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Sample records for iterative learning controller

  1. Velocity observer-based iterative learning control for robot manipulators

    NASA Astrophysics Data System (ADS)

    Bouakrif, Farah; Boukhetala, Djamel; Boudjema, Farès

    2013-02-01

    This article addresses the problem of designing an iterative learning control for trajectory tracking of rigid robot manipulators subject to external disturbances, and performing repetitive tasks, without using the velocity measurement. For solving this problem, a velocity observer having an iterative form is proposed to reconstruct the velocity signal in the control laws. Under assumptions that the disturbances are repetitive and the velocities are bounded, it has been shown that the whole control system (robot plus controller plus observer) is asymptotically stable and the observation error is globally asymptotically stable, over the whole finite time-interval when the iteration number tends to infinity. This proof is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed observer-controller schemes.

  2. Iterative learning control for the filling of wet clutches

    NASA Astrophysics Data System (ADS)

    Pinte, G.; Depraetere, B.; Symens, W.; Swevers, J.; Sas, P.

    2010-10-01

    This paper discusses the development of an advanced iterative learning control (ILC) scheme for the filling of wet clutches. In the presented scheme, the appropriate actuator signal for a new clutch engagement is learned automatically based on the quality of previous engagements, such that time-consuming and cumbersome calibrations can be avoided. First, an ILC controller, which uses the position of the piston as control input, is developed and tested on a non-rotating clutch under well controlled conditions. Afterwards, a similar strategy is tested on a rotating set-up, where a pressure sensor is used as the input of the ILC controller. On a higher level, both the position and the pressure controller are extended with a second learning algorithm, that adapts the reference position/pressure to account for environmental changes which cannot be learned by the low-level ILC controller. It is shown that a strong reduction of the transmitted torque level as well as a significant shortening of the engagement time can be achieved with the developed strategy, compared to traditional time-invariant control strategies.

  3. Multi-input square iterative learning control with input rate limits and bounds.

    PubMed

    Driessen, B J; Sadegh, N

    2002-01-01

    We present a simple modification of the iterative learning control algorithm of Arimoto et al. (1984) for the case where the inputs are bounded and time-rate-limited. The Jacobian error condition for monotonicity of input-error, rather than output-error, norms, is specified, the latter being insufficient to assure convergence, as proved herein. To the best of our knowledge, these facts have not been previously pointed out in the iterative learning control literature. We present a new proof that the modified controller produces monotonically decreasing input error norms, with a norm that covers the entire time interval of a learning trial. PMID:18238150

  4. Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations

    NASA Astrophysics Data System (ADS)

    Zhang, Ruikun; Hou, Zhongsheng; Ji, Honghai; Yin, Chenkun

    2016-04-01

    In this paper, an adaptive iterative learning control scheme is proposed for a class of non-linearly parameterised systems with unknown time-varying parameters and input saturations. By incorporating a saturation function, a new iterative learning control mechanism is presented which includes a feedback term and a parameter updating term. Through the use of parameter separation technique, the non-linear parameters are separated from the non-linear function and then a saturated difference updating law is designed in iteration domain by combining the unknown parametric term of the local Lipschitz continuous function and the unknown time-varying gain into an unknown time-varying function. The analysis of convergence is based on a time-weighted Lyapunov-Krasovskii-like composite energy function which consists of time-weighted input, state and parameter estimation information. The proposed learning control mechanism warrants a L2[0, T] convergence of the tracking error sequence along the iteration axis. Simulation results are provided to illustrate the effectiveness of the adaptive iterative learning control scheme.

  5. Implementation of a new iterative learning control algorithm on real data.

    PubMed

    Zamanian, Hamed; Koohi, Ardavan

    2016-02-01

    In this paper, a newly presented approach is proposed for closed-loop automatic tuning of a proportional integral derivative (PID) controller based on iterative learning control (ILC) algorithm. A modified ILC scheme iteratively changes the control signal by adjusting it. Once a satisfactory performance is achieved, a linear compensator is identified in the ILC behavior using casual relationship between the closed loop signals. This compensator is approximated by a PD controller which is used to tune the original PID controller. Results of implementing this approach presented on the experimental data of Damavand tokamak and are consistent with simulation outcome. PMID:26931852

  6. Implementation of a new iterative learning control algorithm on real data

    NASA Astrophysics Data System (ADS)

    Zamanian, Hamed; Koohi, Ardavan

    2016-02-01

    In this paper, a newly presented approach is proposed for closed-loop automatic tuning of a proportional integral derivative (PID) controller based on iterative learning control (ILC) algorithm. A modified ILC scheme iteratively changes the control signal by adjusting it. Once a satisfactory performance is achieved, a linear compensator is identified in the ILC behavior using casual relationship between the closed loop signals. This compensator is approximated by a PD controller which is used to tune the original PID controller. Results of implementing this approach presented on the experimental data of Damavand tokamak and are consistent with simulation outcome.

  7. Control of a pneumatic power active lower-limb orthosis with filter-based iterative learning control

    NASA Astrophysics Data System (ADS)

    Huang, Chia-En; Chen, Jian-Shiang

    2014-05-01

    A filter-based iterative learning control (FILC) scheme is developed in this paper, which consists in a proportional-derivative (PD) feedback controller and a feedforward filter. Moreover, based on two-dimensional system theory, the stability of the FILC system is proven. The design criteria for a wavelet transform filter (WTF) - chosen as the feedforward filter - and the PD feedback controller are also given. Finally, using a pneumatic power active lower-limb orthosis (PPALO) as the controlled plant, the wavelet-based iterative learning control (WILC) implementation and the orchestration of a trajectory tracking control simulation are given in detail and the overall tracking performance is validated.

  8. An iterative learning control method with application for CNC machine tools

    SciTech Connect

    Kim, D.I.; Kim, S.

    1996-01-01

    A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the industrial robot or the CNC machine tool with the path-dependent repetitive task, the distance difference between the desired path and the actual tracked or machined path, which is one of the most significant factors in the evaluation of control performance, is progressively reduced. The experimental results demonstrate that the proposed learning controller can improve machining accuracy when the CNC machine tool performs repetitive machining tasks.

  9. Enhanced iterative learning control for a piezoelectric actuator system using wavelet transform filtering

    NASA Astrophysics Data System (ADS)

    Chien, Chiang-Ju; Lee, Fu-Shin; Wang, Jhen-Cheng

    2007-01-01

    For trajectory tracking of a piezoelectric actuator system, an enhanced iterative learning control (ILC) scheme based on wavelet transform filtering (WTF) is proposed in this research. The enhanced ILC scheme incorporates a state compensation in the ILC formula. Combining state compensation with iterative learning, the scheme enhances tracking accuracies substantially, in comparison to the conventional D-type ILC and a proportional control-aided D-type ILC. The wavelet transform is adopted to filter learnable tracking errors without phase shift. Based on both a time-frequency analysis of tracking errors and a convergence bandwidth analysis of ILC, a two-level WTF is chosen for ILC in this study. The enhanced ILC scheme using WTF was applied to track two desired trajectories, one with a single frequency and the other with multiple frequencies, respectively. Experimental results validate the efficacy of the enhanced ILC in terms of the speed of convergence and the level of long-term tracking errors.

  10. Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

    NASA Astrophysics Data System (ADS)

    Li, Jinsha; Li, Junmin

    2016-07-01

    In this paper, the adaptive fuzzy iterative learning control scheme is proposed for coordination problems of Mth order (M ≥ 2) distributed multi-agent systems. Every follower agent has a higher order integrator with unknown nonlinear dynamics and input disturbance. The dynamics of the leader are a higher order nonlinear systems and only available to a portion of the follower agents. With distributed initial state learning, the unified distributed protocols combined time-domain and iteration-domain adaptive laws guarantee that the follower agents track the leader uniformly on [0, T]. Then, the proposed algorithm extends to achieve the formation control. A numerical example and a multiple robotic system are provided to demonstrate the performance of the proposed approach.

  11. Application of a repetitive process setting to design of monotonically convergent iterative learning control

    NASA Astrophysics Data System (ADS)

    Boski, Marcin; Paszke, Wojciech

    2015-11-01

    This paper deals with the problem of designing an iterative learning control algorithm for discrete linear systems using repetitive process stability theory. The resulting design produces a stabilizing output feedback controller in the time domain and a feedforward controller that guarantees monotonic convergence in the trial-to-trial domain. The results are also extended to limited frequency range design specification. New design procedure is introduced in terms of linear matrix inequality (LMI) representations, which guarantee the prescribed performances of ILC scheme. A simulation example is given to illustrate the theoretical developments.

  12. Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems

    NASA Astrophysics Data System (ADS)

    Li, Zhifu; Hu, Yueming; Li, Di

    2016-08-01

    For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes.

  13. Model-based iterative learning control of Parkinsonian state in thalamic relay neuron

    NASA Astrophysics Data System (ADS)

    Liu, Chen; Wang, Jiang; Li, Huiyan; Xue, Zhiqin; Deng, Bin; Wei, Xile

    2014-09-01

    Although the beneficial effects of chronic deep brain stimulation on Parkinson's disease motor symptoms are now largely confirmed, the underlying mechanisms behind deep brain stimulation remain unclear and under debate. Hence, the selection of stimulation parameters is full of challenges. Additionally, due to the complexity of neural system, together with omnipresent noises, the accurate model of thalamic relay neuron is unknown. Thus, the iterative learning control of the thalamic relay neuron's Parkinsonian state based on various variables is presented. Combining the iterative learning control with typical proportional-integral control algorithm, a novel and efficient control strategy is proposed, which does not require any particular knowledge on the detailed physiological characteristics of cortico-basal ganglia-thalamocortical loop and can automatically adjust the stimulation parameters. Simulation results demonstrate the feasibility of the proposed control strategy to restore the fidelity of thalamic relay in the Parkinsonian condition. Furthermore, through changing the important parameter—the maximum ionic conductance densities of low-threshold calcium current, the dominant characteristic of the proposed method which is independent of the accurate model can be further verified.

  14. Realization of Comfortable Massage by Using Iterative Learning Control Based on EEG

    NASA Astrophysics Data System (ADS)

    Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

    Recently the massage chair is used by a lot of people because they are able to use it easily at home. However a present massage chair only realizes the massage motion. Moreover the massage chair can not consider the user’s condition and massage force. On the other hand, the professional masseur is according to presume the mental condition by patient’s reaction. Then this paper proposes the method of applying masseur’s procedure for the massage chair using iterative learning control based on EEG. And massage force is estimated by acceleration sensor. The realizability of the proposed method is verified by the experimental works using the massage chair.

  15. UKF-based closed loop iterative learning control of epileptiform wave in a neural mass model.

    PubMed

    Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Li, Huiyan

    2015-02-01

    A novel closed loop control framework is proposed to inhibit epileptiform wave in a neural mass model by external electric field, where the unscented Kalman filter method is used to reconstruct dynamics and estimate unmeasurable parameters of the model. Specifically speaking, the iterative learning control algorithm is introduced into the framework to optimize the control signal. In the proposed method, the control effect can be significantly improved based on the observation of the past attempts. Accordingly, the proposed method can effectively suppress the epileptiform wave as well as showing robustness to noises and uncertainties. Lastly, the simulation is carried out to illustrate the feasibility of the proposed method. Besides, this work shows potential value to design model-based feedback controllers for epilepsy treatment. PMID:26052360

  16. Tracking control of nonlinear lumped mechanical continuous-time systems: A model-based iterative learning approach

    NASA Astrophysics Data System (ADS)

    Smolders, K.; Volckaert, M.; Swevers, J.

    2008-11-01

    This paper presents a nonlinear model-based iterative learning control procedure to achieve accurate tracking control for nonlinear lumped mechanical continuous-time systems. The model structure used in this iterative learning control procedure is new and combines a linear state space model and a nonlinear feature space transformation. An intuitive two-step iterative algorithm to identify the model parameters is presented. It alternates between the estimation of the linear and the nonlinear model part. It is assumed that besides the input and output signals also the full state vector of the system is available for identification. A measurement and signal processing procedure to estimate these signals for lumped mechanical systems is presented. The iterative learning control procedure relies on the calculation of the input that generates a given model output, so-called offline model inversion. A new offline nonlinear model inversion method for continuous-time, nonlinear time-invariant, state space models based on Newton's method is presented and applied to the new model structure. This model inversion method is not restricted to minimum phase models. It requires only calculation of the first order derivatives of the state space model and is applicable to multivariable models. For periodic reference signals the method yields a compact implementation in the frequency domain. Moreover it is shown that a bandwidth can be specified up to which learning is allowed when using this inversion method in the iterative learning control procedure. Experimental results for a nonlinear single-input-single-output system corresponding to a quarter car on a hydraulic test rig are presented. It is shown that the new nonlinear approach outperforms the linear iterative learning control approach which is currently used in the automotive industry on durability test rigs.

