 42 nights with no episodes of severe hypoglycemia, diabetic ketoacidosis, or other study- or device-related adverse events. The addition of a predictive hyperglycemia minimization component to our existing PLGS system was shown to be safe, feasible, and effective in overnight glucose control. © 2017 by the American Diabetes Association.

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.

Drinking without thinking: an implicit measure of alcohol motivation predicts failure to control alcohol use.

PubMed

Ostafin, Brian D; Marlatt, G Alan; Greenwald, Anthony G

2008-11-01

Addiction is characterized by dyscontrol - substance use despite intentions to restrain. Using a sample of at-risk drinkers, the present study examined whether an implicit measure of alcohol motivation (the Implicit Association Test [IAT]; Greenwald, A.G., McGhee, D.E., & Schwartz, J.L.K. (1998). Measuring individual differences in implicit cognition: the Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464-1480) would predict dyscontrol of alcohol use. Participants completed an IAT and, to elicit motivation to restrain alcohol use, were instructed that greater consumption in a taste test would impair performance on a later task for which they could win a prize. All participants viewed aversive slides and then completed a thought-listing task. Participants either exerted self-control by suppressing negative affect and thoughts regarding the slides or did not exert self-control. Post-manipulation, the groups did not differ in mood, urge to drink or motivation to restrain consumption. During the subsequent taste test, participants whose self-control resources were depleted consumed more alcohol than did those in the control group. Additionally, the IAT, but not an explicit measure of alcohol motivation, more strongly predicted alcohol use when self-control resources were depleted. The results indicate that the IAT may have utility in predicting dyscontrolled alcohol use.

Language-independent talker-specificity in first-language and second-language speech production by bilingual talkers: L1 speaking rate predicts L2 speaking rate

PubMed Central

Bradlow, Ann R.; Kim, Midam; Blasingame, Michael

2017-01-01

Second-language (L2) speech is consistently slower than first-language (L1) speech, and L1 speaking rate varies within- and across-talkers depending on many individual, situational, linguistic, and sociolinguistic factors. It is asked whether speaking rate is also determined by a language-independent talker-specific trait such that, across a group of bilinguals, L1 speaking rate significantly predicts L2 speaking rate. Two measurements of speaking rate were automatically extracted from recordings of read and spontaneous speech by English monolinguals (n = 27) and bilinguals from ten L1 backgrounds (n = 86): speech rate (syllables/second), and articulation rate (syllables/second excluding silent pauses). Replicating prior work, L2 speaking rates were significantly slower than L1 speaking rates both across-groups (monolinguals' L1 English vs bilinguals' L2 English), and across L1 and L2 within bilinguals. Critically, within the bilingual group, L1 speaking rate significantly predicted L2 speaking rate, suggesting that a significant portion of inter-talker variation in L2 speech is derived from inter-talker variation in L1 speech, and that individual variability in L2 spoken language production may be best understood within the context of individual variability in L1 spoken language production. PMID:28253679

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.

Model predictive control for spacecraft rendezvous in elliptical orbit

NASA Astrophysics Data System (ADS)

Li, Peng; Zhu, Zheng H.

2018-05-01

This paper studies the control of spacecraft rendezvous with attitude stable or spinning targets in an elliptical orbit. The linearized Tschauner-Hempel equation is used to describe the motion of spacecraft and the problem is formulated by model predictive control. The control objective is to maximize control accuracy and smoothness simultaneously to avoid unexpected change or overshoot of trajectory for safe rendezvous. It is achieved by minimizing the weighted summations of control errors and increments. The effects of two sets of horizons (control and predictive horizons) in the model predictive control are examined in terms of fuel consumption, rendezvous time and computational effort. The numerical results show the proposed control strategy is effective.

Outpatient Closed-Loop Control with Unannounced Moderate Exercise in Adolescents Using Zone Model Predictive Control

PubMed Central

Huyett, Lauren M.; Ly, Trang T.; Forlenza, Gregory P.; Reuschel-DiVirgilio, Suzette; Messer, Laurel H.; Wadwa, R. Paul; Gondhalekar, Ravi; Doyle, Francis J.; Pinsker, Jordan E.; Maahs, David M.; Buckingham, Bruce A.

2017-01-01

Abstract Background: The artificial pancreas (AP) has the potential to improve glycemic control in adolescents. This article presents the first evaluation in adolescents of the Zone Model Predictive Control and Health Monitoring System (ZMPC+HMS) AP algorithms, and their first evaluation in a supervised outpatient setting with frequent exercise. Materials and Methods: Adolescents with type 1 diabetes underwent 3 days of closed-loop control (CLC) in a hotel setting with the ZMPC+HMS algorithms on the Diabetes Assistant platform. Subjects engaged in twice-daily exercise, including soccer, tennis, and bicycling. Meal size (unrestricted) was estimated and entered into the system by subjects to trigger a bolus, but exercise was not announced. Results: Ten adolescents (11.9–17.7 years) completed 72 h of CLC, with data on 95 ± 14 h of sensor-augmented pump (SAP) therapy before CLC as a comparison to usual therapy. The percentage of time with continuous glucose monitor (CGM) 70–180 mg/dL was 71% ± 10% during CLC, compared to 57% ± 16% during SAP (P = 0.012). Nocturnal control during CLC was safe, with 0% (0%, 0.6%) of time with CGM <70 mg/dL compared to 1.1% (0.0%, 14%) during SAP. Despite large meals (estimated up to 120 g carbohydrate), only 8.0% ± 6.9% of time during CLC was spent with CGM >250 mg/dL (16% ± 14% during SAP). The system remained connected in CLC for 97% ± 2% of the total study time. No adverse events or severe hypoglycemia occurred. Conclusions: The use of the ZMPC+HMS algorithms is feasible in the adolescent outpatient environment and achieved significantly more time in the desired glycemic range than SAP in the face of unannounced exercise and large announced meal challenges. PMID:28459617

Development of Physical Protection Regulations for Rosatom State Corporation Sites under the U.S.-Russian MPC&A Program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Izmaylov, Alexander; Babkin, Vladimir; Shemigon, Nikolai N.

2012-07-14

This paper describes issues related to upgrading the physical protection regulatory basis for Rosatom State Corporation sites. It is underlined that most of the regulatory and methodological documents for this subject area have been developed under the U.S.-Russian MPC&A Program. According to the joint management plan developed and agreed upon by the parties in 2005, nearly 50 physical protection documents were identified to be developed, approved and implemented at Rosatom sites by 2012. It is also noted that, on the whole, the plans have been fulfilled.

Analytic prediction of baryonic effects from the EFT of large scale structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lewandowski, Matthew; Perko, Ashley; Senatore, Leonardo, E-mail: mattlew@stanford.edu, E-mail: perko@stanford.edu, E-mail: senatore@stanford.edu

2015-05-01

The large scale structures of the universe will likely be the next leading source of cosmological information. It is therefore crucial to understand their behavior. The Effective Field Theory of Large Scale Structures provides a consistent way to perturbatively predict the clustering of dark matter at large distances. The fact that baryons move distances comparable to dark matter allows us to infer that baryons at large distances can be described in a similar formalism: the backreaction of short-distance non-linearities and of star-formation physics at long distances can be encapsulated in an effective stress tensor, characterized by a few parameters. Themore » functional form of baryonic effects can therefore be predicted. In the power spectrum the leading contribution goes as ∝ k{sup 2} P(k), with P(k) being the linear power spectrum and with the numerical prefactor depending on the details of the star-formation physics. We also perform the resummation of the contribution of the long-wavelength displacements, allowing us to consistently predict the effect of the relative motion of baryons and dark matter. We compare our predictions with simulations that contain several implementations of baryonic physics, finding percent agreement up to relatively high wavenumbers such as k ≅ 0.3 hMpc{sup −1} or k ≅ 0.6 hMpc{sup −1}, depending on the order of the calculation. Our results open a novel way to understand baryonic effects analytically, as well as to interface with simulations.« less

L70 life prediction for solid state lighting using Kalman Filter and Extended Kalman Filter based models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lall, Pradeep; Wei, Junchao; Davis, Lynn

2013-08-08

Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is definedmore » by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying damage using physics-based models. Life

Prediction of Process-Induced Distortions in L-Shaped Composite Profiles Using Path-Dependent Constitutive Law

NASA Astrophysics Data System (ADS)

Ding, Anxin; Li, Shuxin; Wang, Jihui; Ni, Aiqing; Sun, Liangliang; Chang, Lei

2016-10-01

In this paper, the corner spring-in angles of AS4/8552 L-shaped composite profiles with different thicknesses are predicted using path-dependent constitutive law with the consideration of material properties variation due to phase change during curing. The prediction accuracy mainly depends on the properties in the rubbery and glassy states obtained by homogenization method rather than experimental measurements. Both analytical and finite element (FE) homogenization methods are applied to predict the overall properties of AS4/8552 composite. The effect of fiber volume fraction on the properties is investigated for both rubbery and glassy states using both methods. And the predicted results are compared with experimental measurements for the glassy state. Good agreement is achieved between the predicted results and available experimental data, showing the reliability of the homogenization method. Furthermore, the corner spring-in angles of L-shaped composite profiles are measured experimentally and the reliability of path-dependent constitutive law is validated as well as the properties prediction by FE homogenization method.

A receding horizon sliding control approach for electric powertrains with backlash and flexible half-shafts

NASA Astrophysics Data System (ADS)

Li, Yutong; Hansen, Andreas; Karl Hedrick, J.; Zhang, Junzhi

2017-12-01

Active control of electric powertrains is challenging, due to the fact that backlash and structural flexibility in transmission components can cause severe performance degradation or even instability of the control system. Furthermore, high impact forces in transmissions reduce driving comfort and possibly lead to damage of the mechanical elements in contact. In this paper, a nonlinear electric powertrain is modelled as a piecewise affine (PWA) system. The novel receding horizon sliding control (RHSC) idea is extended to constrained PWA systems and utilised to systematically address the active control problem for electric powertrains. Simulations are conducted in Matlab/Simulink in conjunction with the high fidelity Carsim software. RHSC shows superior jerk suppression and target wheel speed tracking performance as well as reduced computational cost over classical model predictive control (MPC). This indicates the newly proposed RHSC is an effective method to address the active control problem for electric powertrains.

Basic Research on Adaptive Model Algorithmic Control

DTIC Science & Technology

1985-12-01

Control Conference. Richalet, J., A. Rault, J.L. Testud and J. Papon (1978). Model predictive heuristic control: applications to industrial...pp.977-982. Richalet, J., A. Rault, J. L. Testud and J. Papon (1978). Model predictive heuristic control: applications to industrial processes

Modelling and control for laser based welding processes: modern methods of process control to improve quality of laser-based joining methods

NASA Astrophysics Data System (ADS)

Zäh, Ralf-Kilian; Mosbach, Benedikt; Hollwich, Jan; Faupel, Benedikt

2017-02-01

To ensure the competitiveness of manufacturing companies it is indispensable to optimize their manufacturing processes. Slight variations of process parameters and machine settings have only marginally effects on the product quality. Therefore, the largest possible editing window is required. Such parameters are, for example, the movement of the laser beam across the component for the laser keyhole welding. That`s why it is necessary to keep the formation of welding seams within specified limits. Therefore, the quality of laser welding processes is ensured, by using post-process methods, like ultrasonic inspection, or special in-process methods. These in-process systems only achieve a simple evaluation which shows whether the weld seam is acceptable or not. Furthermore, in-process systems use no feedback for changing the control variables such as speed of the laser or adjustment of laser power. In this paper the research group presents current results of the research field of Online Monitoring, Online Controlling and Model predictive controlling in laser welding processes to increase the product quality. To record the characteristics of the welding process, tested online methods are used during the process. Based on the measurement data, a state space model is ascertained, which includes all the control variables of the system. Depending on simulation tools the model predictive controller (MPC) is designed for the model and integrated into an NI-Real-Time-System.

76 FR 78080 - Watco Holdings, Inc. and Watco Transportation Services, L.L.C.-Acquisition of Control Exemption...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-15

..., Inc. and Watco Transportation Services, L.L.C.-- Acquisition of Control Exemption--Wisconsin & Southern Railroad, L.L.C. Watco Holdings, Inc. (Watco Holdings) and Watco Transportation Services, L.L.C... Wisconsin & Southern Railroad, L.L.C., a Class II railroad. Watco intends to consummate the transaction on...

PD-L1 (CD274) promoter methylation predicts survival in colorectal cancer patients.

PubMed

Goltz, Diane; Gevensleben, Heidrun; Dietrich, Jörn; Dietrich, Dimo

2017-01-01

This study evaluates promoter methylation of the programmed cell death ligand 1 (PD-L1) as a biomarker in a cohort of 383 colorectal cancer patients. PD-L1 methylation (m PD-L1 ) was inversely correlated with PD-L1 mRNA expression ( p = 0.001) and was associated with significantly shorter overall survival (OS, p = 0.003) and recurrence-free survival (RFS, p < 0.001). In age-stratified multivariate Cox proportional hazards analyses including sex, tumor, nodal, distant metastasis categories, microsatellite instability (MSI)-status, and PD-L1 mRNA, m PD-L1 is classified as an independent prognostic factor (OS: p = 0.030; RFS: p < 0.001). Further studies are needed to evaluate PD-L1 methylation as a biomarker for response prediction of immunotherapies targeting the PD-1/PD-L1 axis.

Robust current control-based generalized predictive control with sliding mode disturbance compensation for PMSM drives.