  17. Gait simulation via a 6-DOF parallel robot with iterative learning control.

    PubMed

    Aubin, Patrick M; Cowley, Matthew S; Ledoux, William R

    2008-03-01

    We have developed a robotic gait simulator (RGS) by leveraging a 6-degree of freedom parallel robot, with the goal of overcoming three significant challenges of gait simulation, including: 1) operating at near physiologically correct velocities; 2) inputting full scale ground reaction forces; and 3) simulating motion in all three planes (sagittal, coronal and transverse). The robot will eventually be employed with cadaveric specimens, but as a means of exploring the capability of the system, we have first used it with a prosthetic foot. Gait data were recorded from one transtibial amputee using a motion analysis system and force plate. Using the same prosthetic foot as the subject, the RGS accurately reproduced the recorded kinematics and kinetics and the appropriate vertical ground reaction force was realized with a proportional iterative learning controller. After six gait iterations the controller reduced the root mean square (RMS) error between the simulated and in situ; vertical ground reaction force to 35 N during a 1.5 s simulation of the stance phase of gait with a prosthetic foot. This paper addresses the design, methodology and validation of the novel RGS. PMID:18334421

  18. Use of PID and Iterative Learning Controls on Improving Intra-Oral Hydraulic Loading System of Dental Implants

    NASA Astrophysics Data System (ADS)

    Huang, Yi-Cheng; Chan, Manuel; Hsin, Yi-Ping; Ko, Ching-Chang

    This study presents the control design and tests of an intra-oral hydraulic system for quantitatively loading of a dental implant. The computer-controlled system was developed and employed for better pressure error compensation by PID (proportional-integral-derivative) control and point-to-point iterative learning algorithm. In vitro experiments showed that implant loading is precisely controlled (error 3%) for 0.5Hz loading without air inclusion, and reasonably performed (error<10%) with air inclusion up to 20% of the total hydraulic volume. The PID controller maintains forces at the desired level while the learning controller eliminates overshoot/undershoot at the onset of each loading cycle. The system can be potentially used for in vivo animal studies for better understanding of how bone responds to implant loading. Quantitative information derived from this biomechanical model will add to improved designs of dental implants.

  19. Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke

    PubMed Central

    2012-01-01

    Background Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Methods Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. Results From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. Conclusions The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this. PMID:22676920

  20. Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement for People With Multiple Sclerosis.

    PubMed

    Sampson, Patrica; Freeman, Chris; Coote, Susan; Demain, Sara; Feys, Peter; Meadmore, Katie; Hughes, Ann-Marie

    2016-02-01

    Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality (VR) training tasks to improve upper limb function in people with multiple sclerosis (pwMS). The system assists patients in following a specified trajectory path, employing an advanced model-based paradigm termed iterative learning control (ILC) to adjust the FES to improve accuracy and maximise voluntary effort. Reaching tasks were repeated six times with ILC learning the optimum control action from previous attempts. A convenience sample of five pwMS was recruited from local MS societies, and the intervention comprised 18 one-hour training sessions over 10 weeks. The accuracy of tracking performance without FES and the amount of FES delivered during training were analyzed using regression analysis. Clinical functioning of the arm was documented before and after treatment with standard tests. Statistically significant results following training included: improved accuracy of tracking performance both when assisted and unassisted by FES; reduction in maximum amount of FES needed to assist tracking; and less impairment in the proximal arm that was trained. The system was well tolerated by all participants with no increase in muscle fatigue reported. This study confirms the feasibility of FES combined with passive robot assistance as a potentially effective intervention to improve arm movement and control in pwMS and provides the basis for a follow-up study. PMID:25823038

  1. Coordinates transformation and learning control for visually-guided voluntary movement with iteration: a Newton-like method in a function space.

    PubMed

    Kawato, M; Isobe, M; Maeda, Y; Suzuki, R

    1988-01-01

    In order to control visually-guided voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: (1) determination of a desired trajectory in the visual coordinates, (2) transformation of the coordinates of the desired trajectory to the body coordinates and (3) generation of motor command. In this paper, the second and the third problems are treated at computational, representational and hardware levels of Marr. We first study the problems at the computational level, and then propose an iterative learning scheme as a possible algorithm. This is a trial and error type learning such as repetitive training of golf swing. The amount of motor command needed to coordinate activities of many muscles is not determined at once, but in a step-wise, trial and error fashion in the course of a set of repetitions. Actually, the motor command in the (n + 1)-th iteration is a sum of the motor command in the n-th iteration plus two modification terms which are, respectively, proportional to acceleration and speed errors between the desired trajectory and the realized trajectory in the n-th iteration. We mathematically formulate this iterative learning control as a Newton-like method in functional spaces and prove its convergence under appropriate mathematical conditions with use of dynamical system theory and functional analysis. Computer simulations of this iterative learning control of a robotic manipulator in the body or visual coordinates are shown. Finally, we propose that areas 2, 5, and 7 of the sensory association cortex are possible sites of this learning control. Further we propose neural network model which acquires transformation matrices from acceleration or velocity to motor command, which are used in these schemes. PMID:3179342

  2. Prospects of ITER Instability Control

    NASA Astrophysics Data System (ADS)

    Kolemen, Egemen

    2015-11-01

    Prospects for real-time MHD stability analysis, plasma response calculations, and their use in ELM, NTM, RWM control and EFC will be discussed. ITER will need various controls to work together in order to achieve the stated goal of Q >= 10 for multiple minutes. These systems will allow operating at high beta while avoiding disruptions that may lead to damage to the reactor. However, it has not yet been demonstrated whether the combined real-time feedback control aim is feasible given the spectrum of plasma instabilities, the quality of the real-time diagnostic measurement/analysis, and the actuator set at ITER. We will explain challenges of instability control for ITER based on experimental and simulation results. We will demonstrate that it will not be possible to parameterize all possible disruption avoidance and ramp down scenarios that ITER may encounter. An alternative approach based on real-time MHD stability analysis and plasma response calculations, and its use in ELM, NTM, RWM control and EFC, will be demonstrated. Supported by the US DOE under DE-AC02-09CH11466.

  3. Iterative learning control for synchronization of reticle stage and wafer stage in step-and-scan lithographic equipment

    NASA Astrophysics Data System (ADS)

    Li, Lan-lan; Hu, Song; Zhao, Li-xin; Ma, Ping

    2013-08-01

    Lithographic equipments are highly complex machines used to manufacture integrated circuits (ICs). To make larger ICs, a larger lens is required, which, however, is prohibitively expensive. The solution to this problem is to expose a chip not in one flash but in a scanning fashion. For step-and-scan lithographic equipment (wafer scanner), the image quality is decided by many factors, in which synchronization of reticle stage and wafer stage during exposure is a key one. In this paper, the principle of reticle stage and wafer stage was analyzed through investigating the structure of scanners, firstly. While scanning, the reticle stage and wafer stage should scan simultaneously at a high speed and the speed ratio is 1:4. Secondly, an iterative learning controller (ILC) for synchronization of reticle stage and wafer stage is presented. In the controller, a master-slave structure is used, with the wafer stage acting as the master, and the reticle stage as the slave. Since the scanning process of scanner is repetitive, ILC is used to improve tracking performance. A simple design procedure is presented which allows design of the ILC system for the reticle stage and wafer stage independently. Finally, performance of the algorithm is illustrated by simulated on the virtual stages (the reticle stage and wafer stage).The results of simulation experiments and theory analyzing demonstrate that using the proposed controller better synchronization performance can be obtained for the reticle stage and wafer stage in scanner. Theory analysis and experiment shows the method is reasonable and efficient.

  4. ITER Plasma Control System Development

    NASA Astrophysics Data System (ADS)

    Snipes, Joseph; ITER PCS Design Team

    2015-11-01

    The development of the ITER Plasma Control System (PCS) continues with the preliminary design phase for 1st plasma and early plasma operation in H/He up to Ip = 15 MA in L-mode. The design is being developed through a contract between the ITER Organization and a consortium of plasma control experts from EU and US fusion laboratories, which is expected to be completed in time for a design review at the end of 2016. This design phase concentrates on breakdown including early ECH power and magnetic control of the poloidal field null, plasma current, shape, and position. Basic kinetic control of the heating (ECH, ICH, NBI) and fueling systems is also included. Disruption prediction, mitigation, and maintaining stable operation are also included because of the high magnetic and kinetic stored energy present already for early plasma operation. Support functions for error field topology and equilibrium reconstruction are also required. All of the control functions also must be integrated into an architecture that will be capable of the required complexity of all ITER scenarios. A database is also being developed to collect and manage PCS functional requirements from operational scenarios that were defined in the Conceptual Design with links to proposed event handling strategies and control algorithms for initial basic control functions. A brief status of the PCS development will be presented together with a proposed schedule for design phases up to DT operation.

  5. Iterative learning control applied to a non-linear vortex panel model for improved aerodynamic load performance of wind turbines with smart rotors

    NASA Astrophysics Data System (ADS)

    Blackwell, Mark W.; Tutty, Owen R.; Rogers, Eric; Sandberg, Richard D.

    2016-01-01

    The inclusion of smart devices in wind turbine rotor blades could, in conjunction with collective and individual pitch control, improve the aerodynamic performance of the rotors. This is currently an active area of research with the primary objective of reducing the fatigue loads but mitigating the effects of extreme loads is also of interest. The aerodynamic loads on a wind turbine blade contain periodic and non-periodic components and one approach is to consider the application of iterative learning control algorithms. In this paper, the control design is based on a simple, in relative terms, computational fluid dynamics model that uses non-linear wake effects to represent flow past an airfoil. A representation for the actuator dynamics is included to undertake a detailed investigation into the level of control possible and on how performance can be effectively measured.

  6. Language Evolution by Iterated Learning with Bayesian Agents

    ERIC Educational Resources Information Center

    Griffiths, Thomas L.; Kalish, Michael L.

    2007-01-01

    Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute…

  7. Learning to improve iterative repair scheduling

    NASA Technical Reports Server (NTRS)

    Zweben, Monte; Davis, Eugene

    1992-01-01

    This paper presents a general learning method for dynamically selecting between repair heuristics in an iterative repair scheduling system. The system employs a version of explanation-based learning called Plausible Explanation-Based Learning (PEBL) that uses multiple examples to confirm conjectured explanations. The basic approach is to conjecture contradictions between a heuristic and statistics that measure the quality of the heuristic. When these contradictions are confirmed, a different heuristic is selected. To motivate the utility of this approach we present an empirical evaluation of the performance of a scheduling system with respect to two different repair strategies. We show that the scheduler that learns to choose between the heuristics outperforms the same scheduler with any one of two heuristics alone.

  8. Novel aspects of plasma control in ITER

    SciTech Connect

    Humphreys, D.; Jackson, G.; Walker, M.; Welander, A.; Ambrosino, G.; Pironti, A.; Felici, F.; Kallenbach, A.; Raupp, G.; Treutterer, W.; Kolemen, E.; Lister, J.; Sauter, O.; Moreau, D.; Schuster, E.

    2015-02-15

    ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily for ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g., current profile regulation, tearing mode (TM) suppression), control mathematics (e.g., algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g., methods for management of highly subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.

  9. Novel aspects of plasma control in ITER

    NASA Astrophysics Data System (ADS)

    Humphreys, D.; Ambrosino, G.; de Vries, P.; Felici, F.; Kim, S. H.; Jackson, G.; Kallenbach, A.; Kolemen, E.; Lister, J.; Moreau, D.; Pironti, A.; Raupp, G.; Sauter, O.; Schuster, E.; Snipes, J.; Treutterer, W.; Walker, M.; Welander, A.; Winter, A.; Zabeo, L.

    2015-02-01

    ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily for ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g., current profile regulation, tearing mode (TM) suppression), control mathematics (e.g., algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g., methods for management of highly subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.

  10. Kernel-based least squares policy iteration for reinforcement learning.

    PubMed

    Xu, Xin; Hu, Dewen; Lu, Xicheng

    2007-07-01

    In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating

  11. ITER Shape Controller and Transport Simulations

    SciTech Connect

    Casper, T A; Meyer, W H; Pearlstein, L D; Portone, A

    2007-05-31

    We currently use the CORSICA integrated modeling code for scenario studies for both the DIII-D and ITER experiments. In these simulations, free- or fixed-boundary equilibria are simultaneously converged with thermal evolution determined from transport models providing temperature and current density profiles. Using a combination of fixed boundary evolution followed by free-boundary calculation to determine the separatrix and coil currents. In the free-boundary calculation, we use the state-space controller representation with transport simulations to provide feedback modeling of shape, vertical stability and profile control. In addition to a tightly coupled calculation with simulator and controller imbedded inside CORSICA, we also use a remote procedure call interface to couple the CORSICA non-linear plasma simulations to the controller environments developed within the Mathworks Matlab/Simulink environment. We present transport simulations using full shape and vertical stability control with evolution of the temperature profiles to provide simulations of the ITER controller and plasma response.

  12. Iter

    NASA Astrophysics Data System (ADS)

    Iotti, Robert

    2015-04-01

    ITER is an international experimental facility being built by seven Parties to demonstrate the long term potential of fusion energy. The ITER Joint Implementation Agreement (JIA) defines the structure and governance model of such cooperation. There are a number of necessary conditions for such international projects to be successful: a complete design, strong systems engineering working with an agreed set of requirements, an experienced organization with systems and plans in place to manage the project, a cost estimate backed by industry, and someone in charge. Unfortunately for ITER many of these conditions were not present. The paper discusses the priorities in the JIA which led to setting up the project with a Central Integrating Organization (IO) in Cadarache, France as the ITER HQ, and seven Domestic Agencies (DAs) located in the countries of the Parties, responsible for delivering 90%+ of the project hardware as Contributions-in-Kind and also financial contributions to the IO, as ``Contributions-in-Cash.'' Theoretically the Director General (DG) is responsible for everything. In practice the DG does not have the power to control the work of the DAs, and there is not an effective management structure enabling the IO and the DAs to arbitrate disputes, so the project is not really managed, but is a loose collaboration of competing interests. Any DA can effectively block a decision reached by the DG. Inefficiencies in completing design while setting up a competent organization from scratch contributed to the delays and cost increases during the initial few years. So did the fact that the original estimate was not developed from industry input. Unforeseen inflation and market demand on certain commodities/materials further exacerbated the cost increases. Since then, improvements are debatable. Does this mean that the governance model of ITER is a wrong model for international scientific cooperation? I do not believe so. Had the necessary conditions for success

  13. ECE for NTM control on ITER

    NASA Astrophysics Data System (ADS)

    van den Brand, H.; de Baar, M. R.; Lopes Cardozo, N. J.; Westerhof, E.

    2012-09-01

    Control of Neoclassical Tearing Modes (NTMs) requires an accurate and low latency detection of the mode position. For a burning H-mode ITER plasma, simulations are conducted for both ECE detected via the equatorial port plug and along the line-of-sight of the ECCD launchers. Simulated ECE is detected using synthetic radiometers, with settings chosen to meet the required accuracy. A video bandwidth of 2 kHz is used which allows for an intermediate frequency bandwidth of BIF = 400 MHz for ECE detected via the equatorial port plug. For ECE detected via the ECCD line-of-sight, an intermediate frequency bandwidth of 1.5 GHz and 1 GHz for the 2/1 and 3/2 NTM respectively suffices for accurate location detection. For both ECE systems, the latency requirements for NTM suppression are fulfilled.

  14. Eliminating Unpredictable Variation through Iterated Learning

    ERIC Educational Resources Information Center

    Smith, Kenny; Wonnacott, Elizabeth

    2010-01-01

    Human languages may be shaped not only by the (individual psychological) processes of language acquisition, but also by population-level processes arising from repeated language learning and use. One prevalent feature of natural languages is that they avoid unpredictable variation. The current work explores whether linguistic predictability might…

  15. Decentralized control of sound radiation using iterative loop recovery.

    PubMed

    Schiller, Noah H; Cabell, Randolph H; Fuller, Chris R

    2010-10-01

    A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units. PMID:20968346

  16. Decentralized Control of Sound Radiation Using Iterative Loop Recovery

    NASA Technical Reports Server (NTRS)

    Schiller, Noah H.; Cabell, Randolph H.; Fuller, Chris R.

    2009-01-01

    A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units.

  17. SAR imaging via iterative adaptive approach and sparse Bayesian learning

    NASA Astrophysics Data System (ADS)

    Xue, Ming; Santiago, Enrique; Sedehi, Matteo; Tan, Xing; Li, Jian

    2009-05-01

    We consider sidelobe reduction and resolution enhancement in synthetic aperture radar (SAR) imaging via an iterative adaptive approach (IAA) and a sparse Bayesian learning (SBL) method. The nonparametric weighted least squares based IAA algorithm is a robust and user parameter-free adaptive approach originally proposed for array processing. We show that it can be used to form enhanced SAR images as well. SBL has been used as a sparse signal recovery algorithm for compressed sensing. It has been shown in the literature that SBL is easy to use and can recover sparse signals more accurately than the l 1 based optimization approaches, which require delicate choice of the user parameter. We consider using a modified expectation maximization (EM) based SBL algorithm, referred to as SBL-1, which is based on a three-stage hierarchical Bayesian model. SBL-1 is not only more accurate than benchmark SBL algorithms, but also converges faster. SBL-1 is used to further enhance the resolution of the SAR images formed by IAA. Both IAA and SBL-1 are shown to be effective, requiring only a limited number of iterations, and have no need for polar-to-Cartesian interpolation of the SAR collected data. This paper characterizes the achievable performance of these two approaches by processing the complex backscatter data from both a sparse case study and a backhoe vehicle in free space with different aperture sizes.

  18. Multiagent reinforcement learning in the Iterated Prisoner's Dilemma.

    PubMed

    Sandholm, T W; Crites, R H

    1996-01-01

    Reinforcement learning (RL) is based on the idea that the tendency to produce an action should be strengthened (reinforced) if it produces favorable results, and weakened if it produces unfavorable results. Q-learning is a recent RL algorithm that does not need a model of its environment and can be used on-line. Therefore, it is well suited for use in repeated games against an unknown opponent. Most RL research has been confined to single-agent settings or to multiagent settings where the agents have totally positively correlated payoffs (team problems) or totally negatively correlated payoffs (zero-sum games). This paper is an empirical study of reinforcement learning in the Iterated Prisoner's Dilemma (IPD), where the agents' payoffs are neither totally positively nor totally negatively correlated. RL is considerably more difficult in such a domain. This paper investigates the ability of a variety of Q-learning agents to play the IPD game against an unknown opponent. In some experiments, the opponent is the fixed strategy Tit-For-Tat, while in others it is another Q-learner. All the Q-learners learned to play optimally against Tit-For-Tat. Playing against another learner was more difficult because the adaptation of the other learner created a non-stationary environment, and because the other learner was not endowed with any a priori knowledge about the IPD game such as a policy designed to encourage cooperation. The learners that were studied varied along three dimensions: the length of history they received as context, the type of memory they employed (lookup tables based on restricted history windows or recurrent neural networks that can theoretically store features from arbitrarily deep in the past), and the exploration schedule they followed. Although all the learners faced difficulties when playing against other learners, agents with longer history windows, lookup table memories, and longer exploration schedules fared best in the IPD games. PMID:8924633

  19. Iterative LQG Controller Design Through Closed-Loop Identification

    NASA Technical Reports Server (NTRS)

    Hsiao, Min-Hung; Huang, Jen-Kuang; Cox, David E.

    1996-01-01

    This paper presents an iterative Linear Quadratic Gaussian (LQG) controller design approach for a linear stochastic system with an uncertain open-loop model and unknown noise statistics. This approach consists of closed-loop identification and controller redesign cycles. In each cycle, the closed-loop identification method is used to identify an open-loop model and a steady-state Kalman filter gain from closed-loop input/output test data obtained by using a feedback LQG controller designed from the previous cycle. Then the identified open-loop model is used to redesign the state feedback. The state feedback and the identified Kalman filter gain are used to form an updated LQC controller for the next cycle. This iterative process continues until the updated controller converges. The proposed controller design is demonstrated by numerical simulations and experiments on a highly unstable large-gap magnetic suspension system.

  20. Iterative inverse kinematics with manipulator configuration control

    SciTech Connect

    Grudic, G.Z.; Lawrence, P.D.

    1993-08-01

    A new method, termed the offset modification method (OM method), for solving the manipulator inverse kinematics problem is presented. The OM method works by modifying the link offset values of a manipulator until it is possible to derive closed-form inverse kinematics equations for the resulting manipulator (termed the model manipulator). This procedure allows one to derive a set of three nonlinear equations in three unknowns that, when numerically solved, give an inverse kinematics solution for the original manipulator. The OM method can be applied to manipulators with any number of degrees of freedom, as long as the manipulator satisfies a given set of conditions (Theorem 1). The OM method is tested on a 6-degree-of-freedom manipulator that has no known closed-form inverse kinematics equations. It is shown that the OM method is applicable to real-time manipulator control, can be used to guarantee convergence to a desired endpoint position and orientation (if it exists), and allows one to directly choose which inverse kinematics solution the algorithm will converge to (as specified in the model manipulator closed-form inverse kinematics equations). Applications of the method to other 6-DOF manipulator geometries and to redundant manipulators (i.e. greater than 6 DOF geometries) are discussed.

  1. ELM control strategies and tools: status and potential for ITER

    NASA Astrophysics Data System (ADS)

    Lang, P. T.; Loarte, A.; Saibene, G.; Baylor, L. R.; Becoulet, M.; Cavinato, M.; Clement-Lorenzo, S.; Daly, E.; Evans, T. E.; Fenstermacher, M. E.; Gribov, Y.; Horton, L. D.; Lowry, C.; Martin, Y.; Neubauer, O.; Oyama, N.; Schaffer, M. J.; Stork, D.; Suttrop, W.; Thomas, P.; Tran, M.; Wilson, H. R.; Kavin, A.; Schmitz, O.

    2013-04-01

    Operating ITER in the reference inductive scenario at the design values of Ip = 15 MA and QDT = 10 requires the achievement of good H-mode confinement that relies on the presence of an edge transport barrier whose pedestal pressure height is key to plasma performance. Strong gradients occur at the edge in such conditions that can drive magnetohydrodynamic instabilities resulting in edge localized modes (ELMs), which produce a rapid energy loss from the pedestal region to the plasma facing components (PFC). Without appropriate control, the heat loads on PFCs during ELMs in ITER are expected to become significant for operation in H-mode at Ip = 6-9 MA operation at higher plasma currents would result in a very reduced life time of the PFCs. Currently, several options are being considered for the achievement of the required level of ELM control in ITER; this includes operation in plasma regimes which naturally have no or very small ELMs, decreasing the ELM energy loss by increasing their frequency by a factor of up to 30 and avoidance of ELMs by actively controlling the edge with magnetic perturbations. Small/no ELM regimes obtained by influencing the edge stability (by plasma shaping, rotational shear control, etc) have shown in present experiments a significant reduction of the ELM heat fluxes compared to type-I ELMs. However, so far they have only been observed under a limited range of pedestal conditions depending on each specific device and their extrapolation to ITER remains uncertain. ELM control by increasing their frequency relies on the controlled triggering of the edge instability leading to the ELM. This has been presently demonstrated with the injection of pellets and with plasma vertical movements; pellets having provided the results more promising for application in ITER conditions. ELM avoidance/suppression takes advantage of the fact that relatively small changes in the pedestal plasma and magnetic field parameters seem to have a large stabilizing

  2. Sawtooth control in ITER using ion cyclotron resonance heating

    SciTech Connect

    Chapman, I. T.; Graves, J P; Johnson, T.; Asunta, O.; Bonoli, P.; Choi, M.; Jaeger, E. F.; Jucker, M.; Sauter, O.