PubMed

Liu, Xudong; Zhang, Chenghui; Li, Ke; Zhang, Qi

2017-11-01

This paper addresses the current control of permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and disturbances. A generalized predictive current control method combined with sliding mode disturbance compensation is proposed to satisfy the requirement of fast response and strong robustness. Firstly, according to the generalized predictive control (GPC) theory based on the continuous time model, a predictive current control method is presented without considering the disturbance, which is convenient to be realized in the digital controller. In fact, it's difficult to derive the exact motor model and parameters in the practical system. Thus, a sliding mode disturbance compensation controller is studied to improve the adaptiveness and robustness of the control system. The designed controller attempts to combine the merits of both predictive control and sliding mode control, meanwhile, the controller parameters are easy to be adjusted. Lastly, the proposed controller is tested on an interior PMSM by simulation and experiment, and the results indicate that it has good performance in both current tracking and disturbance rejection. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Coordinated path-following and direct yaw-moment control of autonomous electric vehicles with sideslip angle estimation

NASA Astrophysics Data System (ADS)

Guo, Jinghua; Luo, Yugong; Li, Keqiang; Dai, Yifan

2018-05-01

This paper presents a novel coordinated path following system (PFS) and direct yaw-moment control (DYC) of autonomous electric vehicles via hierarchical control technique. In the high-level control law design, a new fuzzy factor is introduced based on the magnitude of longitudinal velocity of vehicle, a linear time varying (LTV)-based model predictive controller (MPC) is proposed to acquire the wheel steering angle and external yaw moment. Then, a pseudo inverse (PI) low-level control allocation law is designed to realize the tracking of desired external moment torque and management of the redundant tire actuators. Furthermore, the vehicle sideslip angle is estimated by the data fusion of low-cost GPS and INS, which can be obtained by the integral of modified INS signals with GPS signals as initial value. Finally, the effectiveness of the proposed control system is validated by the simulation and experimental tests.

Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy.

PubMed

Brogden, Kim A; Parashar, Deepak; Hallier, Andrea R; Braun, Terry; Qian, Fang; Rizvi, Naiyer A; Bossler, Aaron D; Milhem, Mohammed M; Chan, Timothy A; Abbasi, Taher; Vali, Shireen

2018-02-27

Programmed Death Ligand 1 (PD-L1) is a co-stimulatory and immune checkpoint protein. PD-L1 expression in non-small cell lung cancers (NSCLC) is a hallmark of adaptive resistance and its expression is often used to predict the outcome of Programmed Death 1 (PD-1) and PD-L1 immunotherapy treatments. However, clinical benefits do not occur in all patients and new approaches are needed to assist in selecting patients for PD-1 or PD-L1 immunotherapies. Here, we hypothesized that patient tumor cell genomics influenced cell signaling and expression of PD-L1, chemokines, and immunosuppressive molecules and these profiles could be used to predict patient clinical responses. We used a recent dataset from NSCLC patients treated with pembrolizumab. Deleterious gene mutational profiles in patient exomes were identified and annotated into a cancer network to create NSCLC patient-specific predictive computational simulation models. Validation checks were performed on the cancer network, simulation model predictions, and PD-1 match rates between patient-specific predicted and clinical responses. Expression profiles of these 24 chemokines and immunosuppressive molecules were used to identify patients who would or would not respond to PD-1 immunotherapy. PD-L1 expression alone was not sufficient to predict which patients would or would not respond to PD-1 immunotherapy. Adding chemokine and immunosuppressive molecule expression profiles allowed patient models to achieve a greater than 85.0% predictive correlation among predicted and reported patient clinical responses. Our results suggested that chemokine and immunosuppressive molecule expression profiles can be used to accurately predict clinical responses thus differentiating among patients who would and would not benefit from PD-1 or PD-L1 immunotherapies.

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.

Aircraft Trajectories Computation-Prediction-Control. Volume 1 (La Trajectoire de l’Avion Calcul-Prediction-Controle)

DTIC Science & Technology

1990-03-01

knowledge covering problems of this type is called calculus of variations or optimal control theory (Refs. 1-8). As stated before, appli - cations occur...to the optimality conditions and the feasibility equations of Problem (GP), respectively. Clearly, after the transformation (26) is applied , the...trajectories, the primal sequential gradient-restoration algorithm (PSGRA) is applied to compute optimal trajectories for aeroassisted orbital transfer

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.

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.

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

Prediction, Control and the Challenge to Complexity

ERIC Educational Resources Information Center

Radford, Mike

2008-01-01

The dominant discourse in research, management and teaching is one that may loosely be characterised as that of prediction and control. The objective of research is to identify causal correlations within policy, management, teaching strategies and educational outcomes that are sufficiently robust as to be able to predict outcomes and make…

Control algorithms for aerobraking in the Martian atmosphere

NASA Technical Reports Server (NTRS)

Ward, Donald T.; Shipley, Buford W., Jr.

1991-01-01

The Analytic Predictor Corrector (APC) and Energy Controller (EC) atmospheric guidance concepts were adapted to control an interplanetary vehicle aerobraking in the Martian atmosphere. Changes are made to the APC to improve its robustness to density variations. These changes include adaptation of a new exit phase algorithm, an adaptive transition velocity to initiate the exit phase, refinement of the reference dynamic pressure calculation and two improved density estimation techniques. The modified controller with the hybrid density estimation technique is called the Mars Hybrid Predictor Corrector (MHPC), while the modified controller with a polynomial density estimator is called the Mars Predictor Corrector (MPC). A Lyapunov Steepest Descent Controller (LSDC) is adapted to control the vehicle. The LSDC lacked robustness, so a Lyapunov tracking exit phase algorithm is developed to guide the vehicle along a reference trajectory. This algorithm, when using the hybrid density estimation technique to define the reference path, is called the Lyapunov Hybrid Tracking Controller (LHTC). With the polynomial density estimator used to define the reference trajectory, the algorithm is called the Lyapunov Tracking Controller (LTC). These four new controllers are tested using a six degree of freedom computer simulation to evaluate their robustness. The MHPC, MPC, LHTC, and LTC show dramatic improvements in robustness over the APC and EC.

Choosing the appropriate forecasting model for predictive parameter control.

PubMed

Aleti, Aldeida; Moser, Irene; Meedeniya, Indika; Grunske, Lars

2014-01-01

All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.

Integrated Flight/Structural Mode Control for Very Flexible Aircraft Using L1 Adaptive Output Feedback Controller

NASA Technical Reports Server (NTRS)

Che, Jiaxing; Cao, Chengyu; Gregory, Irene M.

2012-01-01

This paper explores application of adaptive control architecture to a light, high-aspect ratio, flexible aircraft configuration that exhibits strong rigid body/flexible mode coupling. Specifically, an L(sub 1) adaptive output feedback controller is developed for a semi-span wind tunnel model capable of motion. The wind tunnel mount allows the semi-span model to translate vertically and pitch at the wing root, resulting in better simulation of an aircraft s rigid body motion. The control objective is to design a pitch control with altitude hold while suppressing body freedom flutter. The controller is an output feedback nominal controller (LQG) augmented by an L(sub 1) adaptive loop. A modification to the L(sub 1) output feedback is proposed to make it more suitable for flexible structures. The new control law relaxes the required bounds on the unmatched uncertainty and allows dependence on the state as well as time, i.e. a more general unmatched nonlinearity. The paper presents controller development and simulated performance responses. Simulation is conducted by using full state flexible wing models derived from test data at 10 different dynamic pressure conditions. An L(sub 1) adaptive output feedback controller is designed for a single test point and is then applied to all the test cases. The simulation results show that the L(sub 1) augmented controller can stabilize and meet the performance requirements for all 10 test conditions ranging from 30 psf to 130 psf dynamic pressure.

A facile FeBr3 based photoATRP for surface modification of mesoporous silica nanoparticles for controlled delivery cisplatin

NASA Astrophysics Data System (ADS)

Huang, Long; Liu, Meiying; Mao, Liucheng; Huang, Qiang; Huang, Hongye; Zeng, Guangjian; Tian, Jianwen; Wen, Yuanqing; Zhang, Xiaoyong; Wei, Yen

2018-03-01

Mesoporous silica nanoparticles (MSNs) should be one of the most important materials for biomedical application owing to their high specific surface area, regular porous structure, adjustable pore size and chemical inert. However, the biomedical applications of unmodified MSNs are largely impeded for their poor hydrophilicity and lack of functional groups. In this work, a novel photo-initiated atom transfer radical polymerization (ATRP) strategy has been reported for modified mesoporous silica nanoparticles (MSNs) with hydrophilicility copolymers using FeBr3 as the novel photocatalyst and itaconic acid (IA) and 2-methacryloyloxyethyl phosphorylcholine (MPC) as monomers. Because of the hydrophilicity and anticancer agent cis-dichlorodiamineplatinum(II) (CDDP) loading capacity of poly(MPC-co-IA), the controlled drug delivery applications MSNs-NH2-poly(MPC-co-IA) composites toward CDDP were further investigated. A series of characterization results demonstrated that MSNs-NH2-poly(MPC-co-IA) composites can be successfully fabricated through the novel photo-initiated ATRP. MSNs-NH2-poly(MPC-co-IA) composites showed obvious enhancement of water dispersibility, desirable biocompatibility, high drug loading capability, making them great potential for controlled drug delivery of CDDP. Moreover, as compared with the traditional ATRP, that using the transition metal ions and organic ligands as the catalysis systems in elevated temperature, our method provides a more facile, benign and cost-effective route for fabrication of multifunctional MSNs with great potential for biomedical applications. Finally, this FeBr3 based photoATRP strategy should be further extended for the fabrication of many other polymeric composites owing to its good monomer adoptability.

Long-term Ultraviolet Monitoring of a Tidal Disruption Event at only 90 Mpc

NASA Astrophysics Data System (ADS)

Maksym, W. Peter; Cenko, Bradley; Eracleous, Michael; Keel, William C.; Irwin, Jimmy; Sigurdsson, Steinn; Fruchter, Andrew; Gezari, Suvi; Bogdanovic, Tamara; Roth, Katherine

2018-01-01

At only 90 Mpc, ASASSN-14li is one of the nearest tidal disruption events (TDEs) to permit high-quality multi-wavelength monitoring, and is the first TDE with ultraviolet spectroscopic observations between Lyman alpha and Mg II λ2800Å. We present results from a continued long-term ultraviolet monitoring campaign with the Hubble Space Telescope. Prior observations had showed an array of broad emission lines common to Seyferts. Surpisingly, however, uncommon lines such as He II λ1640Å, N III] λ1750Å and N IV] λ1486Å had been enhanced, whereas others such as C III] λ1909Å and Mg II λ2800Å are notably absent. Our campaign shows contnued continuum emission accompanied by the gradual disappearance of broad line emission, which may indicate the gradual disappearance of a TDE wind as the accretion rate declines to sub-critical levels. Variability of the semi-forbidden lines supports stimulation by the TDE. A continued absence of low-ionization lines like Mg II in our monitoring may constrain the presence of ionized unbound material at large radii.

TCF7L2 gene polymorphisms do not predict susceptibility to diabetes in tropical calcific pancreatitis but may interact with SPINK1 and CTSB mutations in predicting diabetes.

PubMed

Mahurkar, Swapna; Bhaskar, Seema; Reddy, D Nageshwar; Prakash, Swami; Rao, G Venkat; Singh, Shivaram Prasad; Thomas, Varghese; Chandak, Giriraj Ratan

2008-08-16

Tropical calcific pancreatitis (TCP) is a type of chronic pancreatitis unique to developing countries in tropical regions and one of its important features is invariable progression to diabetes, a condition called fibro-calculous pancreatic diabetes (FCPD), but the nature of diabetes in TCP is controversial. We analysed the recently reported type 2 diabetes (T2D) associated polymorphisms in the TCF7L2 gene using a case-control approach, under the hypothesis that TCF7L2 variants should show similar association if diabetes in FCPD is similar to T2D. We also investigated the interaction between the TCF7L2 variants and N34S SPINK1 and L26V CTSB mutations, since they are strong predictors of risk for TCP. Two polymorphisms rs7903146 and rs12255372 in the TCF7L2 gene were analyzed by direct sequencing in 478 well-characterized TCP patients and 661 healthy controls of Dravidian and Indo-European ethnicities. Their association with TCP with diabetes (FCPD) and without diabetes was tested in both populations independently using chi-square test. Finally, a meta analysis was performed on all the cases and controls for assessing the overall significance irrespective of ethnicity. We dichotomized the whole cohort based on the presence or absence of N34S SPINK1 and L26V CTSB mutations and further subdivided them into TCP and FCPD patients and compared the distribution of TCF7L2 variants between them. The allelic and genotypic frequencies for both TCF7L2 polymorphisms, did not differ significantly between TCP patients and controls belonging to either of the ethnic groups or taken together. No statistically significant association of the SNPs was observed with TCP or FCPD or between carriers and non-carriers of N34S SPINK1 and L26V CTSB mutations. The minor allele frequency for rs7903146 was different between TCP and FCPD patients carrying the N34S SPINK1 variant but did not reach statistical significance (OR = 1.59, 95% CI = 0.93-2.70, P = 0.09), while, TCF7L2variant showed a

An improved predictive functional control method with application to PMSM systems

NASA Astrophysics Data System (ADS)

Li, Shihua; Liu, Huixian; Fu, Wenshu

2017-01-01

In common design of prediction model-based control method, usually disturbances are not considered in the prediction model as well as the control design. For the control systems with large amplitude or strong disturbances, it is difficult to precisely predict the future outputs according to the conventional prediction model, and thus the desired optimal closed-loop performance will be degraded to some extent. To this end, an improved predictive functional control (PFC) method is developed in this paper by embedding disturbance information into the system model. Here, a composite prediction model is thus obtained by embedding the estimated value of disturbances, where disturbance observer (DOB) is employed to estimate the lumped disturbances. So the influence of disturbances on system is taken into account in optimisation procedure. Finally, considering the speed control problem for permanent magnet synchronous motor (PMSM) servo system, a control scheme based on the improved PFC method is designed to ensure an optimal closed-loop performance even in the presence of disturbances. Simulation and experimental results based on a hardware platform are provided to confirm the effectiveness of the proposed algorithm.

l-Arginine Pathway Metabolites Predict Need for Intra-operative Shunt During Carotid Endarterectomy.