    2011-01-01

    Numerical modeling of the effects of ion cyclotron resonance heating (ICRH) on the stability of the internal kink mode suggests that ICRH should be considered as an essential sawtooth control tool in ITER. Sawtooth control using ICRH is achieved by directly affecting the energy of the internal kink mode rather than through modification of the magnetic shear by driving localized currents. Consequently, ICRH can be seen as complementary to the planned electron cyclotron current drive actuator, and indeed will improve the efficacy of current drive schemes. Simulations of the ICRH distribution using independent RF codes give confidence in numerical predictions that the stabilizing influence of the fusion-born alphas can be negated by appropriately tailored minority (3)He ICRH heating in ITER. Finally, the effectiveness of all sawtooth actuators is shown to increase as the q = 1 surface moves towards the manetic axis, whilst the passive stabilization arising from the alpha and NBI particles decreases.

  3. Acceleration of reinforcement learning by policy evaluation using nonstationary iterative method.

    PubMed

    Senda, Kei; Hattori, Suguru; Hishinuma, Toru; Kohda, Takehisa

    2014-12-01

    Typical methods for solving reinforcement learning problems iterate two steps, policy evaluation and policy improvement. This paper proposes algorithms for the policy evaluation to improve learning efficiency. The proposed algorithms are based on the Krylov Subspace Method (KSM), which is a nonstationary iterative method. The algorithms based on KSM are tens to hundreds times more efficient than existing algorithms based on the stationary iterative methods. Algorithms based on KSM are far more efficient than they have been generally expected. This paper clarifies what makes algorithms based on KSM makes more efficient with numerical examples and theoretical discussions. PMID:24733037

  4. An Iterative Rate-Control Technique for Motion JPEG2000

    NASA Astrophysics Data System (ADS)

    Tzannes, Alexis P.

    2002-11-01

    This paper addresses the problem of controlling the bit rate for image sequences compressed using the Motion JPEG2000 Standard. We propose a computationally efficient iterative technique that is intended for applications where real time (or near real time) encoding is required. Using real world video sequences, we analyze the rate control accuracy and image quality performance of the proposed technique. Although the effectiveness of the technique was demonstrated on high action video sequences, the proposed technique is also applicable to other video sequence encoding applications such as security and surveillance systems or video over the internet.

  5. Linear decentralized learning control

    NASA Technical Reports Server (NTRS)

    Lee, Soo C.; Longman, Richard W.; Phan, Minh

    1992-01-01

    The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this task. The simplest forms of learning control are based on the same concept as integral control, but operating in the domain of the repetitions of the task. This paper studies the use of such controllers in a decentralized system, such as a robot with the controller for each link acting independently. The basic result of the paper is to show that stability of the learning controllers for all subsystems when the coupling between subsystems is turned off, assures stability of the decentralized learning in the coupled system, provided that the sample time in the digital learning controller is sufficiently short.

  6. Indirect decentralized learning control

    NASA Technical Reports Server (NTRS)

    Longman, Richard W.; Lee, Soo C.; Phan, M.

    1992-01-01

    The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper develops improved indirect learning control algorithms, and studies the use of such controllers in decentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The basic result of the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample time in the digital learning controller is sufficiently short.

  7. Robust iterative learning protocols for finite-time consensus of multi-agent systems with interval uncertain topologies

    NASA Astrophysics Data System (ADS)

    Meng, Deyuan; Jia, Yingmin; Du, Junping

    2015-04-01

    This paper is devoted to the robust finite-time output consensus problems of multi-agent systems under directed graphs, where all agents and their communication topologies are subject to interval uncertainties. Distributed protocols are constructed by using iterative learning control (ILC) algorithms, where information is exchanged only at the end of one iteration and learning is used to update the control inputs after each iteration. It is proved that under ILC-based protocols, the finite-time consensus can be achieved with an increasing number of iterations if the communication network of agents is guaranteed to have a spanning tree. Moreover, if the information of any desired terminal output is available to a portion (not necessarily all) of the agents, then the consensus output that all agents finally reach can be enabled to be the desired terminal output. It is also proved that for all ILC-based protocols, gain selections can be provided in terms of bound values, and consensus conditions can be developed associated with bound matrices. Simulation results are given to demonstrate the effectiveness of our theoretical results.

  8. Iterative exponential growth of stereo- and sequence-controlled polymers

    NASA Astrophysics Data System (ADS)

    Barnes, Jonathan C.; Ehrlich, Deborah J. C.; Gao, Angela X.; Leibfarth, Frank A.; Jiang, Yivan; Zhou, Erica; Jamison, Timothy F.; Johnson, Jeremiah A.

    2015-10-01

    Chemists have long sought sequence-controlled synthetic polymers that mimic nature's biopolymers, but a practical synthetic route that enables absolute control over polymer sequence and structure remains a key challenge. Here, we report an iterative exponential growth plus side-chain functionalization (IEG+) strategy that begins with enantiopure epoxides and facilitates the efficient synthesis of a family of uniform >3 kDa macromolecules of varying sequence and stereoconfiguration that are coupled to produce unimolecular polymers (>6 kDa) with sequences and structures that cannot be obtained using traditional polymerization techniques. Selective side-chain deprotection of three hexadecamers is also demonstrated, which imbues each compound with the ability to dissolve in water. We anticipate that these new macromolecules and the general IEG+ strategy will find broad application as a versatile platform for the scalable synthesis of sequence-controlled polymers.

  9. Fixed Point Transformations Based Iterative Control of a Polymerization Reaction

    NASA Astrophysics Data System (ADS)

    Tar, József K.; Rudas, Imre J.

    As a paradigm of strongly coupled non-linear multi-variable dynamic systems the mathematical model of the free-radical polymerization of methyl-metachrylate with azobis (isobutyro-nitrile) as an initiator and toluene as a solvent taking place in a jacketed Continuous Stirred Tank Reactor (CSTR) is considered. In the adaptive control of this system only a single input variable is used as the control signal (the process input, i.e. dimensionless volumetric flow rate of the initiator), and a single output variable is observed (the process output, i.e. the number-average molecular weight of the polymer). Simulation examples illustrate that on the basis of a very rough and primitive model consisting of two scalar variables various fixed-point transformations based convergent iterations result in a novel, sophisticated adaptive control.

  10. Learning control system design based on 2-D theory - An application to parallel link manipulator

    NASA Technical Reports Server (NTRS)

    Geng, Z.; Carroll, R. L.; Lee, J. D.; Haynes, L. H.

    1990-01-01

    An approach to iterative learning control system design based on two-dimensional system theory is presented. A two-dimensional model for the iterative learning control system which reveals the connections between learning control systems and two-dimensional system theory is established. A learning control algorithm is proposed, and the convergence of learning using this algorithm is guaranteed by two-dimensional stability. The learning algorithm is applied successfully to the trajectory tracking control problem for a parallel link robot manipulator. The excellent performance of this learning algorithm is demonstrated by the computer simulation results.

  11. A strategy for sequence control in vinyl polymers via iterative controlled radical cyclization

    PubMed Central

    Hibi, Yusuke; Ouchi, Makoto; Sawamoto, Mitsuo

    2016-01-01

    There is a growing interest in sequence-controlled polymers toward advanced functional materials. However, control of side-chain order for vinyl polymers has been lacking feasibility in the field of polymer synthesis because of the inherent feature of chain-growth propagation. Here we show a general and versatile strategy to control sequence in vinyl polymers through iterative radical cyclization with orthogonally cleavable and renewable bonds. The proposed methodology employs a repetitive and iterative intramolecular cyclization via a radical intermediate in a one-time template with a radical-generating site at one end and an alkene end at the other, each of which is connected to a linker via independently cleavable and renewable bonds. The unique design specifically allowed control of radical addition reaction although inherent chain-growth intermediate (radical species) was used, as well as the iterative cycle and functionalization for resultant side chains, to lead to sequence-controlled vinyl polymers (or oligomers). PMID:26996881

  12. A strategy for sequence control in vinyl polymers via iterative controlled radical cyclization.

    PubMed

    Hibi, Yusuke; Ouchi, Makoto; Sawamoto, Mitsuo

    2016-01-01

    There is a growing interest in sequence-controlled polymers toward advanced functional materials. However, control of side-chain order for vinyl polymers has been lacking feasibility in the field of polymer synthesis because of the inherent feature of chain-growth propagation. Here we show a general and versatile strategy to control sequence in vinyl polymers through iterative radical cyclization with orthogonally cleavable and renewable bonds. The proposed methodology employs a repetitive and iterative intramolecular cyclization via a radical intermediate in a one-time template with a radical-generating site at one end and an alkene end at the other, each of which is connected to a linker via independently cleavable and renewable bonds. The unique design specifically allowed control of radical addition reaction although inherent chain-growth intermediate (radical species) was used, as well as the iterative cycle and functionalization for resultant side chains, to lead to sequence-controlled vinyl polymers (or oligomers). PMID:26996881

  13. A strategy for sequence control in vinyl polymers via iterative controlled radical cyclization

    NASA Astrophysics Data System (ADS)

    Hibi, Yusuke; Ouchi, Makoto; Sawamoto, Mitsuo

    2016-03-01

    There is a growing interest in sequence-controlled polymers toward advanced functional materials. However, control of side-chain order for vinyl polymers has been lacking feasibility in the field of polymer synthesis because of the inherent feature of chain-growth propagation. Here we show a general and versatile strategy to control sequence in vinyl polymers through iterative radical cyclization with orthogonally cleavable and renewable bonds. The proposed methodology employs a repetitive and iterative intramolecular cyclization via a radical intermediate in a one-time template with a radical-generating site at one end and an alkene end at the other, each of which is connected to a linker via independently cleavable and renewable bonds. The unique design specifically allowed control of radical addition reaction although inherent chain-growth intermediate (radical species) was used, as well as the iterative cycle and functionalization for resultant side chains, to lead to sequence-controlled vinyl polymers (or oligomers).

  14. Sawtooth control in JET with ITER relevant low field side resonance ion cyclotron resonance heating and ITER-like wall

    NASA Astrophysics Data System (ADS)

    Graves, J. P.; Lennholm, M.; Chapman, I. T.; Lerche, E.; Reich, M.; Alper, B.; Bobkov, V.; Dumont, R.; Faustin, J. M.; Jacquet, P.; Jaulmes, F.; Johnson, T.; Keeling, D. L.; Liu, Yueqiang; Nicolas, T.; Tholerus, S.; Blackman, T.; Carvalho, I. S.; Coelho, R.; Van Eester, D.; Felton, R.; Goniche, M.; Kiptily, V.; Monakhov, I.; Nave, M. F. F.; Perez von Thun, C.; Sabot, R.; Sozzi, C.; Tsalas, M.

    2015-01-01

    New experiments at JET with the ITER-like wall show for the first time that ITER-relevant low field side resonance first harmonic ion cyclotron resonance heating (ICRH) can be used to control sawteeth that have been initially lengthened by fast particles. In contrast to previous (Graves et al 2012 Nat. Commun. 3 624) high field side resonance sawtooth control experiments undertaken at JET, it is found that the sawteeth of L-mode plasmas can be controlled with less accurate alignment between the resonance layer and the sawtooth inversion radius. This advantage, as well as the discovery that sawteeth can be shortened with various antenna phasings, including dipole, indicates that ICRH is a particularly effective and versatile tool that can be used in future fusion machines for controlling sawteeth. Without sawtooth control, neoclassical tearing modes (NTMs) and locked modes were triggered at very low normalised beta. High power H-mode experiments show the extent to which ICRH can be tuned to control sawteeth and NTMs while simultaneously providing effective electron heating with improved flushing of high Z core impurities. Dedicated ICRH simulations using SELFO, SCENIC and EVE, including wide drift orbit effects, explain why sawtooth control is effective with various antenna phasings and show that the sawtooth control mechanism cannot be explained by enhancement of the magnetic shear. Hybrid kinetic-magnetohydrodynamic stability calculations using MISHKA and HAGIS unravel the optimal sawtooth control regimes in these ITER relevant plasma conditions.

  15. Magnetic Control of Locked Modes in Present Devices and ITER

    NASA Astrophysics Data System (ADS)

    Volpe, F. A.; Sabbagh, S.; Sweeney, R.; Hender, T.; Kirk, A.; La Haye, R. J.; Strait, E. J.; Ding, Y. H.; Rao, B.; Fietz, S.; Maraschek, M.; Frassinetti, L.; in, Y.; Jeon, Y.; Sakakihara, S.

    2014-10-01

    The toroidal phase of non-rotating (``locked'') neoclassical tearing modes was controlled in several devices by means of applied magnetic perturbations. Evidence is presented from various tokamaks (ASDEX Upgrade, DIII-D, JET, J-TEXT, KSTAR), spherical tori (MAST, NSTX) and a reversed field pinch (EXTRAP-T2R). Furthermore, the phase of interchange modes was controlled in the LHD helical device. These results share a common interpretation in terms of torques acting on the mode. Based on this interpretation, it is predicted that control-coil currents will be sufficient to control the phase of locking in ITER. This will be possible both with the internal coils and with the external error-field-correction coils, and might have promising consequences for disruption avoidance (by aiding the electron cyclotron current drive stabilization of locked modes), as well as for spatially distributing heat loads during disruptions. This work was supported in part by the US Department of Energy under DE-SC0008520, DE-FC-02-04ER54698 and DE-AC02-09CH11466.

  16. Nonlinear Burn Control and Operating Point Optimization in ITER

    NASA Astrophysics Data System (ADS)

    Boyer, Mark; Schuster, Eugenio

    2013-10-01

    Control of the fusion power through regulation of the plasma density and temperature will be essential for achieving and maintaining desired operating points in fusion reactors and burning plasma experiments like ITER. In this work, a volume averaged model for the evolution of the density of energy, deuterium and tritium fuel ions, alpha-particles, and impurity ions is used to synthesize a multi-input multi-output nonlinear feedback controller for stabilizing and modulating the burn condition. Adaptive control techniques are used to account for uncertainty in model parameters, including particle confinement times and recycling rates. The control approach makes use of the different possible methods for altering the fusion power, including adjusting the temperature through auxiliary heating, modulating the density and isotopic mix through fueling, and altering the impurity density through impurity injection. Furthermore, a model-based optimization scheme is proposed to drive the system as close as possible to desired fusion power and temperature references. Constraints are considered in the optimization scheme to ensure that, for example, density and beta limits are avoided, and that optimal operation is achieved even when actuators reach saturation. Supported by the NSF CAREER award program (ECCS-0645086).

  17. Not All Wizards Are from Oz: Iterative Design of Intelligent Learning Environments by Communication Capacity Tapering

    ERIC Educational Resources Information Center

    Mavrikis, Manolis; Gutierrez-Santos, Sergio

    2010-01-01

    This paper presents a methodology for the design of intelligent learning environments. We recognise that in the educational technology field, theory development and system-design should be integrated and rely on an iterative process that addresses: (a) the difficulty to elicit precise, concise, and operationalized knowledge from "experts" and (b)…

  18. Application of iterative path revision technique for laser cutting with controlled fracture

    NASA Astrophysics Data System (ADS)

    Tsai, Chwan-Huei; Chen, Chien-Jen

    2004-01-01

    Laser cutting using the controlled fracture technique has great potential to be employed for the ceramic substrate machining. The heat produced on the surface of a ceramic substrate by the laser separates the substrate controllably along the moving path of the laser beam. Because the extension of the breaking frontier is lager than the movement of the laser spot, the actual fracture trajectory deviates from the desired trajectory when cutting a curve or cutting an asymmetrical straight line. To eliminate this deviation, the iterative learning control method is introduced to obtain the optimal laser beam movement path. The fracture contour image is grabbed by a CCD camera after laser cutting completion. A new image processing system is proposed to detect the deviation between the desired cutting path and the actual fracture trajectory. The laser-movement path for the next trial can then be determined according to the iterative path revision algorithm. The actual fracture trajectory converging to the desired cutting path is assured after a few path revisions. The experimental materials used in these experiments are alumina ceramics and the laser source is CO 2 laser. The proposed system can achieve a machining precision of about 0.1 mm.

  19. Multimodal and Adaptive Learning Management: An Iterative Design

    ERIC Educational Resources Information Center

    Squires, David R.; Orey, Michael A.

    2015-01-01

    The purpose of this study is to measure the outcome of a comprehensive learning management system implemented at a Spinal Cord Injury (SCI) hospital in the Southeast United States. Specifically this SCI hospital has been experiencing an evident volume of patients returning seeking more information about the nature of their injuries. Recognizing…

  20. Near-Optimal Controller for Nonlinear Continuous-Time Systems With Unknown Dynamics Using Policy Iteration.

    PubMed

    Dutta, Samrat; Patchaikani, Prem Kumar; Behera, Laxmidhar

    2016-07-01

    This paper presents a single-network adaptive critic-based controller for continuous-time systems with unknown dynamics in a policy iteration (PI) framework. It is assumed that the unknown dynamics can be estimated using the Takagi-Sugeno-Kang fuzzy model with arbitrary precision. The successful implementation of a PI scheme depends on the effective learning of critic network parameters. Network parameters must stabilize the system in each iteration in addition to approximating the critic and the cost. It is found that the critic updates according to the Hamilton-Jacobi-Bellman formulation sometimes lead to the instability of the closed-loop systems. In the proposed work, a novel critic network parameter update scheme is adopted, which not only approximates the critic at current iteration but also provides feasible solutions that keep the policy stable in the next step of training by combining a Lyapunov-based linear matrix inequalities approach with PI. The critic modeling technique presented here is the first of its kind to address this issue. Though multiple literature exists discussing the convergence of PI, however, to the best of our knowledge, there exists no literature, which focuses on the effect of critic network parameters on the convergence. Computational complexity in the proposed algorithm is reduced to the order of (Fz)(n-1) , where n is the fuzzy state dimensionality and Fz is the number of fuzzy zones in the states space. A genetic algorithm toolbox of MATLAB is used for searching stable parameters while minimizing the training error. The proposed algorithm also provides a way to solve for the initial stable control policy in the PI scheme. The algorithm is validated through real-time experiment on a commercial robotic manipulator. Results show that the algorithm successfully finds stable critic network parameters in real time for a highly nonlinear system. PMID:26259150

  1. Comparison between iterative wavefront control algorithm and direct gradient wavefront control algorithm for adaptive optics system

    NASA Astrophysics Data System (ADS)

    Cheng, Sheng-Yi; Liu, Wen-Jin; Chen, Shan-Qiu; Dong, Li-Zhi; Yang, Ping; Xu, Bing

    2015-08-01

    Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltages directly from wavefront slopes through pre-measuring the relational matrix between deformable mirror actuators and Hartmann wavefront sensor with perfect real-time characteristic and stability. However, with increasing the number of sub-apertures in wavefront sensor and deformable mirror actuators of adaptive optics systems, the matrix operation in direct gradient algorithm takes too much time, which becomes a major factor influencing control effect of adaptive optics systems. In this paper we apply an iterative wavefront control algorithm to high-resolution adaptive optics systems, in which the voltages of each actuator are obtained through iteration arithmetic, which gains great advantage in calculation and storage. For AO system with thousands of actuators, the computational complexity estimate is about O(n2) ˜ O(n3) in direct gradient wavefront control algorithm, while the computational complexity estimate in iterative wavefront control algorithm is about O(n) ˜ (O(n)3/2), in which n is the number of actuators of AO system. And the more the numbers of sub-apertures and deformable mirror actuators, the more significant advantage the iterative wavefront control algorithm exhibits. Project supported by the National Key Scientific and Research Equipment Development Project of China (Grant No. ZDYZ2013-2), the National Natural Science Foundation of China (Grant No. 11173008), and the Sichuan Provincial Outstanding Youth Academic Technology Leaders Program, China (Grant No. 2012JQ0012).

  2. Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

    PubMed

    Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

    2013-10-01

    This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example. PMID:24808590

  3. Analysis of Distribution of Time Scores in Iterative Learning Type Courseware Using Fourier Transform

    NASA Astrophysics Data System (ADS)

    Watanabe, Hiroyuki

    In this research, an iterative learning type courseware was made, the distribution of time scores in the courseware is gotten by the learning management system. It is a proposed method by which the distribution of time scores is changed to frequency and to power spectrum using Fourier Transform. The learning process continues until students get the passing scores and are classified by using these values, which are related to average time and the average of scores‧ square. Furthermore, the cross-correlation coefficients between the standard student and students are calculated, and delay times are analyzed. Finally, the transfer functions of some students are calculated, and the characteristics of the learning processes are analyzed.

  4. Analysis of iteration control for turbo decoders in turbo synchronization applications

    NASA Astrophysics Data System (ADS)

    Lehnigk-Emden, T.; Wasenmüller, U.; Gimmler, C.; Wehn, N.

    2009-05-01

    Wireless data transmission results in frequency and phase offsets of the signal in the receiver. In addition, the received symbols are corrupted by noise. Therefore, synchronization and channel coding are vital parts of each receiver in digital communication systems. By combining the phase and frequency synchronization with an advanced iterative channel decoder (inner loop) e.g. turbo codes in an iterative way (outer loop), the communications performance can be further increased. This principle is referred to as turbo synchronization. The energy consumption and the peak throughput of the system depend on the number of iterations for both loops. An advanced iteration control can decrease the mean number of needed iterations by detecting correctly decoded blocks. This leads to a dramatic energy saving or to an increase of throughput. In this paper we present a new stopping criterion for decodable blocks for turbo decoding in interrelation with turbo synchronization. Furthermore the implementation complexity of the turbo decoder is shown on a Xilinx FPGA.

  5. A policy iteration approach to online optimal control of continuous-time constrained-input systems.

    PubMed

    Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L

    2013-09-01

    This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach. PMID:23706414

  6. Learning to Teach Elementary Science Through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

    NASA Astrophysics Data System (ADS)

    Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

    2015-12-01

    Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves together cycles of enactment, core practices in science education and culturally relevant pedagogies. The theoretical foundation draws upon situated learning theory and communities of practice. Using video analysis by PSTs and course artifacts, the authors studied how the iterative process of these cycles guided PSTs development as teachers of elementary science. Findings demonstrate how PSTs were drawing on resources to inform practice, purposefully noticing their practice, renegotiating their roles in teaching, and reconsidering "professional blindness" through cultural practice.

  7. Nuclear Safety Functions of ITER Gas Injection System Instrumentation and Control and the Concept Design

    NASA Astrophysics Data System (ADS)

    Yang, Yu; Maruyama, S.; Fossen, A.; Villers, F.; Kiss, G.; Zhang, Bo; Li, Bo; Jiang, Tao; Huang, Xiangmei

    2016-08-01

    The ITER Gas Injection System (GIS) plays an important role on fueling, wall conditioning and distribution for plasma operation. Besides that, to support the safety function of ITER, GIS needs to implement three nuclear safety Instrumentation and Control (I&C) functions. In this paper, these three functions are introduced with the emphasis on their latest safety classifications. The nuclear I&C design concept is briefly discussed at the end.

  8. Machine learning in motion control

    NASA Technical Reports Server (NTRS)

    Su, Renjeng; Kermiche, Noureddine

    1989-01-01

    The existing methodologies for robot programming originate primarily from robotic applications to manufacturing, where uncertainties of the robots and their task environment may be minimized by repeated off-line modeling and identification. In space application of robots, however, a higher degree of automation is required for robot programming because of the desire of minimizing the human intervention. We discuss a new paradigm of robotic programming which is based on the concept of machine learning. The goal is to let robots practice tasks by themselves and the operational data are used to automatically improve their motion performance. The underlying mathematical problem is to solve the problem of dynamical inverse by iterative methods. One of the key questions is how to ensure the convergence of the iterative process. There have been a few small steps taken into this important approach to robot programming. We give a representative result on the convergence problem.