PubMed

Szabo, P; Lantos, J; Nagy, L; Keki, S; Volgyi, E; Menyhei, G; Illes, Z; Molnar, T

2016-12-01

Asymmetric dimethylarginine (ADMA) inhibits nitric oxide (NO) synthesis and is a marker of atherosclerosis. This study examined the correlation between pre-operative l-arginine and ADMA concentration during carotid endarterectomy (CEA), and jugular lactate indicating anaerobic cerebral metabolism, jugular S100B reflecting blood-brain barrier integrity, and with factors of surgical intervention. The concentration of l-arginine, ADMA, and symmetric dimethylarginine was measured in blood taken under regional anaesthesia from the radial artery of 55 patients prior to CEA. Blood gas parameters, concentration of lactate, and S100B were also serially measured in blood taken from both the radial artery and the jugular bulb before and after carotid clamping, and after release of the clamp. To estimate anaerobic metabolism, the jugulo-arterial ratio of CO 2 gap/oxygen extraction was calculated. Positive correlation was found between pre-operative ADMA levels and the ratio of jugulo-arterial CO 2 gap/oxygen extraction during clamp and reperfusion (p = .005 and p = .01, respectively). An inverse correlation was found between the pre-operative l-arginine concentration and jugular lactate at each time point (both p = .002). The critical pre-operative level of l-arginine was determined by receiver operator curve analysis. If l-arginine was below the cutoff value of 35 μmol/L, jugular S100B concentration was higher 24 h post-operatively (p = .03), and jugular lactate levels were increased during reperfusion (p = .02). The median pre-operative concentration of l-arginine was lower in patients requiring an intra-operative shunt than in patients without need of shunt (median: 30.3 μmol/L [interquartile range 24.4-34.4 μmol/L] vs. 57.6 μmol/L [interquartile range 42.3-74.5 μmol/L]; p = .002). High pre-operative ADMA concentration predicts poor cerebral perfusion indicated by elevated jugulo-arterial CO 2 gap/oxygen extraction. Low pre-operative l

People, planning, predictions pull DP&L to pinnacle

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beaty, W.; Warkentin, D.

Dayton Power and Light was chosen as the 26th utility to receive Electric Light and Power`s annual Utility of the Year award for investor-owned electric utilities. The award not only recognizes management for having guided the company to a high level of achievement, but to each employee for their contribution to the company`s success. Using its formula of three Ps to success - people, planning, and predict and prevent - this West Central Ohio utility plans on using its current plain vanilla approach to business to carve out its own pattern for the years ahead. DP&L`s employees have gone abovemore » and beyond the call of duty to serve its customers and shareholders. The utility`s operations are epitomized by the excellent fuel efficiency of its generating plants. DP&L has been in Electric Light & Power`s top 10 heat rate rankings for nine out of the past 10 years. Investor earnings per share increased from $1.15 in 1991 to $1.34 in 1992, with earnings per share rising by 6% to $1.42 in 1993.« less

Microparticle content of platelet concentrates is predicted by donor microparticles and is altered by production methods and stress.

PubMed

Maurer-Spurej, Elisabeth; Larsen, Rune; Labrie, Audrey; Heaton, Andrew; Chipperfield, Kate

2016-08-01

In circulation, shedding of microparticles from a variety of viable cells can be triggered by pathological activation of inflammatory processes, by activation of coagulation or complement systems, or by physical stress. Elevated microparticle content (MPC) in donor blood might therefore indicate a clinical condition of the donor which, upon transfusion, might affect the recipient. In blood products, elevated MPC might also represent product stress. Surprisingly, the MPC in blood collected from normal blood donors is highly variable, which raises the question whether donor microparticles are present in-vivo and transfer into the final blood component, and how production methods and post-production processing might affect the MPC. We measured MPC using ThromboLUX in (a) platelet-rich plasma (PRP) of 54 apheresis donors and the corresponding apheresis products, (b) 651 apheresis and 646 pooled platelet concentrates (PCs) with plasma and 414 apheresis PCs in platelet additive solution (PAS), and (c) apheresis PCs before and after transportation, gamma irradiation, and pathogen inactivation (N = 8, 7, and 12 respectively). ThromboLUX-measured MPC in donor PRP and their corresponding apheresis PC samples were highly correlated (r = 0.82, P = .001). The average MPC in pooled PC was slightly lower than that in apheresis PC and substantially lower in apheresis PC stored with PAS rather than plasma. Mirasol Pathogen Reduction treatment significantly increased MPC with age. Thus, MPC measured in donor samples might be a useful predictor of product stability, especially if post-production processes are necessary. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Limb Dominance Results from Asymmetries in Predictive and Impedance Control Mechanisms

PubMed Central

Yadav, Vivek; Sainburg, Robert L.

2014-01-01

Handedness is a pronounced feature of human motor behavior, yet the underlying neural mechanisms remain unclear. We hypothesize that motor lateralization results from asymmetries in predictive control of task dynamics and in control of limb impedance. To test this hypothesis, we present an experiment with two different force field environments, a field with a predictable magnitude that varies with the square of velocity, and a field with a less predictable magnitude that varies linearly with velocity. These fields were designed to be compatible with controllers that are specialized in predicting limb and task dynamics, and modulating position and velocity dependent impedance, respectively. Because the velocity square field does not change the form of the equations of motion for the reaching arm, we reasoned that a forward dynamic-type controller should perform well in this field, while control of linear damping and stiffness terms should be less effective. In contrast, the unpredictable linear field should be most compatible with impedance control, but incompatible with predictive dynamics control. We measured steady state final position accuracy and 3 trajectory features during exposure to these fields: Mean squared jerk, Straightness, and Movement time. Our results confirmed that each arm made straighter, smoother, and quicker movements in its compatible field. Both arms showed similar final position accuracies, which were achieved using more extensive corrective sub-movements when either arm performed in its incompatible field. Finally, each arm showed limited adaptation to its incompatible field. Analysis of the dependence of trajectory errors on field magnitude suggested that dominant arm adaptation occurred by prediction of the mean field, thus exploiting predictive mechanisms for adaptation to the unpredictable field. Overall, our results support the hypothesis that motor lateralization reflects asymmetries in specific motor control mechanisms associated

Lecithin has no effect on serum lipoprotein, plasma fibrinogen and macro molecular protein complex levels in hyperlipidaemic men in a double-blind controlled study.

PubMed

Oosthuizen, W; Vorster, H H; Vermaak, W J; Smuts, C M; Jerling, J C; Veldman, F J; Burger, H M

1998-06-01

To examine the effects of lecithin on serum lipoprotein, plasma fibrinogen and macro molecular protein complex (MPC) levels. Twenty free living hyperlipidaemic men participated in this double-blind study which controlled for possible indirect effects. The subjects were randomly assigned to one of three treatments: frozen yoghurt or frozen yoghurt with 20 g soya bean lecithin or frozen yoghurt with 17 g sunflower oil. Sunflower oil was used to control for the increased energy and linoleic acid intake from lecithin. Yoghurt served as the 'vehicle' for the lecithin and sunflower oil and yoghurt alone was given to one group to control for possible effects due to the yoghurt 'vehicle', as well as other environmental influences. Variables were measured with standard methods twice at baseline and after 2 and 4 weeks of treatment. Plasma linoleic acid levels increased significantly with lecithin and sunflower oil treatments indicating that compliance to the treatments were obtained. Lecithin treatment did not have significant effects on serum total cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A, apolipoprotein B or lipoprotein (a) levels. Plasma fibrinogen and MPC levels were also not affected by lecithin therapy. Sunflower oil treatment resulted in significant increased body weight, serum TC and decreased MPC levels. Lecithin treatment had no independent effects on serum lipoprotein, plasma fibrinogen or MPC levels in hyperlipidaemic men.

Control surface hinge moment prediction using computational fluid dynamics

NASA Astrophysics Data System (ADS)

Simpson, Christopher David

The following research determines the feasibility of predicting control surface hinge moments using various computational methods. A detailed analysis is conducted using a 2D GA(W)-1 airfoil with a 20% plain flap. Simple hinge moment prediction methods are tested, including empirical Datcom relations and XFOIL. Steady-state and time-accurate turbulent, viscous, Navier-Stokes solutions are computed using Fun3D. Hinge moment coefficients are computed. Mesh construction techniques are discussed. An adjoint-based mesh adaptation case is also evaluated. An NACA 0012 45-degree swept horizontal stabilizer with a 25% elevator is also evaluated using Fun3D. Results are compared with experimental wind-tunnel data obtained from references. Finally, the costs of various solution methods are estimated. Results indicate that while a steady-state Navier-Stokes solution can accurately predict control surface hinge moments for small angles of attack and deflection angles, a time-accurate solution is necessary to accurately predict hinge moments in the presence of flow separation. The ability to capture the unsteady vortex shedding behavior present in moderate to large control surface deflections is found to be critical to hinge moment prediction accuracy. Adjoint-based mesh adaptation is shown to give hinge moment predictions similar to a globally-refined mesh for a steady-state 2D simulation.

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

DOE PAGES

Li, Mingjie; Zhou, Ping; Wang, Hong; ...

2017-09-19

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refiningmore » system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.« less

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Mingjie; Zhou, Ping; Wang, Hong

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refiningmore » system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.« less

4. CONTROL BUILDING B, VIEW TOWARDS SOUTHEAST. Glenn L. ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

4. CONTROL BUILDING B, VIEW TOWARDS SOUTHEAST. - Glenn L. Martin Company, Titan Missile Test Facilities, Control Building B, Waterton Canyon Road & Colorado Highway 121, Lakewood, Jefferson County, CO

Predicting the potential distribution of main malaria vectors Anopheles stephensi, An. culicifacies s.l. and An. fluviatilis s.l. in Iran based on maximum entropy model.

PubMed

Pakdad, Kamran; Hanafi-Bojd, Ahmad Ali; Vatandoost, Hassan; Sedaghat, Mohammad Mehdi; Raeisi, Ahmad; Moghaddam, Abdolreza Salahi; Foroushani, Abbas Rahimi

2017-05-01

Malaria is considered as a major public health problem in southern areas of Iran. The goal of this study was to predict best ecological niches of three main malaria vectors of Iran: Anopheles stephensi, Anopheles culicifacies s.l. and Anopheles fluviatilis s.l. A databank was created which included all published data about Anopheles species of Iran from 1961 to 2015. The suitable environmental niches for the three above mentioned Anopheles species were predicted using maximum entropy model (MaxEnt). AUC (area under Roc curve) values were 0.943, 0.974 and 0.956 for An. stephensi, An. culicifacies s.l. and An. fluviatilis s.l respectively, which are considered as high potential power of model in the prediction of species niches. The biggest bioclimatic contributor for An. stephensi and An. fluviatilis s.l. was bio 15 (precipitation seasonality), 25.5% and 36.1% respectively, followed by bio 1 (annual mean temperature), 20.8% for An. stephensi and bio 4 (temperature seasonality) with 49.4% contribution for An. culicifacies s.l. This is the first step in the mapping of the country's malaria vectors. Hence, future weather situation can change the dispersal maps of Anopheles. Iran is under elimination phase of malaria, so that such spatio-temporal studies are essential and could provide guideline for decision makers for IVM strategies in problematic areas. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

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.

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.

Fourier transform wavefront control with adaptive prediction of the atmosphere.

PubMed

Poyneer, Lisa A; Macintosh, Bruce A; Véran, Jean-Pierre

2007-09-01

Predictive Fourier control is a temporal power spectral density-based adaptive method for adaptive optics that predicts the atmosphere under the assumption of frozen flow. The predictive controller is based on Kalman filtering and a Fourier decomposition of atmospheric turbulence using the Fourier transform reconstructor. It provides a stable way to compensate for arbitrary numbers of atmospheric layers. For each Fourier mode, efficient and accurate algorithms estimate the necessary atmospheric parameters from closed-loop telemetry and determine the predictive filter, adjusting as conditions change. This prediction improves atmospheric rejection, leading to significant improvements in system performance. For a 48x48 actuator system operating at 2 kHz, five-layer prediction for all modes is achievable in under 2x10(9) floating-point operations/s.

Predictive Multiple Model Switching Control with the Self-Organizing Map

NASA Technical Reports Server (NTRS)

Motter, Mark A.

2000-01-01

A predictive, multiple model control strategy is developed by extension of self-organizing map (SOM) local dynamic modeling of nonlinear autonomous systems to a control framework. Multiple SOMs collectively model the global response of a nonautonomous system to a finite set of representative prototype controls. Each SOM provides a codebook representation of the dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the global minimization of a similarity metric. The SOM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme that selects the best available model for the applied control. SOM based linear models are used to predict the response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal.

Sparse generalized linear model with L0 approximation for feature selection and prediction with big omics data.

PubMed

Liu, Zhenqiu; Sun, Fengzhu; McGovern, Dermot P

2017-01-01

Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L 1 , SCAD and MC+. However, none of the existing algorithms optimizes L 0 , which penalizes the number of nonzero features directly. In this paper, we develop a novel sparse generalized linear model (GLM) with L 0 approximation for feature selection and prediction with big omics data. The proposed approach approximate the L 0 optimization directly. Even though the original L 0 problem is non-convex, the problem is approximated by sequential convex optimizations with the proposed algorithm. The proposed method is easy to implement with only several lines of code. Novel adaptive ridge algorithms ( L 0 ADRIDGE) for L 0 penalized GLM with ultra high dimensional big data are developed. The proposed approach outperforms the other cutting edge regularization methods including SCAD and MC+ in simulations. When it is applied to integrated analysis of mRNA, microRNA, and methylation data from TCGA ovarian cancer, multilevel gene signatures associated with suboptimal debulking are identified simultaneously. The biological significance and potential clinical importance of those genes are further explored. The developed Software L 0 ADRIDGE in MATLAB is available at https://github.com/liuzqx/L0adridge.

Theoretical prediction and experimental verification on enantioselectivity of haloacid dehalogenase L-DEX YL with chloropropionate

NASA Astrophysics Data System (ADS)

Kondo, Hirotaka; Fujimoto, Kazuhiro J.; Tanaka, Shigenori; Deki, Hiroyuki; Nakamura, Takashi

2015-03-01

L-2-Haloacid dehalogenase (L-DEX YL) is a member of a family of enzymes that decontaminate a variety of environmental pollutants such as L-2-chloropropionate (L-2-CPA). This enzyme specifically catalyzes the hydrolytic dehalogenation of L-2-haloacid to produce D-2-hydroxy acid, and does not catalyze that of D-2-haloacid. Here, using the quantum-mechanical/molecular-mechanical and the fragment molecular orbital calculations, the enzymatic reaction of L-DEX YL to D-2-CPA was compared with that to L-2-CPA. As a result, Tyr12, Leu45 and Phe60 were predicted to affect the enantioselectivity. We then performed the site-directed-mutagenesis experiments and the activity measurement of these mutants, thus finding that the F60Y mutant had the enzymatic activity with D-2-CPA.

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.

L(sub 1) Adaptive Flight Control System: Flight Evaluation and Technology Transition

NASA Technical Reports Server (NTRS)

Xargay, Enric; Hovakimyan, Naira; Dobrokhodov, Vladimir; Kaminer, Isaac; Gregory, Irene M.; Cao, Chengyu

2010-01-01

Certification of adaptive control technologies for both manned and unmanned aircraft represent a major challenge for current Verification and Validation techniques. A (missing) key step towards flight certification of adaptive flight control systems is the definition and development of analysis tools and methods to support Verification and Validation for nonlinear systems, similar to the procedures currently used for linear systems. In this paper, we describe and demonstrate the advantages of L(sub l) adaptive control architectures for closing some of the gaps in certification of adaptive flight control systems, which may facilitate the transition of adaptive control into military and commercial aerospace applications. As illustrative examples, we present the results of a piloted simulation evaluation on the NASA AirSTAR flight test vehicle, and results of an extensive flight test program conducted by the Naval Postgraduate School to demonstrate the advantages of L(sub l) adaptive control as a verifiable robust adaptive flight control system.

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

Predicting Failure of Glyburide Therapy in Gestational Diabetes

PubMed Central

Harper, Lorie M.; Glover, Angelica V.; Biggio, Joseph R.; Tita, Alan

2016-01-01

Objective We sought to develop a prediction model to identify women with gestational diabetes (GDM) who require insulin to achieve glycemic control. Study Design Retrospective cohort of all singletons with GDM treated with glyburide 2007–2013. Glyburide failure was defined as reaching glyburide 20 mg/day and receiving insulin. Glyburide success was defined as any glyburide dose without insulin and >70% of visits with glycemic control. Multivariable logistic regression analysis was performed to create a prediction model. Results Of 360 women, 63 (17.5%) qualified as glyburide failure and 157 (43.6%) glyburide success. The final prediction model for glyburide failure included prior GDM, GDM diagnosis ≤26 weeks, 1-hour GCT ≥228 mg/dL, 3-hour GTT 1-hour value ≥221 mg/dL, ≥7 post-prandial blood sugars >120 mg/dL in the week glyburide started, and ≥1 blood sugar >200 mg/dL. The model accurately classified 81% of subjects. Conclusions Women with GDM who will require insulin can be identified at initiation of pharmacologic therapy. PMID:26796130

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.

L(sub 1) Adaptive Control Design for NASA AirSTAR Flight Test Vehicle

NASA Technical Reports Server (NTRS)

Gregory, Irene M.; Cao, Chengyu; Hovakimyan, Naira; Zou, Xiaotian

2009-01-01

In this paper we present a new L(sub 1) adaptive control architecture that directly compensates for matched as well as unmatched system uncertainty. To evaluate the L(sub 1) adaptive controller, we take advantage of the flexible research environment with rapid prototyping and testing of control laws in the Airborne Subscale Transport Aircraft Research system at the NASA Langley Research Center. We apply the L(sub 1) adaptive control laws to the subscale turbine powered Generic Transport Model. The presented results are from a full nonlinear simulation of the Generic Transport Model and some preliminary pilot evaluations of the L(sub 1) adaptive control law.

Quality by control: Towards model predictive control of mammalian cell culture bioprocesses.

PubMed

Sommeregger, Wolfgang; Sissolak, Bernhard; Kandra, Kulwant; von Stosch, Moritz; Mayer, Martin; Striedner, Gerald

2017-07-01

The industrial production of complex biopharmaceuticals using recombinant mammalian cell lines is still mainly built on a quality by testing approach, which is represented by fixed process conditions and extensive testing of the end-product. In 2004 the FDA launched the process analytical technology initiative, aiming to guide the industry towards advanced process monitoring and better understanding of how critical process parameters affect the critical quality attributes. Implementation of process analytical technology into the bio-production process enables moving from the quality by testing to a more flexible quality by design approach. The application of advanced sensor systems in combination with mathematical modelling techniques offers enhanced process understanding, allows on-line prediction of critical quality attributes and subsequently real-time product quality control. In this review opportunities and unsolved issues on the road to a successful quality by design and dynamic control implementation are discussed. A major focus is directed on the preconditions for the application of model predictive control for mammalian cell culture bioprocesses. Design of experiments providing information about the process dynamics upon parameter change, dynamic process models, on-line process state predictions and powerful software environments seem to be a prerequisite for quality by control realization. © 2017 The Authors. Biotechnology Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optical absorption and electrical properties of MPc (M =Fe, Cu, Zn)-TCNQ interfaces for optoelectronic applications

NASA Astrophysics Data System (ADS)

Sánchez Vergara, M. E.; Medrano Gallardo, D.; Vera Estrada, I. L.; Jiménez Sandoval, O.

2018-04-01

This research is related to the growth and characterization of doped molecular semiconductor metallophthalocyanine-tetracyanoquinodimethane (MPc-TCNQ) films, with M = Fe, Zn, Cu. FT-IR and Raman spectroscopies were employed to study the chemical interactions taking place in the MPc-TCNQ films. XRD was carried out to determine the crystalline structure present in the samples, due to the facility of the MPcs to be in alpha and/or beta phases. The thin films were analized by SEM and UV-vis spectroscopy in order to study their morphological and optical properties. The absorption spectra recorded in the UV-Vis region for the deposited samples showed two bands, namely the Q and Soret bands. The absorption coefficient (α) and photon energy (hν) were calculated from the UV-vis spectra, to in turn determine the optic activation energy in each film and its semiconductor behavior. The values obtained for direct transitions due to the crystallinity of the films were: 1.2, 1.4 and 2 eV for FePc-TCNQ (MMFe), ZnPc-TCNQ (MMZn) and CuPc-TCNQ (MMCu), respectively. Additionally, I-V characteristics have been obtained from fabricated glass/ITO/MM/Ag devices using ohmic contacts both after annealing. The electrical properties of the devices, e.g. carrier mobility and concentration of thermally generated holes, were extracted from the J-V characteristics. The results show that the conduction process is ohmic for the MMZn and MMCu devices, at low voltages, while at high voltages, a space-charge-limited conduction (SCLC) is present. The effect of temperature on conductivity was also measured in these samples and the lower thermal activation energy calculated was 0.37 eV for MMZn. Moreover, it was found that the temperature-dependent electric current is always higher for the MMZn device and suggests a semiconductor-like behavior with an important conductivity of the order of 103 S cm-1. Anyhow, in terms not only of electric properties, but also of optic behavior, the results suggest that

E-wave generated intraventricular diastolic vortex to L-wave relation: model-based prediction with in vivo validation.

PubMed

Ghosh, Erina; Caruthers, Shelton D; Kovács, Sándor J

2014-08-01

The Doppler echocardiographic E-wave is generated when the left ventricle's suction pump attribute initiates transmitral flow. In some subjects E-waves are accompanied by L-waves, the occurrence of which has been correlated with diastolic dysfunction. The mechanisms for L-wave generation have not been fully elucidated. We propose that the recirculating diastolic intraventricular vortex ring generates L-waves and based on this mechanism, we predict the presence of L-waves in the right ventricle (RV). We imaged intraventricular flow using Doppler echocardiography and phase-contrast magnetic resonance imaging (PC-MRI) in 10 healthy volunteers. L-waves were recorded in all subjects, with highest velocities measured typically 2 cm below the annulus. Fifty-five percent of cardiac cycles (189 of 345) had L-waves. Color M-mode images eliminated mid-diastolic transmitral flow as the cause of the observed L-waves. Three-dimensional intraventricular flow patterns were imaged via PC-MRI and independently validated our hypothesis. Additionally as predicted, L-waves were observed in the RV, by both echocardiography and PC-MRI. The re-entry of the E-wave-generated vortex ring flow through a suitably located echo sample volume can be imaged as the L-wave. These waves are a general feature and a direct consequence of LV and RV diastolic fluid mechanics. Copyright © 2014 the American Physiological Society.

Predicting worsening asthma control following the common cold

PubMed Central

Walter, Michael J.; Castro, Mario; Kunselman, Susan J.; Chinchilli, Vernon M; Reno, Melissa; Ramkumar, Thiruvamoor P.; Avila, Pedro C.; Boushey, Homer A.; Ameredes, Bill T.; Bleecker, Eugene R.; Calhoun, William J.; Cherniack, Reuben M.; Craig, Timothy J.; Denlinger, Loren C.; Israel, Elliot; Fahy, John V.; Jarjour, Nizar N.; Kraft, Monica; Lazarus, Stephen C.; Lemanske, Robert F.; Martin, Richard J.; Peters, Stephen P.; Ramsdell, Joe W.; Sorkness, Christine A.; Rand Sutherland, E.; Szefler, Stanley J.; Wasserman, Stephen I.; Wechsler, Michael E.

2008-01-01

The asthmatic response to the common cold is highly variable and early characteristics that predict worsening of asthma control following a cold have not been identified. In this prospective multi-center cohort study of 413 adult subjects with asthma, we used the mini-Asthma Control Questionnaire (mini-ACQ) to quantify changes in asthma control and the Wisconsin Upper Respiratory Symptom Survey-21 (WURSS-21) to measure cold severity. Univariate and multivariable models examined demographic, physiologic, serologic, and cold-related characteristics for their relationship to changes in asthma control following a cold. We observed a clinically significant worsening of asthma control following a cold (increase in mini-ACQ of 0.69 ± 0.93). Univariate analysis demonstrated season, center location, cold length, and cold severity measurements all associated with a change in asthma control. Multivariable analysis of the covariates available within the first 2 days of cold onset revealed the day 2 and the cumulative sum of the day 1 and 2 WURSS-21 scores were significant predictors for the subsequent changes in asthma control. In asthmatic subjects the cold severity measured within the first 2 days can be used to predict subsequent changes in asthma control. This information may help clinicians prevent deterioration in asthma control following a cold. PMID:18768579

[Mutant prevention concentrations of antibacterial agents to ocular pathogenic bacteria].

PubMed

Liang, Qing-Feng; Wang, Zhi-Qun; Li, Ran; Luo, Shi-Yun; Deng, Shi-Jing; Sun, Xu-Guang

2009-01-01

To establish a method to measure mutant prevention concentration (MPC) in vitro, and to measure MPC of antibacterial agents for ocular bacteria caused keratitis. It was an experimental study. Forty strains of ocular bacteria were separated from cornea in Beijing Institute of Ophthalmology, which included 8 strains of Staphylococcus aureus, Staphylococcus epidermidis, Streptococcus pneumoniae, Pseudomonas aeruginosa and Klebsiella pneumoniae respectively. The minimal inhibitory concentration (MIC) of the levofloxacin (LVF), ofloxacin (OFL), ciprofloxacin (CIP), norfloxacin (NFL), tobramycin (TOB) and chloromycetin (CHL) were determined by agar dilution method from National Committee of Clinical Laboratory Standard (NCCLS). The MPC were measured by accumulate-bacterial methods with bacterial population inoculated more than 1.2 x 10(10) colony forming units per milliliter with Mueller-Hinton broth and tryptic soy agar plate. With the software of SPSS 11.0, the datum such as the range of MIC, MPC, MIC90 and MPC90 were calculated, and the selection index (MPC90/ MI90) and mutant selection window (MSW) were obtained. The MI90 of LVF and TOB (4 mg/L) to Staphylococcus aureus strains were the lowest. CIP showed the lowest MIC90 (0.25 mg/L) to Pseudomonas aeruginosa among six kinds of antibacterial agents. The MIC90 of LVF to Staphylococcus epidermidis (256 mg/L), Streptococcus pneumoniae (1 mg/L) and Klebsiella pneumoniae (0.25 mg/L) were lower than other antibacterial agents. The MPC90, MSW and the MPC90/MIC90 of levofloxacin showed lower values compared with other antibacterial medicines. From all the datum, the MIC90 of CHL was the highest and the activity was the weakest. Although the activity of LVF was higher to every kind of bacteria, CIP had the highest activity antibacterial to Pseudomonas aeruginosa. The capacity of CHL and TOB was weaker than Quinolones for restricting resistant mutants on ocular bacteria. LVF had the strongest capacity for restricting resistant

Distributed MPC based consensus for single-integrator multi-agent systems.

PubMed

Cheng, Zhaomeng; Fan, Ming-Can; Zhang, Hai-Tao

2015-09-01

This paper addresses model predictive control schemes for consensus in multi-agent systems (MASs) with discrete-time single-integrator dynamics under switching directed interaction graphs. The control horizon is extended to be greater than one which endows the closed-loop system with extra degree of freedom. We derive sufficient conditions on the sampling period and the interaction graph to achieve consensus by using the property of infinite products of stochastic matrices. Consensus can be achieved asymptotically if the sampling period is selected such that the interaction graph among agents has a directed spanning tree jointly. Significantly, if the interaction graph always has a spanning tree, one can select an arbitrary large sampling period to guarantee consensus. Finally, several simulations are conducted to illustrate the effectiveness of the theoretical results. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

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

Energy efficient model based algorithm for control of building HVAC systems.

PubMed

Kirubakaran, V; Sahu, Chinmay; Radhakrishnan, T K; Sivakumaran, N

2015-11-01

Energy efficient designs are receiving increasing attention in various fields of engineering. Heating ventilation and air conditioning (HVAC) control system designs involve improved energy usage with an acceptable relaxation in thermal comfort. In this paper, real time data from a building HVAC system provided by BuildingLAB is considered. A resistor-capacitor (RC) framework for representing thermal dynamics of the building is estimated using particle swarm optimization (PSO) algorithm. With objective costs as thermal comfort (deviation of room temperature from required temperature) and energy measure (Ecm) explicit MPC design for this building model is executed based on its state space representation of the supply water temperature (input)/room temperature (output) dynamics. The controllers are subjected to servo tracking and external disturbance (ambient temperature) is provided from the real time data during closed loop control. The control strategies are ported on a PIC32mx series microcontroller platform. The building model is implemented in MATLAB and hardware in loop (HIL) testing of the strategies is executed over a USB port. Results indicate that compared to traditional proportional integral (PI) controllers, the explicit MPC's improve both energy efficiency and thermal comfort significantly. Copyright © 2015 Elsevier Inc. All rights reserved.