  9. Iterative development of visual control systems in a research vivarium.

    PubMed

    Bassuk, James A; Washington, Ida M

    2014-01-01

    The goal of this study was to test the hypothesis that reintroduction of Continuous Performance Improvement (CPI) methodology, a lean approach to management at Seattle Children's (Hospital, Research Institute, Foundation), would facilitate engagement of vivarium employees in the development and sustainment of a daily management system and a work-in-process board. Such engagement was implemented through reintroduction of aspects of the Toyota Production System. Iterations of a Work-In-Process Board were generated using Shewhart's Plan-Do-Check-Act process improvement cycle. Specific attention was given to the importance of detecting and preventing errors through assessment of the following 5 levels of quality: Level 1, customer inspects; Level 2, company inspects; Level 3, work unit inspects; Level 4, self-inspection; Level 5, mistake proofing. A functioning iteration of a Mouse Cage Work-In-Process Board was eventually established using electronic data entry, an improvement that increased the quality level from 1 to 3 while reducing wasteful steps, handoffs and queues. A visual workplace was realized via a daily management system that included a Work-In-Process Board, a problem solving board and two Heijunka boards. One Heijunka board tracked cage changing as a function of a biological kanban, which was validated via ammonia levels. A 17% reduction in cage changing frequency provided vivarium staff with additional time to support Institute researchers in their mutual goal of advancing cures for pediatric diseases. Cage washing metrics demonstrated an improvement in the flow continuum in which a traditional batch and queue push system was replaced with a supermarket-type pull system. Staff engagement during the improvement process was challenging and is discussed. The collective data indicate that the hypothesis was found to be true. The reintroduction of CPI into daily work in the vivarium is consistent with the 4P Model of the Toyota Way and selected Principles

  10. Iterative Development of Visual Control Systems in a Research Vivarium

    PubMed Central

    Bassuk, James A.; Washington, Ida M.

    2014-01-01

    The goal of this study was to test the hypothesis that reintroduction of Continuous Performance Improvement (CPI) methodology, a lean approach to management at Seattle Children’s (Hospital, Research Institute, Foundation), would facilitate engagement of vivarium employees in the development and sustainment of a daily management system and a work-in-process board. Such engagement was implemented through reintroduction of aspects of the Toyota Production System. Iterations of a Work-In-Process Board were generated using Shewhart’s Plan-Do-Check-Act process improvement cycle. Specific attention was given to the importance of detecting and preventing errors through assessment of the following 5 levels of quality: Level 1, customer inspects; Level 2, company inspects; Level 3, work unit inspects; Level 4, self-inspection; Level 5, mistake proofing. A functioning iteration of a Mouse Cage Work-In-Process Board was eventually established using electronic data entry, an improvement that increased the quality level from 1 to 3 while reducing wasteful steps, handoffs and queues. A visual workplace was realized via a daily management system that included a Work-In-Process Board, a problem solving board and two Heijunka boards. One Heijunka board tracked cage changing as a function of a biological kanban, which was validated via ammonia levels. A 17% reduction in cage changing frequency provided vivarium staff with additional time to support Institute researchers in their mutual goal of advancing cures for pediatric diseases. Cage washing metrics demonstrated an improvement in the flow continuum in which a traditional batch and queue push system was replaced with a supermarket-type pull system. Staff engagement during the improvement process was challenging and is discussed. The collective data indicate that the hypothesis was found to be true. The reintroduction of CPI into daily work in the vivarium is consistent with the 4P Model of the Toyota Way and selected

  11. e-Learning Application for Machine Maintenance Process using Iterative Method in XYZ Company

    NASA Astrophysics Data System (ADS)

    Nurunisa, Suaidah; Kurniawati, Amelia; Pramuditya Soesanto, Rayinda; Yunan Kurnia Septo Hediyanto, Umar

    2016-02-01

    XYZ Company is a company based on manufacturing part for airplane, one of the machine that is categorized as key facility in the company is Millac 5H6P. As a key facility, the machines should be assured to work well and in peak condition, therefore, maintenance process is needed periodically. From the data gathering, it is known that there are lack of competency from the maintenance staff to maintain different type of machine which is not assigned by the supervisor, this indicate that knowledge which possessed by maintenance staff are uneven. The purpose of this research is to create knowledge-based e-learning application as a realization from externalization process in knowledge transfer process to maintain the machine. The application feature are adjusted for maintenance purpose using e-learning framework for maintenance process, the content of the application support multimedia for learning purpose. QFD is used in this research to understand the needs from user. The application is built using moodle with iterative method for software development cycle and UML Diagram. The result from this research is e-learning application as sharing knowledge media for maintenance staff in the company. From the test, it is known that the application make maintenance staff easy to understand the competencies.

  12. Learning Iteration-wise Generalized Shrinkage-Thresholding Operators for Blind Deconvolution.

    PubMed

    Wangmeng Zuo; Dongwei Ren; Zhang, David; Shuhang Gu; Lei Zhang

    2016-04-01

    Salient edge selection and time-varying regularization are two crucial techniques to guarantee the success of maximum a posteriori (MAP)-based blind deconvolution. However, the existing approaches usually rely on carefully designed regularizers and handcrafted parameter tuning to obtain satisfactory estimation of the blur kernel. Many regularizers exhibit the structure-preserving smoothing capability, but fail to enhance salient edges. In this paper, under the MAP framework, we propose the iteration-wise ℓp-norm regularizers together with data-driven strategy to address these issues. First, we extend the generalized shrinkage-thresholding (GST) operator for ℓp-norm minimization with negative p value, which can sharpen salient edges while suppressing trivial details. Then, the iteration-wise GST parameters are specified to allow dynamical salient edge selection and time-varying regularization. Finally, instead of handcrafted tuning, a principled discriminative learning approach is proposed to learn the iterationwise GST operators from the training dataset. Furthermore, the multi-scale scheme is developed to improve the efficiency of the algorithm. Experimental results show that, negative p value is more effective in estimating the coarse shape of blur kernel at the early stage, and the learned GST operators can be well generalized to other dataset and real world blurry images. Compared with the state-of-the-art methods, our method achieves better deblurring results in terms of both quantitative metrics and visual quality, and it is much faster than the state-of-the-art patch-based blind deconvolution method. PMID:26915121

  13. Procedural Learning during Declarative Control

    ERIC Educational Resources Information Center

    Crossley, Matthew J.; Ashby, F. Gregory

    2015-01-01

    There is now abundant evidence that human learning and memory are governed by multiple systems. As a result, research is now turning to the next question of how these putative systems interact. For instance, how is overall control of behavior coordinated, and does learning occur independently within systems regardless of what system is in control?…

  14. Multiagent reinforcement learning: spiking and nonspiking agents in the iterated Prisoner's Dilemma.

    PubMed

    Vassiliades, Vassilis; Cleanthous, Aristodemos; Christodoulou, Chris

    2011-04-01

    This paper investigates multiagent reinforcement learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory nature of these games makes their study in multiagent systems quite challenging. In particular, we investigate MARL with spiking and nonspiking agents in the Iterated Prisoner's Dilemma by exploring the conditions required to enhance its cooperative outcome. The spiking agents are neural networks with leaky integrate-and-fire neurons trained with two different learning algorithms: 1) reinforcement of stochastic synaptic transmission, or 2) reward-modulated spike-timing-dependent plasticity with eligibility trace. The nonspiking agents use a tabular representation and are trained with Q- and SARSA learning algorithms, with a novel reward transformation process also being applied to the Q-learning agents. According to the results, the cooperative outcome is enhanced by: 1) transformed internal reinforcement signals and a combination of a high learning rate and a low discount factor with an appropriate exploration schedule in the case of non-spiking agents, and 2) having longer eligibility trace time constant in the case of spiking agents. Moreover, it is shown that spiking and nonspiking agents have similar behavior and therefore they can equally well be used in a multiagent interaction setting. For training the spiking agents in the case where more than one output neuron competes for reinforcement, a novel and necessary modification that enhances competition is applied to the two learning algorithms utilized, in order to avoid a possible synaptic saturation. This is done by administering to the networks additional global reinforcement signals for every spike of the output neurons that were not "responsible" for the preceding decision. PMID:21421435

  15. Learning fuzzy logic control system

    NASA Technical Reports Server (NTRS)

    Lung, Leung Kam

    1994-01-01

    The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the

  16. Face super-resolution via multilayer locality-constrained iterative neighbor embedding and intermediate dictionary learning.

    PubMed

    Jiang, Junjun; Hu, Ruimin; Wang, Zhongyuan; Han, Zhen

    2014-10-01

    Based on the assumption that low-resolution (LR) and high-resolution (HR) manifolds are locally isometric, the neighbor embedding super-resolution algorithms try to preserve the geometry (reconstruction weights) of the LR space for the reconstructed HR space, but neglect the geometry of the original HR space. Due to the degradation process of the LR image (e.g., noisy, blurred, and down-sampled), the neighborhood relationship of the LR space cannot reflect the truth. To this end, this paper proposes a coarse-to-fine face super-resolution approach via a multilayer locality-constrained iterative neighbor embedding technique, which intends to represent the input LR patch while preserving the geometry of original HR space. In particular, we iteratively update the LR patch representation and the estimated HR patch, and meanwhile an intermediate dictionary learning scheme is employed to bridge the LR manifold and original HR manifold. The proposed method can faithfully capture the intrinsic image degradation shift and enhance the consistency between the reconstructed HR manifold and the original HR manifold. Experiments with application to face super-resolution on the CAS-PEAL-R1 database and real-world images demonstrate the power of the proposed algorithm. PMID:25134081

  17. Control of convergence in convective flow simulations using a fuzzy rule set that stabilizes iterative oscillations

    SciTech Connect

    Dragojlovic, Z.; Kaminski, D.A.; Ryoo, J.

    1999-07-01

    Under-relaxation in an iterative CFD solver is guided by fuzzy logic in order to achieve automatic convergence with minimum CPU time. The fuzzy logic set of rules determines the near-optimal relaxation factor during the execution of the code, based on information from a Fourier transform of a set of characteristic values. The control algorithm was tested on four benchmark problems: buoyancy driven flow in a square cavity, lid driven flow in a square enclosure, mixed convection over a backward facing step and Dean flow. The incompressible Newtonian conservation equations are solved by the SIMPLER algorithm with simple substitution. The relaxation factors for u and v velocities and temperatures area adjusted on each iteration using the fuzzy logic algorithm. Close to optimal convergence is achieved in each of the benchmark cases with nearly minimal number of iterations and CPU time.

  18. Single-Iteration Learning Algorithm for Feed-Forward Neural Networks

    SciTech Connect

    Barhen, J.; Cogswell, R.; Protopopescu, V.

    1999-07-31

    A new methodology for neural learning is presented, whereby only a single iteration is required to train a feed-forward network with near-optimal results. To this aim, a virtual input layer is added to the multi-layer architecture. The virtual input layer is connected to the nominal input layer by a specird nonlinear transfer function, and to the fwst hidden layer by regular (linear) synapses. A sequence of alternating direction singular vrdue decompositions is then used to determine precisely the inter-layer synaptic weights. This algorithm exploits the known separability of the linear (inter-layer propagation) and nonlinear (neuron activation) aspects of information &ansfer within a neural network.

  19. An Information-Based Learning Approach to Dual Control.

    PubMed

    Alpcan, Tansu; Shames, Iman

    2015-11-01

    Dual control aims to concurrently learn and control an unknown system. However, actively learning the system conflicts directly with any given control objective for it will disturb the system during exploration. This paper presents a receding horizon approach to dual control, where a multiobjective optimization problem is solved repeatedly and subject to constraints representing system dynamics. Balancing a standard finite-horizon control objective, a knowledge gain objective is defined to explicitly quantify the information acquired when learning the system dynamics. Measures from information theory, such as entropy-based uncertainty, Fisher information, and relative entropy, are studied and used to quantify the knowledge gained as a result of the control actions. The resulting iterative framework is applied to Markov decision processes and discrete-time nonlinear systems. Thus, the broad applicability and usefulness of the presented approach is demonstrated in diverse problem settings. The framework is illustrated with multiple numerical examples. PMID:25730828

  20. How to Combine Objectives and Methods of Evaluation in Iterative ILE Design: Lessons Learned from Designing Ambre-Add

    ERIC Educational Resources Information Center

    Nogry, S.; Jean-Daubias, S.; Guin, N.