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

The anti-tumour agent lonidamine is a potent inhibitor of the mitochondrial pyruvate carrier and plasma membrane monocarboxylate transporters.

PubMed

Nancolas, Bethany; Guo, Lili; Zhou, Rong; Nath, Kavindra; Nelson, David S; Leeper, Dennis B; Blair, Ian A; Glickson, Jerry D; Halestrap, Andrew P

2016-04-01

Lonidamine (LND) is an anti-tumour drug particularly effective at selectively sensitizing tumours to chemotherapy, hyperthermia and radiotherapy, although its precise mode of action remains unclear. It has been reported to perturb the bioenergetics of cells by inhibiting glycolysis and mitochondrial respiration, whereas indirect evidence suggests it may also inhibit L-lactic acid efflux from cells mediated by members of the proton-linked monocarboxylate transporter (MCT) family and also pyruvate uptake into the mitochondria by the mitochondrial pyruvate carrier (MPC). In the present study, we test these possibilities directly. We demonstrate that LND potently inhibits MPC activity in isolated rat liver mitochondria (Ki2.5 μM) and co-operatively inhibits L-lactate transport by MCT1, MCT2 and MCT4 expressed in Xenopus laevisoocytes with K0.5 and Hill coefficient values of 36-40 μM and 1.65-1.85 respectively. In rat heart mitochondria LND inhibited the MPC with similar potency and uncoupled oxidation of pyruvate was inhibited more effectively (IC50~ 7 μM) than other substrates including glutamate (IC50~ 20 μM). In isolated DB-1 melanoma cells 1-10 μM LND increased L-lactate output, consistent with MPC inhibition, but higher concentrations (150 μM) decreased L-lactate output whereas increasing intracellular [L-lactate] > 5-fold, consistent with MCT inhibition. We conclude that MPC inhibition is the most sensitive anti-tumour target for LND, with additional inhibitory effects on MCT-mediated L-lactic acid efflux and glutamine/glutamate oxidation. Together these actions can account for published data on the selective tumour effects of LND onL-lactate, intracellular pH (pHi) and ATP levels that can be partially mimicked by the established MPC and MCT inhibitor α-cyano-4-hydroxycinnamate (CHC). © 2016 Authors; published by Portland Press Limited.

The anti-tumour agent lonidamine is a potent inhibitor of the mitochondrial pyruvate carrier and plasma membrane monocarboxylate transporters

PubMed Central

Nancolas, Bethany; Guo, Lili; Zhou, Rong; Nath, Kavindra; Nelson, David S.; Leeper, Dennis B.; Blair, Ian A.; Glickson, Jerry D.; Halestrap, Andrew P.

2016-01-01

Lonidamine (LND) is an anti-tumour drug particularly effective at selectively sensitising tumours to chemotherapy, hyperthermia and radiotherapy, although its precise mode of action remains unclear. It has been reported to perturb the bioenergetics of cells by inhibiting glycolysis and mitochondrial respiration, while indirect evidence suggests it may also inhibit L-lactic acid efflux from cells mediated by members of the proton-linked monocarboxylate transporter (MCT) family and also pyruvate uptake into the mitochondria by the mitochondrial pyruvate carrier (MPC). Here we test these possibilities directly. We demonstrate that LND potently inhibits MPC activity in isolated rat liver mitochondria (Ki 2.5 μM) and cooperatively inhibits L-lactate transport by MCT1, MCT2 and MCT4 expressed in Xenopus laevis oocytes with K0.5 and Hill Coefficient values of 36–40 μM and 1.65–1.85. In rat heart mitochondria LND inhibited the MPC with similar potency and uncoupled oxidation of pyruvate was inhibited more effectively (IC50 ~7 μM) than other substrates including glutamate (IC50 ~20 μM). In isolated DB-1 melanoma cells 1–10 μM LND increased L-lactate output, consistent with MPC inhibition, but higher concentrations (150 μM) decreased L-lactate output while increasing intracellular [L-lactate] > five-fold, consistent with MCT inhibition. We conclude that MPC inhibition is the most sensitive anti-tumour target for LND, with additional inhibitory effects on MCT-mediated L-lactic acid efflux and glutamine/glutamate oxidation. Together these actions can account for published data on the selective tumour effects of LND on L-lactate, intracellular pH (pHi) and ATP levels that can be partially mimicked by the established MPC and MCT inhibitor α-cyano-4-hydroxycinnamate. PMID:26831515

Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors

NASA Technical Reports Server (NTRS)

Mercer, Joey; Hunt-Espinosa, Sarah; Bienert, Nancy; Laraway, Sean

2016-01-01

A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?

Tailored high-resolution numerical weather forecasts for energy efficient predictive building control

NASA Astrophysics Data System (ADS)

Stauch, V. J.; Gwerder, M.; Gyalistras, D.; Oldewurtel, F.; Schubiger, F.; Steiner, P.

2010-09-01

The high proportion of the total primary energy consumption by buildings has increased the public interest in the optimisation of buildings' operation and is also driving the development of novel control approaches for the indoor climate. In this context, the use of weather forecasts presents an interesting and - thanks to advances in information and predictive control technologies and the continuous improvement of numerical weather prediction (NWP) models - an increasingly attractive option for improved building control. Within the research project OptiControl (www.opticontrol.ethz.ch) predictive control strategies for a wide range of buildings, heating, ventilation and air conditioning (HVAC) systems, and representative locations in Europe are being investigated with the aid of newly developed modelling and simulation tools. Grid point predictions for radiation, temperature and humidity of the high-resolution limited area NWP model COSMO-7 (see www.cosmo-model.org) and local measurements are used as disturbances and inputs into the building system. The control task considered consists in minimizing energy consumption whilst maintaining occupant comfort. In this presentation, we use the simulation-based OptiControl methodology to investigate the impact of COSMO-7 forecasts on the performance of predictive building control and the resulting energy savings. For this, we have selected building cases that were shown to benefit from a prediction horizon of up to 3 days and therefore, are particularly suitable for the use of numerical weather forecasts. We show that the controller performance is sensitive to the quality of the weather predictions, most importantly of the incident radiation on differently oriented façades. However, radiation is characterised by a high temporal and spatial variability in part caused by small scale and fast changing cloud formation and dissolution processes being only partially represented in the COSMO-7 grid point predictions. On the

A combined-slip predictive control of vehicle stability with experimental verification

NASA Astrophysics Data System (ADS)

Jalali, Milad; Hashemi, Ehsan; Khajepour, Amir; Chen, Shih-ken; Litkouhi, Bakhtiar

2018-02-01

In this paper, a model predictive vehicle stability controller is designed based on a combined-slip LuGre tyre model. Variations in the lateral tyre forces due to changes in tyre slip ratios are considered in the prediction model of the controller. It is observed that the proposed combined-slip controller takes advantage of the more accurate tyre model and can adjust tyre slip ratios based on lateral forces of the front axle. This results in an interesting closed-loop response that challenges the notion of braking only the wheels on one side of the vehicle in differential braking. The performance of the proposed controller is evaluated in software simulations and is compared to a similar pure-slip controller. Furthermore, experimental tests are conducted on a rear-wheel drive electric Chevrolet Equinox equipped with differential brakes to evaluate the closed-loop response of the model predictive control controller.

Characterization of dermatitis after PD-1/PD-L1 inhibitor therapy and association with multiple oncologic outcomes: a retrospective case-control study.

PubMed

Min Lee, Charles Kyung; Li, Shufeng; Tran, Duy Cong; Zhu, Gefei Alex; Kim, Jinah; Kwong, Bernice Y; Chang, Anne Lynn S

2018-05-29

Cutaneous adverse events are common with Programmed Death (PD)-1/ PD-Ligand (L)1 inhibitors. However, the nature of the specific cutaneous adverse event of dermatitis has not been investigated across various PD-1/PD-L1 inhibitors. Oncologic outcomes potentially associated with dermatitis are not well characterized. (s): To assess the nature of dermatitis after PD-1/PD-L1 inhibitor exposure and oncologic outcomes associated with dermatitis. Retrospective, matched, case-control study conducted at a single academic center. The most common histologic patterns were lichenoid dermatitis (50%) and spongiotic dermatitis (40%). Overall tumor response rate was 65.0% for cases and 17.0% for controls (p=0.0007), odds ratio: 7.3 (95% CI 2.3-23.1). Progression Free Survival (PFS) and Overall Survival (OS) times were significantly longer for cases than controls by Kaplan-Meier analysis (p<0.0001 and 0.0203, respectively). Retrospective design and relatively small sample size precluded matching on all cancer types. Lichenoid and spongiotic dermatitis associated with PD-1/PD-L1 inhibitors could be a sign of robust immune response and improved oncologic outcomes. The predictive value of PD-1/PD-L1 related dermatitis on cancer outcomes awaits investigation through prospective multicenter studies for specific cancer types. Copyright © 2018. Published by Elsevier Inc.

Comparison of Model Prediction with Measurements of Galactic Background Noise at L-Band

NASA Technical Reports Server (NTRS)

LeVine, David M.; Abraham, Saji; Kerr, Yann H.; Wilson, Willam J.; Skou, Niels; Sobjaerg, S.

2004-01-01

The spectral window at L-band (1.413 GHz) is important for passive remote sensing of surface parameters such as soil moisture and sea surface salinity that are needed to understand the hydrological cycle and ocean circulation. Radiation from celestial (mostly galactic) sources is strong in this window and an accurate accounting for this background radiation is often needed for calibration. Modem radio astronomy measurements in this spectral window have been converted into a brightness temperature map of the celestial sky at L-band suitable for use in correcting passive measurements. This paper presents a comparison of the background radiation predicted by this map with measurements made with several modem L-band remote sensing radiometers. The agreement validates the map and the procedure for locating the source of down-welling radiation.

Assessment of Predictive Capabilities of L1 Orbiters using Realtime Solar Wind Data

NASA Astrophysics Data System (ADS)

Holmes, J.; Kasper, J. C.; Welling, D. T.

2017-12-01

Realtime measurements of solar wind conditions at L1 point allow us to predict geomagnetic activity at Earth up to an hour in advance. These predictions are quantified in the form of geomagnetic indices such as Kp and Ap, allowing for a concise, standardized prediction and measurement system. For years, the Space Weather Prediction Center used ACE realtime solar wind data to develop its one and four-hour Kp forecasts, but has in the past year switched to using DSCOVR data as its source. In this study, the performance of both orbiters in predicting Kp over the course of one month was assessed in an attempt to determine whether or not switching to DSCOVR data has resulted in improved forecasts. The period of study was chosen to encompass a time when the satellites were close to each other, and when moderate to high activity was observed. Kp predictions were made using the Geospace Model, part of the Space Weather Modeling Framework, to simulate conditions based on observed solar wind parameters. The performance of each satellite was assessed by comparing the model output to observed data.

Towards feasible and effective predictive wavefront control for adaptive optics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poyneer, L A; Veran, J

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.

Prediction of forces and moments for flight vehicle control effectors. Part 2: An analysis of delta wing aerodynamic control effectiveness in ground effect

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. Here, an investigation of the aerodynamic control effectiveness of highly swept delta planforms operating in ground effect is presented. A vortex-lattice computer program incorporating a free wake is developed as a tool to calculate aerodynamic stability and control derivatives. Data generated using this program are compared to experimental data and to data from other vortex-lattice programs. Results show that an elevon deflection produces greater increments in C sub L and C sub M in ground effect than the same deflection produces out of ground effect and that the free wake is indeed necessary for good predictions near the ground.

Predictive IP controller for robust position control of linear servo system.

PubMed

Lu, Shaowu; Zhou, Fengxing; Ma, Yajie; Tang, Xiaoqi

2016-07-01

Position control is a typical application of linear servo system. In this paper, to reduce the system overshoot, an integral plus proportional (IP) controller is used in the position control implementation. To further improve the control performance, a gain-tuning IP controller based on a generalized predictive control (GPC) law is proposed. Firstly, to represent the dynamics of the position loop, a second-order linear model is used and its model parameters are estimated on-line by using a recursive least squares method. Secondly, based on the GPC law, an optimal control sequence is obtained by using receding horizon, then directly supplies the IP controller with the corresponding control parameters in the real operations. Finally, simulation and experimental results are presented to show the efficiency of proposed scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Inpatient Trial of an Artificial Pancreas Based on Multiple Model Probabilistic Predictive Control with Repeated Large Unannounced Meals

PubMed Central

Niemeyer, Günter; Wilson, Darrell M.; Bequette, B. Wayne; Benassi, Kari S.; Clinton, Paula; Buckingham, Bruce A.

2014-01-01

Abstract Background: Closed-loop control of blood glucose levels in people with type 1 diabetes offers the potential to reduce the incidence of diabetes complications and reduce the patients' burden, particularly if meals do not need to be announced. We therefore tested a closed-loop algorithm that does not require meal announcement. Materials and Methods: A multiple model probabilistic predictive controller (MMPPC) was assessed on four patients, revised to improve performance, and then assessed on six additional patients. Each inpatient admission lasted for 32 h with five unannounced meals containing approximately 1 g/kg of carbohydrate per admission. The system used an Abbott Diabetes Care (Alameda, CA) Navigator® continuous glucose monitor (CGM) and Insulet (Bedford, MA) Omnipod® insulin pump, with the MMPPC implemented through the artificial pancreas system platform. The controller was initialized only with the patient's total daily dose and daily basal pattern. Results: On a 24-h basis, the first cohort had mean reference and CGM readings of 179 and 167 mg/dL, respectively, with 53% and 62%, respectively, of readings between 70 and 180 mg/dL and four treatments for glucose values <70 mg/dL. The second cohort had mean reference and CGM readings of 161 and 142 mg/dL, respectively, with 63% and 78%, respectively, of the time spent euglycemic. There was one controller-induced hypoglycemic episode. For the 30 unannounced meals in the second cohort, the mean reference and CGM premeal, postmeal maximum, and 3-h postmeal values were 139 and 132, 223 and 208, and 168 and 156 mg/dL, respectively. Conclusions: The MMPPC, tested in-clinic against repeated, large, unannounced meals, maintained reasonable glycemic control with a mean blood glucose level that would equate to a mean glycated hemoglobin value of 7.2%, with only one controller-induced hypoglycemic event occurring in the second cohort. PMID:25259939

Autonomous Motivation Predicts 7-Day Physical Activity in Hong Kong Students.