    2012-01-01

    This article deals with evaluating an interactive learning environment (ILE) during the iterative-design process. Various aspects of the system must be assessed and a number of evaluation methods are available. In designing the ILE Ambre-add, several techniques were combined to test and refine the system. In particular, we point out the merits of…

  1. Foucauldian Iterative Learning Conversations--An Example of Organisational Change: Developing Conjoint-Work between EPS and Social Workers

    ERIC Educational Resources Information Center

    Apter, Brian

    2014-01-01

    An organisational change-process in a UK local authority (LA) over two years is examined using transcribed excerpts from three meetings. The change-process is analysed using a Foucauldian analytical tool--Iterative Learning Conversations (ILCS). An Educational Psychology Service was changed from being primarily an education-focussed…

  2. Co-Simulation Research of the Mechanical-Hydraulic-Control Coupling System of ITER Tractor

    NASA Astrophysics Data System (ADS)

    Yang, Xiuqing; Luo, Minzhou; Mei, Tao; Yao, Damao

    2009-06-01

    The virtual prototyping models of the mechanical, hydraulic and control system of the ITER tractor were built with CATIA, ADAMS and MATLAB/Simulink respectively according to its heavy load and high precision characteristics, and the data transfer between the different models was accomplished by the integration interface between different software. Consequently the virtual experimental platform for the multi-disciplinary co-simulation was established. A co-simulation study of the mechanical-hydraulic-control coupling system of the ITER tractor was carried out. The synchronization servo control of parallel hydraulic cylinders was implemented, and the tracking control of the preconcerted trajectory of the hydraulic cylinders was realized on the established experimental platform. This paper presents the optimization design and technology rebuilding for the complicated coupling system with its theoretic foundation and co-simulation virtual experimental platform.

  3. An iterative investigation into the implementation of handheld computers as learning tools in a science museum

    NASA Astrophysics Data System (ADS)

    Phipps, Molly E.

    In this study I discuss the state of Free-Choice Learning research, and an investigation into the use of personal ubiquitous technology on visitors' experiences at a science center. The three manuscripts included in this document: (1) Review published research on free-choice learning from 1997-2007 from selected journals (2) Examine visitors' interest in using handheld computers (iPods) for learning in a science museum, and report on refining protocols for this type of research. (3) Investigate the impact of using an iPod with supplementary videos on visitors use and understanding of an exhibit on scientific chaos. This study was approached in two phases, the first phase follows the principles of design research in exploring ways to present the iPods within the most favorable context to encourage learning. These changes were systematically implemented and their impact on visitors' experiences were documented. The second phase of the research focused on one particular exhibit and three accompanying videos on the iPod. This exhibit is well loved, but difficult to understand for visitors and docents alike. Through naturalistic inquiry and iterative open coding, I found visitors interpreted appropriate use of the exhibit in four distinct ways: HOW DOES IT WORK?, WAITING FOR THE SPLASH, INTERACTING, and RESTING. However, iPod users all interpreted appropriate use of the exhibit as HOW DOES IT WORK?. Careful observation of visitors' actions at the "Chaos Wheel" exhibit suggests that the exhibit needs some revision if it is to become more accessible to more visitors. The iPod represents one way to increase the accessibility of the exhibit, but other means should be explored.

  4. Iterative Refinement of Possibility Distributions by Learning for Pixel-Based Classification.

    PubMed

    Alsahwa, Bassem; Solaiman, Basel; Almouahed, Shaban; Bosse, Eloi; Gueriot, Didier

    2016-08-01

    This paper proposes an approach referred as: iterative refinement of possibility distributions by learning (IRPDL) for pixel-based image classification. The IRPDL approach is based on the use of possibilistic reasoning concepts exploiting expert knowledge sources as well as ground possibilistic seeds learning. The set of seeds is constructed by incrementally updating and refining the possibility distributions. Synthetic images as well as real images from the RIDER Breast MRI database are being used to evaluate the IRPDL performance. Its performance is compared with three relevant reference methods: region growing, semi-supervised fuzzy pattern matching, and Markov random fields. The IRDPL performance (in terms of recognition rate, 87.3%) is close to the Markovian method (88.8%) that is considered to be the reference in pixel-based image classification. IRPDL outperforms the other two methods, respectively, at the recognition rates of 83.9% and 84.7%. In addition, the proposed IRPDL requires fewer parameters for the mathematical representation and presents a reduced computational complexity. PMID:27305673

  5. Aircraft adaptive learning control

    NASA Technical Reports Server (NTRS)

    Lee, P. S. T.; Vanlandingham, H. F.

    1979-01-01

    The optimal control theory of stochastic linear systems is discussed in terms of the advantages of distributed-control systems, and the control of randomly-sampled systems. An optimal solution to longitudinal control is derived and applied to the F-8 DFBW aircraft. A randomly-sampled linear process model with additive process and noise is developed.

  6. Evaluating neoclassical tearing mode detection with ECE for control on ITER

    NASA Astrophysics Data System (ADS)

    van den Brand, H.; de Baar, M. R.; Lopes Cardozo, N. J.; Westerhof, E.

    2013-01-01

    Neoclassical tearing mode (NTM) control on ITER requires detection of the mode location to be accurate and with low latency. This paper presents a systematic way to evaluate mode detection algorithms for ITER using numerical simulations of electron cyclotron emission (ECE), taking into account the radial asymmetry in the temperature perturbation by a rotating magnetic island. Simulated ECE is detected using a synthetic radiometer, in the ITER equatorial port plug, and processed by two detection algorithms for the 2/1 and 3/2 NTMs for a burning H-mode ITER plasma. One of the algorithms also incorporates simulated Mirnov data. The video bandwidth is set at 2 kHz. This allows for intermediate frequency bandwidths of BIF = 400 MHz and BIF = 300 MHz for the two algorithms, respectively. The intermediate frequency bandwidth provides a trade-off between radial accuracy (low bandwidth) and low noise/latency (large bandwidth). 2/1 and 3/2 NTMs, seeded with widths up to 9 and 11 cm, are detectable with the required accuracy within 250 ms. With appropriate settings for the radiometer, the NTM detection using ECE is accurate and with low latency. The algorithm that incorporates both ECE and Mirnov data showed the lowest detection latencies.

  7. An iterative approach to the optimal co-design of linear control systems

    NASA Astrophysics Data System (ADS)

    Jiang, Yu; Wang, Yebin; Bortoff, Scott A.; Jiang, Zhong-Ping

    2016-04-01

    This paper investigates the optimal co-design of both physical plants and control policies for a class of continuous-time linear control systems. The optimal co-design of a specific linear control system is commonly formulated as a nonlinear non-convex optimisation problem (NNOP), and solved by using iterative techniques, where the plant parameters and the control policy are updated iteratively and alternately. This paper proposes a novel iterative approach to solve the NNOP, where the plant parameters are updated by solving a standard semi-definite programming problem, with non-convexity no longer involved. The proposed system design is generally less conservative in terms of the system performance compared to the conventional system-equivalence-based design, albeit the range of applicability is slightly reduced. A practical optimisation algorithm is proposed to compute a sub-optimal solution ensuring the system stability, and the convergence of the algorithm is established. The effectiveness of the proposed algorithm is illustrated by its application to the optimal co-design of a physical load positioning system.

  8. Iterated non-linear model predictive control based on tubes and contractive constraints.

    PubMed

    Murillo, M; Sánchez, G; Giovanini, L

    2016-05-01

    This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle. PMID:26850752

  9. Tension control of space tether via online quasi-linearization iterations

    NASA Astrophysics Data System (ADS)

    Wen, Hao; Zhu, Zheng H.; Jin, Dongping; Hu, Haiyan

    2016-02-01

    The paper presents how to stabilize the deployment and retrieval processes of a space tether system via the tension control, where the model predictive control is exploited to optimize the control performance while the nonlinear dynamics and tension constraint are explicitly taken into account. A new scheme of online quasi-linearization iteration is proposed to transfer the nonlinear optimal control problem into a series of linear optimal control problems that can be solved in sequence at a series of sampling instants. Consequently, it avoids the complete solution of the nonlinear optimal control problem at each sampling interval such that the computational load can be greatly alleviated. Furthermore, the scheme extends the conventional quasi-linearization schemes by distributing the iterative process across sampling instants and online updating the initial condition of the linear optimal control problem. The problems of linear optimal control are discretized using a pseudo-spectral algorithm and then solved by a solver of linear quadratic programming. Numerical case studies indicate that successful deployment and retrieval of the system can be achieved using the proposed control scheme without violating the positive tension constraint. The time cost for each online optimization in the proposed scheme is on the order of 10 ms and far below the sampling interval under consideration.

  10. Decentralized Control of Sound Radiation from an Aircraft-Style Panel Using Iterative Loop Recovery

    NASA Technical Reports Server (NTRS)

    Schiller, Noah H.; Cabell, Randolph H.; Fuller, Chris R.

    2008-01-01

    A decentralized LQG-based control strategy is designed to reduce low-frequency sound transmission through periodically stiffened panels. While modern control strategies have been used to reduce sound radiation from relatively simple structural acoustic systems, significant implementation issues have to be addressed before these control strategies can be extended to large systems such as the fuselage of an aircraft. For instance, centralized approaches typically require a high level of connectivity and are computationally intensive, while decentralized strategies face stability problems caused by the unmodeled interaction between neighboring control units. Since accurate uncertainty bounds are not known a priori, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is validated using real-time control experiments performed on a built-up aluminum test structure representative of the fuselage of an aircraft. Experiments demonstrate that the iterative approach is capable of achieving 12 dB peak reductions and a 3.6 dB integrated reduction in radiated sound power from the stiffened panel.

  11. Towards Current Profile Control in ITER: Potential Approaches and Research Needs

    NASA Astrophysics Data System (ADS)

    Schuster, E.; Barton, J. E.; Wehner, W. P.

    2014-10-01

    Many challenging plasma control problems still need to be addressed in order for the ITER Plasma Control System (PCS) to be able to successfully achieve the ITER project goals. For instance, setting up a suitable toroidal current density profile is key for one possible advanced scenario characterized by noninductive sustainment of the plasma current and steady-state operation. The nonlinearity and high dimensionality exhibited by the plasma demand a model-based current-profile control synthesis procedure that can accommodate this complexity through embedding the known physics within the design. The development of a model capturing the dynamics of the plasma relevant for control design enables not only the design of feedback controllers for regulation or tracking but also the design of optimal feedforward controllers for a systematic model-based approach to scenario planning, the design of state estimators for a reliable real-time reconstruction of the plasma internal profiles based on limited and noisy diagnostics, and the development of a fast predictive simulation code for closed-loop performance evaluation before implementation. Progress towards control-oriented modeling of the current profile evolution and associated control design has been reported following both data-driven and first-principles-driven approaches. An overview of these two approaches will be provided, as well as a discussion on research needs associated with each one of the model applications described above. Supported by the US Department of Energy under DE-SC0001334 and DE-SC0010661.

  12. Current Control in ITER Steady State Plasmas With Neutral Beam Steering

    SciTech Connect

    R.V. Budny

    2009-09-10

    Predictions of quasi steady state DT plasmas in ITER are generated using the PTRANSP code. The plasma temperatures, densities, boundary shape, and total current (9 - 10 MA) anticipated for ITER steady state plasmas are specified. Current drive by negative ion neutral beam injection, lower-hybrid, and electron cyclotron resonance are calculated. Four modes of operation with different combinations of current drive are studied. For each mode, scans with the NNBI aimed at differing heights in the plasma are performed to study effects of current control on the q profile. The timeevolution of the currents and q are calculated to evaluate long duration transients. Quasi steady state, strongly reversed q profiles are predicted for some beam injection angles if the current drive and bootstrap currents are sufficiently large.