PubMed

Ha, Amy S; Ng, Johan Y Y

2015-07-01

Autonomous motivation predicts positive health behaviors such as physical activity. However, few studies have examined the relation between motivational regulations and objectively measured physical activity and sedentary behaviors. Thus, we investigated whether different motivational regulations (autonomous motivation, controlled motivation, and amotivation) predicted 7-day physical activity, sedentary behaviors, and health-related quality of life (HRQoL) of students. A total of 115 students (mean age = 11.6 years, 55.7% female) self-reported their motivational regulations and health-related quality of life. Physical activity and sedentary behaviors were measured using accelerometers for seven days. Using multilevel modeling, we found that autonomous motivation predicted higher levels of moderate-to-vigorous physical activity, less sedentary behaviors, and better HRQoL. Controlled motivation and amotivation each only negatively predicted one facet of HRQoL. Results suggested that autonomous motivation could be an important predictor of physical activity behaviors in Hong Kong students. Promotion of this form of motivational regulation may also increase HRQoL. © 2015 The International Association of Applied Psychology.

A Wireless Platform for Energy Efficient Building Control Retrofits

DTIC Science & Technology

2012-08-01

University of Illinois at Urbana Champaign UTRC United Technologies Research Center VFD variable frequency drive WSN wireless sensor network ...demonstration area. .............................................................. 16 Table 4. Cost model for wireless sensor network ...buildings with MPC-based whole-building optimal control and (2) reduction in first costs achievable with a wireless sensor network (WSN)-based

Collaborative Russian-US work in nuclear material protection, control and accounting at the Institute of Physics and Power Engineering. 2: Extension to additional facilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuzin, V.V.; Pshakin, G.M.; Belov, A.P.

1996-12-31

During 1995, collaborative Russian-US nuclear material protection, control, and accounting (MPC and A) tasks at the Institute of Physics and Power Engineering (IPPE) in Obninsk, Russia focused on improving the protection of nuclear materials at the BFS Fast Critical Facility. BFS has tens of thousands of fuel disks containing highly enriched uranium and weapons-grade plutonium that are used to simulate the core configurations of experimental reactors in two critical assemblies. Completed tasks culminated in demonstrations of newly implemented equipment (Russian and US) and methods that enhanced the MPC and A at BFS through computerized accounting, nondestructive inventory verification measurements, personnelmore » identification and access control, physical inventory taking, physical protection, and video surveillance. The collaborative work with US Department of Energy national laboratories is now being extended. In 1996 additional tasks to improve MPC and A have been implemented at BFS, the Technological Laboratory for Fuel Fabrication (TLFF) the Central Storage Facility (CSF), and for the entire site. The TLFF reclads BFS uranium metal fuel disks (process operations and transfers of fissile material). The CSF contains many different types of nuclear material. MPC and A at these additional facilities will be integrated with that at BFS as a prototype site-wide approach. Additional site-wide tasks encompass communications and tamper-indicating devices. Finally, new storage alternatives are being implemented that will consolidate the more attractive nuclear materials in a better-protected nuclear island. The work this year represents not just the addition of new facilities and the site-wide approach, but the systematization of the MPC and A elements that are being implemented as a first step and the more comprehensive ones planned.« less

Pinus Taeda L. response to fertilization, Herbaceous plant control, and woody plant control

Treesearch

Allan E. Tiarks; James D. Haywood

1986-01-01

On an intensively prepared site, a complete fertilizer applied at planting, and control of herbaceous and woody plants for the first 4 years, increased Pinus taeda L. volume at age 5 to 25.9 m3/ha compared to 11.8 m3/ha without the treatments. The fertilizer and competition control factors affected pine...

Network control principles predict neuron function in the Caenorhabditis elegans connectome

PubMed Central

Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László

2017-01-01

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social and technological networks1–3. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode C. elegans4–6, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires twelve neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation7–13, as well as one previously uncharacterised neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed, with single-cell ablations of DD04 or DD05, but not DD02 or DD03, specifically affecting posterior body movements. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterised connectomes. PMID:29045391

Network control principles predict neuron function in the Caenorhabditis elegans connectome

NASA Astrophysics Data System (ADS)

Yan, Gang; Vértes, Petra E.; Towlson, Emma K.; Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László

2017-10-01

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

Network control principles predict neuron function in the Caenorhabditis elegans connectome.

PubMed

Yan, Gang; Vértes, Petra E; Towlson, Emma K; Chew, Yee Lian; Walker, Denise S; Schafer, William R; Barabási, Albert-László

2017-10-26

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

Stabilizing l1-norm prediction models by supervised feature grouping.

PubMed

Kamkar, Iman; Gupta, Sunil Kumar; Phung, Dinh; Venkatesh, Svetha

2016-02-01

Emerging Electronic Medical Records (EMRs) have reformed the modern healthcare. These records have great potential to be used for building clinical prediction models. However, a problem in using them is their high dimensionality. Since a lot of information may not be relevant for prediction, the underlying complexity of the prediction models may not be high. A popular way to deal with this problem is to employ feature selection. Lasso and l1-norm based feature selection methods have shown promising results. But, in presence of correlated features, these methods select features that change considerably with small changes in data. This prevents clinicians to obtain a stable feature set, which is crucial for clinical decision making. Grouping correlated variables together can improve the stability of feature selection, however, such grouping is usually not known and needs to be estimated for optimal performance. Addressing this problem, we propose a new model that can simultaneously learn the grouping of correlated features and perform stable feature selection. We formulate the model as a constrained optimization problem and provide an efficient solution with guaranteed convergence. Our experiments with both synthetic and real-world datasets show that the proposed model is significantly more stable than Lasso and many existing state-of-the-art shrinkage and classification methods. We further show that in terms of prediction performance, the proposed method consistently outperforms Lasso and other baselines. Our model can be used for selecting stable risk factors for a variety of healthcare problems, so it can assist clinicians toward accurate decision making. Copyright © 2015 Elsevier Inc. All rights reserved.

Populating dark matter haloes with galaxies: comparing the 2dFGRS with mock galaxy redshift surveys

NASA Astrophysics Data System (ADS)

Yang, Xiaohu; Mo, H. J.; Jing, Y. P.; van den Bosch, Frank C.; Chu, YaoQuan

2004-06-01

In two recent papers, we developed a powerful technique to link the distribution of galaxies to that of dark matter haloes by considering halo occupation numbers as a function of galaxy luminosity and type. In this paper we use these distribution functions to populate dark matter haloes in high-resolution N-body simulations of the standard ΛCDM cosmology with Ωm= 0.3, ΩΛ= 0.7 and σ8= 0.9. Stacking simulation boxes of 100 h-1 Mpc and 300 h-1 Mpc with 5123 particles each we construct mock galaxy redshift surveys out to a redshift of z= 0.2 with a numerical resolution that guarantees completeness down to 0.01L*. We use these mock surveys to investigate various clustering statistics. The predicted two-dimensional correlation function ξ(rp, π) reveals clear signatures of redshift space distortions. The projected correlation functions for galaxies with different luminosities and types, derived from ξ(rp, π), match the observations well on scales larger than ~3 h-1 Mpc. On smaller scales, however, the model overpredicts the clustering power by about a factor two. Modelling the `finger-of-God' effect on small scales reveals that the standard ΛCDM model predicts pairwise velocity dispersions (PVD) that are ~400 km s-1 too high at projected pair separations of ~1 h-1 Mpc. A strong velocity bias in massive haloes, with bvel≡σgal/σdm~ 0.6 (where σgal and σdm are the velocity dispersions of galaxies and dark matter particles, respectively) can reduce the predicted PVD to the observed level, but does not help to resolve the overprediction of clustering power on small scales. Consistent results can be obtained within the standard ΛCDM model only when the average mass-to-light ratio of clusters is of the order of 1000 (M/L)solar in the B-band. Alternatively, as we show by a simple approximation, a ΛCDM model with σ8~= 0.75 may also reproduce the observational results. We discuss our results in light of the recent WMAP results and the constraints on σ8 obtained

Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model

PubMed Central

Bogard, Matthieu; Ravel, Catherine; Paux, Etienne; Bordes, Jacques; Balfourier, François; Chapman, Scott C.; Le Gouis, Jacques; Allard, Vincent

2014-01-01

Prediction of wheat phenology facilitates the selection of cultivars with specific adaptations to a particular environment. However, while QTL analysis for heading date can identify major genes controlling phenology, the results are limited to the environments and genotypes tested. Moreover, while ecophysiological models allow accurate predictions in new environments, they may require substantial phenotypic data to parameterize each genotype. Also, the model parameters are rarely related to all underlying genes, and all the possible allelic combinations that could be obtained by breeding cannot be tested with models. In this study, a QTL-based model is proposed to predict heading date in bread wheat (Triticum aestivum L.). Two parameters of an ecophysiological model (V sat and P base, representing genotype vernalization requirements and photoperiod sensitivity, respectively) were optimized for 210 genotypes grown in 10 contrasting location × sowing date combinations. Multiple linear regression models predicting V sat and P base with 11 and 12 associated genetic markers accounted for 71 and 68% of the variance of these parameters, respectively. QTL-based V sat and P base estimates were able to predict heading date of an independent validation data set (88 genotypes in six location × sowing date combinations) with a root mean square error of prediction of 5 to 8.6 days, explaining 48 to 63% of the variation for heading date. The QTL-based model proposed in this study may be used for agronomic purposes and to assist breeders in suggesting locally adapted ideotypes for wheat phenology. PMID:25148833

Preschool Inhibitory Control Predicts ADHD Group Status and Inhibitory Weakness in School.

PubMed

Jacobson, Lisa A; Schneider, Heather; Mahone, E Mark

2017-12-26

Discriminative utility of performance measures of inhibitory control was examined in preschool children with and without ADHD to determine whether performance measures added to diagnostic prediction and to prediction of informant-rated day-to-day executive function. Children ages 4-5 years (N = 105, 61% boys; 54 ADHD, medication-naïve) were assessed using performance measures (Auditory Continuous Performance Test for Preschoolers-Commission errors, Conflicting Motor Response Test, NEPSY Statue) and caregiver (parent, teacher) ratings of inhibition (Behavior Rating Inventory of Executive Function-Preschool version). Performance measures and parent and teacher reports of inhibitory control significantly and uniquely predicted ADHD group status; however, performance measures did not add to prediction of group status beyond parent reports. Performance measures did significantly predict classroom inhibitory control (teacher ratings), over and above parent reports of inhibitory control. Performance measures of inhibitory control may be adequate predictors of ADHD status and good predictors of young children's classroom inhibitory control, demonstrating utility as components of clinical assessments. © The Author(s) 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control.

PubMed

Wang, Tong; Gao, Huijun; Qiu, Jianbin

2016-02-01

This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period T(d) is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the outputs and inputs of the device layer subsystems are sampled with sampling period T(u) at operation layer to form the index prediction, which is used to predict the overall performance index at lower frequency. Radial basis function NN is utilized as the prediction function due to its approximation ability. Then, considering the dynamics of the overall closed-loop system, nonlinear model predictive control method is proposed to guarantee the system stability and compensate the network-induced delays and packet dropouts. Finally, a continuous stirred tank reactor system is given in the simulation part to demonstrate the effectiveness of the proposed method.

Model Predictive Control Based Motion Drive Algorithm for a Driving Simulator

NASA Astrophysics Data System (ADS)

Rehmatullah, Faizan

In this research, we develop a model predictive control based motion drive algorithm for the driving simulator at Toronto Rehabilitation Institute. Motion drive algorithms exploit the limitations of the human vestibular system to formulate a perception of motion within the constrained workspace of a simulator. In the absence of visual cues, the human perception system is unable to distinguish between acceleration and the force of gravity. The motion drive algorithm determines control inputs to displace the simulator platform, and by using the resulting inertial forces and angular rates, creates the perception of motion. By using model predictive control, we can optimize the use of simulator workspace for every maneuver while simulating the vehicle perception. With the ability to handle nonlinear constraints, the model predictive control allows us to incorporate workspace limitations.

Flight Validation of a Metrics Driven L(sub 1) Adaptive Control

NASA Technical Reports Server (NTRS)

Dobrokhodov, Vladimir; Kitsios, Ioannis; Kaminer, Isaac; Jones, Kevin D.; Xargay, Enric; Hovakimyan, Naira; Cao, Chengyu; Lizarraga, Mariano I.; Gregory, Irene M.

2008-01-01

The paper addresses initial steps involved in the development and flight implementation of new metrics driven L1 adaptive flight control system. The work concentrates on (i) definition of appropriate control driven metrics that account for the control surface failures; (ii) tailoring recently developed L1 adaptive controller to the design of adaptive flight control systems that explicitly address these metrics in the presence of control surface failures and dynamic changes under adverse flight conditions; (iii) development of a flight control system for implementation of the resulting algorithms onboard of small UAV; and (iv) conducting a comprehensive flight test program that demonstrates performance of the developed adaptive control algorithms in the presence of failures. As the initial milestone the paper concentrates on the adaptive flight system setup and initial efforts addressing the ability of a commercial off-the-shelf AP with and without adaptive augmentation to recover from control surface failures.

Enhancement of L-Threonine Production by Controlling Sequential Carbon-Nitrogen Ratios during Fermentation.

PubMed

Lee, Hyeok-Won; Lee, Hee-Suk; Kim, Chun-Suk; Lee, Jin-Gyeom; Kim, Won-Kyo; Lee, Eun-Gyo; Lee, Hong-Weon

2018-02-28

Controlling the residual glucose concentration is important for improving productivity in L-threonine fermentation. In this study, we developed a procedure to automatically control the feeding quantity of glucose solution as a function of ammonia-water consumption rate. The feeding ratio (R C/N ) of glucose and ammonia water was predetermined via a stoichiometric approach, on the basis of glucose-ammonia water consumption rates. In a 5-L fermenter, 102 g/l L -threonine was obtained using our glucose-ammonia water combined feeding strategy, which was then successfully applied in a 500-L fermenter (89 g/l). Therefore, we conclude that an automatic combination feeding strategy is suitable for improving L-threonine production.

Probabilistic Guidance of Swarms using Sequential Convex Programming

DTIC Science & Technology

2014-01-01

quadcopter fleet [24]. In this paper, sequential convex programming (SCP) [25] is implemented using model predictive control (MPC) to provide real-time...in order to make Problem 1 convex. The details for convexifying this problem can be found in [26]. The main steps are discretizing the problem using

Control and prediction components of movement planning in stuttering vs. nonstuttering adults

PubMed Central

Daliri, Ayoub; Prokopenko, Roman A.; Flanagan, J. Randall; Max, Ludo

2014-01-01

Purpose Stuttering individuals show speech and nonspeech sensorimotor deficiencies. To perform accurate movements, the sensorimotor system needs to generate appropriate control signals and correctly predict their sensory consequences. Using a reaching task, we examined the integrity of these control and prediction components, separately, for movements unrelated to the speech motor system. Method Nine stuttering and nine nonstuttering adults made fast reaching movements to visual targets while sliding an object under the index finger. To quantify control, we determined initial direction error and end-point error. To quantify prediction, we calculated the correlation between vertical and horizontal forces applied to the object—an index of how well vertical force (preventing slip) anticipated direction-dependent variations in horizontal force (moving the object). Results Directional and end-point error were significantly larger for the stuttering group. Both groups performed similarly in scaling vertical force with horizontal force. Conclusions The stuttering group's reduced reaching accuracy suggests limitations in generating control signals for voluntary movements, even for non-orofacial effectors. Typical scaling of vertical force with horizontal force suggests an intact ability to predict the consequences of planned control signals. Stuttering may be associated with generalized deficiencies in planning control signals rather than predicting the consequences of those signals. PMID:25203459

Low trait self-control predicts self-handicapping.

PubMed

Uysal, Ahmet; Knee, C Raymond

2012-02-01

Past research has shown that self-handicapping stems from uncertainty about one's ability and self-presentational concerns. The present studies suggest that low dispositional self-control is also associated with self-handicapping. In 3 studies (N = 289), the association between self-control and self-handicapping was tested. Self-control was operationalized as trait self-control, whereas self-handicapping was operationalized as trait self-handicapping in Study 1 (N = 160), self-reported self-handicapping in Study 2 (N = 74), and behavioral self-handicapping in Study 3 (N = 55). In all 3 studies, hierarchical regression analyses revealed that low self-control predicts self-handicapping, independent of self-esteem, self-doubt, social desirability, and gender. © 2012 The Authors. Journal of Personality © 2012, Wiley Periodicals, Inc.

Real-time economic nonlinear model predictive control for wind turbine control

NASA Astrophysics Data System (ADS)

Gros, Sebastien; Schild, Axel

2017-12-01

Nonlinear model predictive control (NMPC) is a strong candidate to handle the control challenges emerging in the modern wind energy industry. Recent research suggested that wind turbine (WT) control based on economic NMPC (ENMPC) can improve the closed-loop performance and simplify the task of controller design when compared to a classical NMPC approach. This paper establishes a formal relationship between the ENMPC controller and the classic NMPC approach, and compares empirically their closed-loop nominal behaviour and performance. The robustness of the performance is assessed for an inaccurate modelling of the tower fore-aft main frequency. Additionally, though a perfect wind preview is assumed here, the effect of having a limited horizon of preview of the wind speed via the LIght Detection And Ranging (LIDAR) sensor is investigated. Finally, this paper provides new algorithmic solutions for deploying ENMPC for WT control, and report improved computational times.

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

NASA Astrophysics Data System (ADS)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

Fault Tolerance Analysis of L1 Adaptive Control System for Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Krishnamoorthy, Kiruthika

Trajectory tracking is a critical element for the better functionality of autonomous vehicles. The main objective of this research study was to implement and analyze L1 adaptive control laws for autonomous flight under normal and upset flight conditions. The West Virginia University (WVU) Unmanned Aerial Vehicle flight simulation environment was used for this purpose. A comparison study between the L1 adaptive controller and a baseline conventional controller, which relies on position, proportional, and integral compensation, has been performed for a reduced size jet aircraft, the WVU YF-22. Special attention was given to the performance of the proposed control laws in the presence of abnormal conditions. The abnormal conditions considered are locked actuators (stabilator, aileron, and rudder) and excessive turbulence. Several levels of abnormal condition severity have been considered. The performance of the control laws was assessed over different-shape commanded trajectories. A set of comprehensive evaluation metrics was defined and used to analyze the performance of autonomous flight control laws in terms of control activity and trajectory tracking errors. The developed L1 adaptive control laws are supported by theoretical stability guarantees. The simulation results show that L1 adaptive output feedback controller achieves better trajectory tracking with lower level of control actuation as compared to the baseline linear controller under nominal and abnormal conditions.

Cognitive demand and predictive adaptational responses in dynamic stability control.

PubMed

Bohm, Sebastian; Mersmann, Falk; Bierbaum, Stefanie; Dietrich, Ralf; Arampatzis, Adamantios

2012-09-21

We studied the effects of a concurrent cognitive task on predictive motor control, a feedforward mechanism of dynamic stability control, during disturbed gait in young and old adults. Thirty-two young and 27 elderly male healthy subjects participated and were randomly assigned to either control or dual task groups. By means of a covered exchangeable element the surface condition on a gangway could be altered to induce gait perturbations. The experimental protocol included a baseline on hard surface and an adaptation phase with twelve trials on soft surface. After the first, sixth and last soft surface trial, the surface condition was changed to hard (H1-3), to examine after-effects and, thus, to quantify predictive motor control. Dynamic stability was assessed using the 'margin of stability (MoS)' as a criterion for the stability state of the human body (extrapolated center of mass concept). In H1-3 the young participants significantly increased the MoS at touchdown of the disturbed leg compared to baseline. The magnitude and the rate of these after-effects were unaffected by the dual task condition. The old participants presented a trend to after-effects (i.e., increase of MoS) in H3 but only under the dual task condition.In conclusion, the additional cognitive demand did not compromise predictive motor control during disturbed walking in the young and old participants. In contrast to the control group, the old dual task group featured a trend to predictive motor adjustments, which may be a result of a higher state of attention or arousal due to the dual task paradigm. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

DTIC Science & Technology

2016-04-30

adaptive optics. 15. SUBJECT TERMS control, filtering, prediction, system identification, adaptive optics, laser beam pointing, target tracking, phase... laser beam control; furthermore, wavefront sensors are plagued by the difficulty of maintaining the required alignment and focusing in dynamic mission...developed new methods for filtering, prediction and system identification in adaptive optics for high energy laser systems including phased arrays. The

Control of nonlinear flexible space structures

NASA Astrophysics Data System (ADS)

Shi, Jianjun

With the advances made in computer technology and efficiency of numerical algorithms over last decade, the MPC strategies have become quite popular among control community. However, application of MPC or GPC to flexible space structure control has not been explored adequately in the literature. The work presented in this thesis primarily focuses on application of GPC to control of nonlinear flexible space structures. This thesis is particularly devoted to the development of various approximate dynamic models, design and assessment of candidate controllers, and extensive numerical simulations for a realistic multibody flexible spacecraft, namely, Jupiter Icy Moons Orbiter (JIMO)---a Prometheus class of spacecraft proposed by NASA for deep space exploratory missions. A stable GPC algorithm is developed for Multi-Input-Multi-Output (MIMO) systems. An end-point weighting (penalty) is used in the GPC cost function to guarantee the nominal stability of the closed-loop system. A method is given to compute the desired end-point state from the desired output trajectory. The methodologies based on Fake Algebraic Riccati Equation (FARE) and constrained nonlinear optimization, are developed for synthesis of state weighting matrix. This makes this formulation more practical. A stable reconfigurable GPC architecture is presented and its effectiveness is demonstrated on both aircraft as well as spacecraft model. A representative in-orbit maneuver is used for assessing the performance of various control strategies using various design models. Different approximate dynamic models used for analysis include linear single body flexible structure, nonlinear single body flexible structure, and nonlinear multibody flexible structure. The control laws evaluated include traditional GPC, feedback linearization-based GPC (FLGPC), reconfigurable GPC, and nonlinear dissipative control. These various control schemes are evaluated for robust stability and robust performance in the presence of

Einstein SSS and MPC observations of Aql X-1 and 4U1820-30

NASA Technical Reports Server (NTRS)

Kelley, R. L.; Christian, D. J.; Schoelkopf, R. J.; Swank, J. H.

1989-01-01

The results of timing and spectral analyses of the X-ray sources Aql X-1 (X1908+005) and 4U1820-30 (NGC6624) are reported using data obtained with the Einstein SSS (Solid State Spectrometer) and MPC (Monitor Proportional Counter) instruments. A classic type I burst was observed from Aql X-1 in both detectors and a coherent modulation with a period of 131.66 + or - 0.02 ms and a pulsed fraction of 10 percent was detected in the SSS data. There is no evidence for a loss of coherance during the approximately 80 sec when the burst is observable. The 2 sigma upper limit on the rate of change of the pulse period is 0.00005s/s. It is argued that an asymmetrical burst occurring on a neutron star rotating at 7.6 Hz offers a plausible explanation for the oscillation. The data from 4U1820-30 show that the amplitude of the 685 sec modulation, identified as the orbital period, is independent of energy down to 0.6 keV. The SSS data show that the light curve in the 0.6 to 4.5 keV band is smoother than at higher energies.

Efficacy of predictive wavefront control for compensating aero-optical aberrations

NASA Astrophysics Data System (ADS)

Goorskey, David J.; Schmidt, Jason; Whiteley, Matthew R.

2013-07-01

Imaging and laser beam propagation from airborne platforms are degraded by dynamic aberrations due to air flow around the aircraft, aero-mechanical distortions and jitter, and free atmospheric turbulence. For certain applications, like dim-object imaging, free-space optical communications, and laser weapons, adaptive optics (AO) is necessary to compensate for the aberrations in real time. Aero-optical flow is a particularly interesting source of aberrations whose flowing structures can be exploited by adaptive and predictive AO controllers, thereby realizing significant performance gains. We analyze dynamic aero-optical wavefronts to determine the pointing angles at which predictive wavefront control is more effective than conventional, fixed-gain, linear-filter control. It was found that properties of the spatial decompositions and temporal statistics of the wavefronts are directly traceable to specific features in the air flow. Furthermore, the aero-optical wavefront aberrations at the side- and aft-looking angles were the most severe, but they also benefited the most from predictive AO.

Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks.

PubMed

León-Roque, Noemí; Abderrahim, Mohamed; Nuñez-Alejos, Luis; Arribas, Silvia M; Condezo-Hoyos, Luis

2016-12-01

Several procedures are currently used to assess fermentation index (FI) of cocoa beans (Theobroma cacao L.) for quality control. However, all of them present several drawbacks. The aim of the present work was to develop and validate a simple image based quantitative procedure, using color measurement and artificial neural network (ANNs). ANN models based on color measurements were tested to predict fermentation index (FI) of fermented cocoa beans. The RGB values were measured from surface and center region of fermented beans in images obtained by camera and desktop scanner. The FI was defined as the ratio of total free amino acids in fermented versus non-fermented samples. The ANN model that included RGB color measurement of fermented cocoa surface and R/G ratio in cocoa bean of alkaline extracts was able to predict FI with no statistical difference compared with the experimental values. Performance of the ANN model was evaluated by the coefficient of determination, Bland-Altman plot and Passing-Bablok regression analyses. Moreover, in fermented beans, total sugar content and titratable acidity showed a similar pattern to the total free amino acid predicted through the color based ANN model. The results of the present work demonstrate that the proposed ANN model can be adopted as a low-cost and in situ procedure to predict FI in fermented cocoa beans through apps developed for mobile device. Copyright © 2016 Elsevier B.V. All rights reserved.

Manufacture of modified milk protein concentrate utilizing injection of carbon dioxide.

PubMed

Marella, Chenchaiah; Salunke, P; Biswas, A C; Kommineni, A; Metzger, L E

2015-06-01

Dried milk protein concentrate is produced from skim milk using a combination of processes such as ultrafiltration (UF), evaporation or nanofiltration, and spray drying. It is well established that dried milk protein concentrate (MPC) that contains 80% (MPC80) and greater protein content (relative to dry matter) can lose solubility during storage as a result of protein-protein interactions and formation of insoluble complexes. Previous studies have shown that partial replacement of calcium with sodium improves MPC80 functionality and prevents the loss in solubility during storage. Those studies have used pH adjustment with the addition of acids, addition of monovalent salts, or ion exchange treatment of UF retentate. The objective of this study was to use carbon dioxide to produce MPC80 with improved functionality. In this study, reduced-calcium MPC80 (RCMPC) was produced from skim milk that was subjected to injection of 2,200 ppm of CO2 before UF, along with additional CO2 injection at a flow rate of 1.5 to 2 L/min during UF. A control MPC80 (CtrlMPC) was also produced from the same lot of skim milk without injection of CO2. The above processes were replicated 3 times, using different lots of skim milk for each replication. All the UF retentates were spray dried using a pilot-scale dryer. Skim milk and UF retentates were tested for ζ-potential (net negative charge), particle size, and viscosity. All the MPC were stored at room (22±1°C) and elevated (40°C) temperatures for 6 mo. Solubility was measured by dissolving the dried MPC in water at 22°C and at 10°C (cold solubility). Injection of CO2 and the resultant solubilization of calcium phosphate had a significant effect on UF performance, resulting in 10 and 20% loss in initial and average flux, respectively. Processing of skim milk with injection of CO2 also resulted in higher irreversible fouling resistances. Compared with control, the reduced-calcium MPC had 28 and 34% less ash and calcium, respectively

Model Predictive Control of LCL Three-level Photovoltaic Grid-connected Inverter

NASA Astrophysics Data System (ADS)

Liang, Cheng; Tian, Engang; Pang, Baobing; Li, Juan; Yang, Yang

2018-05-01

In this paper, neutral point clamped three-level inverter circuit is analyzed to establish a mathematical model of the three-level inverter in the αβ coordinate system. The causes and harms of the midpoint potential imbalance problem are described. The paper use the method of model predictive control to control the entire inverter circuit[1]. The simulation model of the inverter system is built in Matlab/Simulink software. It is convenient to control the grid-connected current, suppress the unbalance of the midpoint potential and reduce the switching frequency by changing the weight coefficient in the cost function. The superiority of the model predictive control in the control method of the inverter system is verified.

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.

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.

Predictions of the near edge transport shortfall in DIII-D L-mode plasmas using the trapped gyro-Landau-fluid model [Predictions of the near edge transport shortfall in DIII-D L-mode plasmas using the TGLF model

DOE PAGES

Kinsey, Jon E.; Staebler, Gary M.; Candy, Jefferey M.; ...

2015-01-14

Previous studies of DIII-D L-mode plasmas have shown that a transport shortfall exists in that our current models of turbulent transport can significantly underestimate the energy transport in the near edge region. In this paper, the Trapped Gyro-Landau-Fluid (TGLF) drift wave transport model is used to simulate the near edge transport in a DIII-D L-mode experiment designed to explore the impact of varying the safety factor on the shortfall. We find that the shortfall systematically increases with increasing safety factor and is more pronounced for the electrons than for the ions. Within the shortfall dataset, a single high current casemore » has been found where no transport shortfall is predicted. Reduced neutral beam injection power has been identified as the key parameter separating this discharge from other discharges exhibiting a shortfall. Further analysis shows that the energy transport in the L-mode near edge region is not stiff according to TGLF. Unlike the H-mode core region, the predicted temperature profiles are relatively more responsive to changes in auxiliary heating power. In testing the fidelity of TGLF for the near edge region, we find that a recalibration of the collision model is warranted. A recalibration improves agreement between TGLF and nonlinear gyrokinetic simulations performed using the GYRO code with electron-ion collisions. As a result, the recalibration only slightly impacts the predicted shortfall.« less

Model-based Optimization and Feedback Control of the Current Density Profile Evolution in NSTX-U

NASA Astrophysics Data System (ADS)

Ilhan, Zeki Okan

trajectories and analyzing the resulting plasma evolution. Finally, the proposed control-oriented model is embedded in feedback control schemes based on optimal control and Model Predictive Control (MPC) approaches. Integrators are added to the standard Linear Quadratic Gaussian (LQG) and MPC formulations to provide robustness against various modeling uncertainties and external disturbances. The effectiveness of the proposed feedback controllers in regulating the current density profile in NSTX-U is demonstrated in closed-loop nonlinear simulations. Moreover, the optimal feedback control algorithm has been implemented successfully in closed-loop control simulations within TRANSP through the recently developed Expert routine. (Abstract shortened by ProQuest.).

Interpreting Disruption Prediction Models to Improve Plasma Control

NASA Astrophysics Data System (ADS)

Parsons, Matthew

2017-10-01

In order for the tokamak to be a feasible design for a fusion reactor, it is necessary to minimize damage to the machine caused by plasma disruptions. Accurately predicting disruptions is a critical capability for triggering any mitigative actions, and a modest amount of attention has been given to efforts that employ machine learning techniques to make these predictions. By monitoring diagnostic signals during a discharge, such predictive models look for signs that the plasma is about to disrupt. Typically these predictive models are interpreted simply to give a `yes' or `no' response as to whether a disruption is approaching. However, it is possible to extract further information from these models to indicate which input signals are more strongly correlated with the plasma approaching a disruption. If highly accurate predictive models can be developed, this information could be used in plasma control schemes to make better decisions about disruption avoidance. This work was supported by a Grant from the 2016-2017 Fulbright U.S. Student Program, administered by the Franco-American Fulbright Commission in France.

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.

Robustness analysis of an air heating plant and control law by using polynomial chaos

DOE Office of Scientific and Technical Information (OSTI.GOV)

Colón, Diego; Ferreira, Murillo A. S.; Bueno, Átila M.

2014-12-10

This paper presents a robustness analysis of an air heating plant with a multivariable closed-loop control law by using the polynomial chaos methodology (MPC). The plant consists of a PVC tube with a fan in the air input (that forces the air through the tube) and a mass flux sensor in the output. A heating resistance warms the air as it flows inside the tube, and a thermo-couple sensor measures the air temperature. The plant has thus two inputs (the fan's rotation intensity and heat generated by the resistance, both measured in percent of the maximum value) and two outputsmore » (air temperature and air mass flux, also in percent of the maximal value). The mathematical model is obtained by System Identification techniques. The mass flux sensor, which is nonlinear, is linearized and the delays in the transfer functions are properly approximated by non-minimum phase transfer functions. The resulting model is transformed to a state-space model, which is used for control design purposes. The multivariable robust control design techniques used is the LQG/LTR, and the controllers are validated in simulation software and in the real plant. Finally, the MPC is applied by considering some of the system's parameters as random variables (one at a time, and the system's stochastic differential equations are solved by expanding the solution (a stochastic process) in an orthogonal basis of polynomial functions of the basic random variables. This method transforms the stochastic equations in a set of deterministic differential equations, which can be solved by traditional numerical methods (That is the MPC). Statistical data for the system (like expected values and variances) are then calculated. The effects of randomness in the parameters are evaluated in the open-loop and closed-loop pole's positions.« less

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.

Predictive thermal control applied to HabEx

NASA Astrophysics Data System (ADS)

Brooks, Thomas E.

2017-09-01

Exoplanet science can be accomplished with a telescope that has an internal coronagraph or with an external starshade. An internal coronagraph architecture requires extreme wavefront stability (10 pm change/10 minutes for 10-10 contrast), so every source of wavefront error (WFE) must be controlled. Analysis has been done to estimate the thermal stability required to meet the wavefront stability requirement. This paper illustrates the potential of a new thermal control method called predictive thermal control (PTC) to achieve the required thermal stability. A simple development test using PTC indicates that PTC may meet the thermal stability requirements. Further testing of the PTC method in flight-like environments will be conducted in the X-ray and Cryogenic Facility (XRCF) at Marshall Space Flight Center (MSFC).

Predictive Thermal Control Applied to HabEx

NASA Technical Reports Server (NTRS)

Brooks, Thomas E.

2017-01-01

Exoplanet science can be accomplished with a telescope that has an internal coronagraph or with an external starshade. An internal coronagraph architecture requires extreme wavefront stability (10 pm change/10 minutes for 10(exp -10) contrast), so every source of wavefront error (WFE) must be controlled. Analysis has been done to estimate the thermal stability required to meet the wavefront stability requirement. This paper illustrates the potential of a new thermal control method called predictive thermal control (PTC) to achieve the required thermal stability. A simple development test using PTC indicates that PTC may meet the thermal stability requirements. Further testing of the PTC method in flight-like environments will be conducted in the X-ray and Cryogenic Facility (XRCF) at Marshall Space Flight Center (MSFC).

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.

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abbas, Nikhar; Tom, Nathan M

2017-06-03

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

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.

Model-Based Closed-Loop Glucose Control in Type 1 Diabetes: The DiaCon Experience

PubMed Central

Schmidt, Signe; Boiroux, Dimitri; Duun-Henriksen, Anne Katrine; Frøssing, Laurits; Skyggebjerg, Ole; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad; Madsen, Henrik; Madsbad, Sten; Nørgaard, Kirsten

2013-01-01

Background To improve type 1 diabetes mellitus (T1DM) management, we developed a model predictive control (MPC) algorithm for closed-loop (CL) glucose control based on a linear second-order deterministic-stochastic model. The deterministic part of the model is specified by three patient-specific parameters: insulin sensitivity factor, insulin action time, and basal insulin infusion rate. The stochastic part is identical for all patients but identified from data from a single patient. Results of the first clinical feasibility test of the algorithm are presented. Methods We conducted two randomized crossover studies. Study 1 compared CL with open-loop (OL) control. Study 2 compared glucose control after CL initiation in the euglycemic (CL-Eu) and hyperglycemic (CL-Hyper) ranges, respectively. Patients were studied from 22:00–07:00 on two separate nights. Results Each study included six T1DM patients (hemoglobin A1c 7.2% ± 0.4%). In study 1, hypoglycemic events (plasma glucose < 54 mg/dl) occurred on two OL and one CL nights. Average glucose from 22:00–07:00 was 90 mg/dl [74–146 mg/dl; median (interquartile range)] during OL and 108 mg/dl (101–128 mg/dl) during CL (determined by continuous glucose monitoring). However, median time spent in the range 70–144 mg/dl was 67.9% (3.0–73.3%) during OL and 80.8% (70.5–89.7%) during CL. In study 2, there was one episode of hypoglycemia with plasma glucose <54 mg/dl in a CL-Eu night. Mean glucose from 22:00–07:00 and time spent in the range 70–144 mg/dl were 121 mg/dl (117–133 mg/dl) and 69.0% (30.7–77.9%) in CL-Eu and 149 mg/dl (140–193 mg/dl) and 48.2% (34.9–72.5%) in CL-Hyper, respectively. Conclusions This study suggests that our novel MPC algorithm can safely and effectively control glucose overnight, also when CL control is initiated during hyperglycemia. PMID:24124952

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.

Water hammer prediction and control: the Green's function method

NASA Astrophysics Data System (ADS)

Xuan, Li-Jun; Mao, Feng; Wu, Jie-Zhi

2012-04-01

By Green's function method we show that the water hammer (WH) can be analytically predicted for both laminar and turbulent flows (for the latter, with an eddy viscosity depending solely on the space coordinates), and thus its hazardous effect can be rationally controlled and minimized. To this end, we generalize a laminar water hammer equation of Wang et al. (J. Hydrodynamics, B2, 51, 1995) to include arbitrary initial condition and variable viscosity, and obtain its solution by Green's function method. The predicted characteristic WH behaviors by the solutions are in excellent agreement with both direct numerical simulation of the original governing equations and, by adjusting the eddy viscosity coefficient, experimentally measured turbulent flow data. Optimal WH control principle is thereby constructed and demonstrated.

A possible instance of sexual dimorphism in the tails of two oviraptorosaur dinosaurs

PubMed Central

IV, W. Scott Persons; Funston, Gregory F.; Currie, Philip J.; Norell, Mark A.

2015-01-01

The hypothesis that oviraptorosaurs used tail-feather displays in courtship behavior previously predicted that oviraptorosaurs would be found to display sexually dimorphic caudal osteology. MPC-D 100/1002 and MPC-D 100/1127 are two specimens of the oviraptorosaur Khaan mckennai. Although similar in absolute size and in virtually all other anatomical details, the anterior haemal spines of MPC-D 100/1002 exceed those of MPC-D 100/1127 in ventral depth and develop a hitherto unreported “spearhead” shape. This dissimilarity cannot be readily explained as pathologic and is too extreme to be reasonably attributed to the amount of individual variation expected among con-specifics. Instead, this discrepancy in haemal spine morphology may be attributable to sexual dimorphism. The haemal spine form of MPC-D 100/1002 offers greater surface area for caudal muscle insertions. On this basis, MPC-D 100/1002 is regarded as most probably male, and MPC-D 100/1127 is regarded as most probably female. PMID:25824625

Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator.

PubMed

Dubay, Rickey; Hassan, Marwan; Li, Chunying; Charest, Meaghan

2014-09-01

This paper presents a unique approach for active vibration control of a one-link flexible manipulator. The method combines a finite element model of the manipulator and an advanced model predictive controller to suppress vibration at its tip. This hybrid methodology improves significantly over the standard application of a predictive controller for vibration control. The finite element model used in place of standard modelling in the control algorithm provides a more accurate prediction of dynamic behavior, resulting in enhanced control. Closed loop control experiments were performed using the flexible manipulator, instrumented with strain gauges and piezoelectric actuators. In all instances, experimental and simulation results demonstrate that the finite element based predictive controller provides improved active vibration suppression in comparison with using a standard predictive control strategy. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Occupant-vehicle dynamics and the role of the internal model

NASA Astrophysics Data System (ADS)

Cole, David J.

2018-05-01

With the increasing need to reduce time and cost of vehicle development there is increasing advantage in simulating mathematically the dynamic interaction of a vehicle and its occupant. The larger design space arising from the introduction of automated vehicles further increases the potential advantage. The aim of the paper is to outline the role of the internal model hypothesis in understanding and modelling occupant-vehicle dynamics, specifically the dynamics associated with direction and speed control of the vehicle. The internal model is the driver's or passenger's understanding of the vehicle dynamics and is thought to be employed in the perception, cognition and action processes of the brain. The internal model aids the estimation of the states of the vehicle from noisy sensory measurements. It can also be used to optimise cognitive control action by predicting the consequence of the action; thus model predictive control (MPC) theory provides a foundation for modelling the cognition process. The stretch reflex of the neuromuscular system also makes use of the prediction of the internal model. Extensions to the MPC approach are described which account for: interaction with an automated vehicle; robust control; intermittent control; and cognitive workload. Further work to extend understanding of occupant-vehicle dynamic interaction is outlined. This paper is based on a keynote presentation given by the author to the 13th International Symposium on Advanced Vehicle Control (AVEC) conference held in Munich, September 2016.

Robust model predictive control for satellite formation keeping with eccentricity/inclination vector separation

NASA Astrophysics Data System (ADS)

Lim, Yeerang; Jung, Youeyun; Bang, Hyochoong

2018-05-01

This study presents model predictive formation control based on an eccentricity/inclination vector separation strategy. Alternative collision avoidance can be accomplished by using eccentricity/inclination vectors and adding a simple goal function term for optimization process. Real-time control is also achievable with model predictive controller based on convex formulation. Constraint-tightening approach is address as well improve robustness of the controller, and simulation results are presented to verify performance enhancement for the proposed approach.

Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization.

PubMed

Li, Zhijun; Xia, Yuanqing; Su, Chun-Yi; Deng, Jun; Fu, Jun; He, Wei

2015-08-01

In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed by incorporating missile inside constraints. The combined model predictive approach could be transformed as a constrained quadratic programming (QP) problem, which may be solved using a linear variational inequality-based primal-dual neural network over a finite receding horizon. Online solutions to multiple parametric QP problems are used so that constrained optimal control decisions can be made in real time. Simulation studies are conducted to illustrate the effectiveness and performance of the proposed guidance control law for missile interception.

The Mpc-scale radio source associated with the GPS galaxy B1144+352

NASA Astrophysics Data System (ADS)

Schoenmakers, A. P.; de Bruyn, A. G.; Röttgering, H. J. A.; van der Laan, H.

1999-01-01