  13. Tuberculosis control learning games.

    PubMed

    Smith, I

    1993-07-01

    In teaching health workers about tuberculosis (TB) control we frequently concentrate on the technological aspects, such as diagnosis, treatment and recording. Health workers also need to understand the sociological aspects of TB control, particularly those that influence the likelihood of diagnosis and cure. Two games are presented that help health workers comprehend the reasons why TB patients often delay in presenting for diagnosis, and why they then frequently default from treatment. PMID:8356734

  14. Spine detection in CT and MR using iterated marginal space learning.

    PubMed

    Michael Kelm, B; Wels, Michael; Kevin Zhou, S; Seifert, Sascha; Suehling, Michael; Zheng, Yefeng; Comaniciu, Dorin

    2013-12-01

    Examinations of the spinal column with both, Magnetic Resonance (MR) imaging and Computed Tomography (CT), often require a precise three-dimensional positioning, angulation and labeling of the spinal disks and the vertebrae. A fully automatic and robust approach is a prerequisite for an automated scan alignment as well as for the segmentation and analysis of spinal disks and vertebral bodies in Computer Aided Diagnosis (CAD) applications. In this article, we present a novel method that combines Marginal Space Learning (MSL), a recently introduced concept for efficient discriminative object detection, with a generative anatomical network that incorporates relative pose information for the detection of multiple objects. It is used to simultaneously detect and label the spinal disks. While a novel iterative version of MSL is used to quickly generate candidate detections comprising position, orientation, and scale of the disks with high sensitivity, the anatomical network selects the most likely candidates using a learned prior on the individual nine dimensional transformation spaces. Finally, we propose an optional case-adaptive segmentation approach that allows to segment the spinal disks and vertebrae in MR and CT respectively. Since the proposed approaches are learning-based, they can be trained for MR or CT alike. Experimental results based on 42 MR and 30 CT volumes show that our system not only achieves superior accuracy but also is among the fastest systems of its kind in the literature. On the MR data set the spinal disks of a whole spine are detected in 11.5s on average with 98.6% sensitivity and 0.073 false positive detections per volume. On the CT data a comparable sensitivity of 98.0% with 0.267 false positives is achieved. Detected disks are localized with an average position error of 2.4 mm/3.2 mm and angular error of 3.9°/4.5° in MR/CT, which is close to the employed hypothesis resolution of 2.1 mm and 3.3°. PMID:23265800

  15. A Tale of Two Chambers: Iterative Approaches and Lessons Learned from Life Support Systems Testing in Altitude Chambers

    NASA Technical Reports Server (NTRS)

    Callini, Gianluca

    2016-01-01

    The drive for the journey to Mars is in a higher gear than ever before. We are developing new spacecraft and life support systems to take humans to the Red Planet. The journey that development hardware takes before its final incarnation in a fully integrated spacecraft can take years, as is the case for the Orion environmental control and life support system (ECLSS). Through the Pressure Integrated Suit Test (PIST) series, NASA personnel at Johnson Space Center have been characterizing the behavior of a closed loop ECLSS in the event of cabin depressurization. This kind of testing - one of the most hazardous activities performed at JSC - requires an iterative approach, increasing in complexity and hazards). The PIST series, conducted in the Crew and Thermal Systems Division (CTSD) 11-ft Chamber, started with unmanned test precursors before moving to a human-in-the-loop phase, and continues to evolve with the eventual goal of a qualification test for the final system that will be installed on Orion. Meanwhile, the Human Exploration Spacecraft Testbed for Integration and Advancement (HESTIA) program is an effort to research and develop technologies that will work in concert to support habitation on Mars. September 2015 marked the first unmanned HESTIA test, with the goal of characterizing how ECLSS technologies work together in a closed environment. HESTIA will culminate in crewed testing, but it can benefit from the lessons learned from another test that is farther ahead in its development and life cycle. Discussing PIST and HESTIA, this paper illustrates how we approach testing, the kind of information that facility teams need to ensure efficient collaborations and successful testing, and how we can apply what we learn to execute future tests.

  16. eNOSHA, a Free, Open and Flexible Learning Object Repository--An Iterative Development Process for Global User-Friendliness

    ERIC Educational Resources Information Center

    Mozelius, Peter; Hettiarachchi, Enosha

    2012-01-01

    This paper describes the iterative development process of a Learning Object Repository (LOR), named eNOSHA. Discussions on a project for a LOR started at the e-Learning Centre (eLC) at The University of Colombo, School of Computing (UCSC) in 2007. The eLC has during the last decade been developing learning content for a nationwide e-learning…

  17. Metacognitive Control and Optimal Learning

    ERIC Educational Resources Information Center

    Son, Lisa K.; Sethi, Rajiv

    2006-01-01

    The notion of optimality is often invoked informally in the literature on metacognitive control. We provide a precise formulation of the optimization problem and show that optimal time allocation strategies depend critically on certain characteristics of the learning environment, such as the extent of time pressure, and the nature of the uptake…

  18. Performance analysis of model based iterative reconstruction with dictionary learning in transportation security CT

    NASA Astrophysics Data System (ADS)

    Haneda, Eri; Luo, Jiajia; Can, Ali; Ramani, Sathish; Fu, Lin; De Man, Bruno

    2016-05-01

    In this study, we implement and compare model based iterative reconstruction (MBIR) with dictionary learning (DL) over MBIR with pairwise pixel-difference regularization, in the context of transportation security. DL is a technique of sparse signal representation using an over complete dictionary which has provided promising results in image processing applications including denoising,1 as well as medical CT reconstruction.2 It has been previously reported that DL produces promising results in terms of noise reduction and preservation of structural details, especially for low dose and few-view CT acquisitions.2 A distinguishing feature of transportation security CT is that scanned baggage may contain items with a wide range of material densities. While medical CT typically scans soft tissues, blood with and without contrast agents, and bones, luggage typically contains more high density materials (i.e. metals and glass), which can produce severe distortions such as metal streaking artifacts. Important factors of security CT are the emphasis on image quality such as resolution, contrast, noise level, and CT number accuracy for target detection. While MBIR has shown exemplary performance in the trade-off of noise reduction and resolution preservation, we demonstrate that DL may further improve this trade-off. In this study, we used the KSVD-based DL3 combined with the MBIR cost-minimization framework and compared results to Filtered Back Projection (FBP) and MBIR with pairwise pixel-difference regularization. We performed a parameter analysis to show the image quality impact of each parameter. We also investigated few-view CT acquisitions where DL can show an additional advantage relative to pairwise pixel difference regularization.

  19. Dual-Dictionary Learning-Based Iterative Image Reconstruction for Spectral Computed Tomography Application

    PubMed Central

    Zhao, Bo; Ding, Huanjun; Lu, Yang; Wang, Ge; Zhao, Jun; Molloi, Sabee

    2015-01-01

    In this study, we investigated the effectiveness of a novel Iterative Reconstruction (IR) method coupled with Dual-Dictionary Learning (DDL) for image reconstruction in a dedicated breast Computed Tomography (CT) system based on a Cadmium-Zinc-Telluride (CZT) photon-counting detector and compared it to the Filtered-Back-Projection (FBP) method with the ultimate goal of reducing the number of projections necessary for reconstruction without sacrificing image quality. Postmortem breast samples were scanned in a fan-beam CT system and were reconstructed from 100–600 projections with both IR and FBP methods. The Contrast-to-Noise Ratio (CNR) between the glandular and adipose tissues of the postmortem breast samples was calculated to compare the quality of images reconstructed from IR and FBP. The spatial resolution of the two reconstruction techniques was evaluated using Aluminum (Al) wires with diameters of 643, 813, 1020, 1290 and 1630 µm in a plastic epoxy resin phantom with diameter of 13 cm. Both the spatial resolution and the CNR were improved with IR compared to FBP for the images reconstructed from the same number of projections. In comparison with FBP reconstruction, the CNR was improved from 3.4 to 7.5 by using the IR method with 6-fold fewer projections while maintaining the same spatial resolution. The study demonstrated that the IR method coupled with DDL could significantly reduce the required number of projections for a CT reconstruction compared to FBP method while achieving a much better CNR and maintaining the same spatial resolution. From this, the radiation dose and scanning time can potentially be reduced by a factor of approximately 6 by using this IR method for image reconstruction in a CZT-based breast CT system. PMID:23192234

  20. Exploring the knowledge behind predictions in everyday cognition: an iterated learning study.

    PubMed

    Stephens, Rachel G; Dunn, John C; Rao, Li-Lin; Li, Shu

    2015-10-01

    Making accurate predictions about events is an important but difficult task. Recent work suggests that people are adept at this task, making predictions that reflect surprisingly accurate knowledge of the distributions of real quantities. Across three experiments, we used an iterated learning procedure to explore the basis of this knowledge: to what extent is domain experience critical to accurate predictions and how accurate are people when faced with unfamiliar domains? In Experiment 1, two groups of participants, one resident in Australia, the other in China, predicted the values of quantities familiar to both (movie run-times), unfamiliar to both (the lengths of Pharaoh reigns), and familiar to one but unfamiliar to the other (cake baking durations and the lengths of Beijing bus routes). While predictions from both groups were reasonably accurate overall, predictions were inaccurate in the selectively unfamiliar domains and, surprisingly, predictions by the China-resident group were also inaccurate for a highly familiar domain: local bus route lengths. Focusing on bus routes, two follow-up experiments with Australia-resident groups clarified the knowledge and strategies that people draw upon, plus important determinants of accurate predictions. For unfamiliar domains, people appear to rely on extrapolating from (not simply directly applying) related knowledge. However, we show that people's predictions are subject to two sources of error: in the estimation of quantities in a familiar domain and extension to plausible values in an unfamiliar domain. We propose that the key to successful predictions is not simply domain experience itself, but explicit experience of relevant quantities. PMID:25837024

  1. Dual-dictionary learning-based iterative image reconstruction for spectral computed tomography application

    NASA Astrophysics Data System (ADS)

    Zhao, Bo; Ding, Huanjun; Lu, Yang; Wang, Ge; Zhao, Jun; Molloi, Sabee

    2012-12-01

    In this study, we investigated the effectiveness of a novel iterative reconstruction (IR) method coupled with dual-dictionary learning (DDL) for image reconstruction in a dedicated breast computed tomography (CT) system based on a cadmium-zinc-telluride (CZT) photon-counting detector and compared it to the filtered-back-projection (FBP) method with the ultimate goal of reducing the number of projections necessary for reconstruction without sacrificing the image quality. Postmortem breast samples were scanned in a fan-beam CT system and were reconstructed from 100 to 600 projections with both IR and FBP methods. The contrast-to-noise ratio (CNR) between the glandular and adipose tissues of the postmortem breast samples was calculated to compare the quality of images reconstructed from IR and FBP. The spatial resolution of the two reconstruction techniques was evaluated using aluminum (Al) wires with diameters of 643, 813, 1020, 1290 and 1630 µm in a plastic epoxy resin phantom with a diameter of 13 cm. Both the spatial resolution and the CNR were improved with IR compared to FBP for the images reconstructed from the same number of projections. In comparison with FBP reconstruction, the CNR was improved from 3.4 to 7.5 by using the IR method with six-fold fewer projections while maintaining the same spatial resolution. The study demonstrated that the IR method coupled with DDL could significantly reduce the required number of projections for a CT reconstruction compared to the FBP method while achieving a much better CNR and maintaining the same spatial resolution. From this, the radiation dose and scanning time can potentially be reduced by a factor of approximately 6 by using this IR method for image reconstruction in a CZT-based breast CT system.

  2. Performance improvement of robots using a learning control scheme

    NASA Technical Reports Server (NTRS)

    Krishna, Ramuhalli; Chiang, Pen-Tai; Yang, Jackson C. S.

    1987-01-01

    Many applications of robots require that the same task be repeated a number of times. In such applications, the errors associated with one cycle are also repeated every cycle of the operation. An off-line learning control scheme is used here to modify the command function which would result in smaller errors in the next operation. The learning scheme is based on a knowledge of the errors and error rates associated with each cycle. Necessary conditions for the iterative scheme to converge to zero errors are derived analytically considering a second order servosystem model. Computer simulations show that the errors are reduced at a faster rate if the error rate is included in the iteration scheme. The results also indicate that the scheme may increase the magnitude of errors if the rate information is not included in the iteration scheme. Modification of the command input using a phase and gain adjustment is also proposed to reduce the errors with one attempt. The scheme is then applied to a computer model of a robot system similar to PUMA 560. Improved performance of the robot is shown by considering various cases of trajectory tracing. The scheme can be successfully used to improve the performance of actual robots within the limitations of the repeatability and noise characteristics of the robot.

  3. An Application of Fictitious Reference Iterative Tuning to State Feedback Control

    NASA Astrophysics Data System (ADS)

    Matsui, Yoshihiro; Akamatsu, Shunichi; Kimura, Tomohiko; Nakano, Kazushi; Sakurama, Kazunori

    In this paper, an application method of Fictitious Reference Iterative Tuning (FRIT), which has been developed for controller gain tuning for single-input single-output systems, to state feedback gain tuning for single-input multivariable systems is proposed. Transient response data of a single-input multivariable plant obtained under closed-loop operation is used for model matching by the FRIT in time domain. The data is also used in frequency domain to estimate the stability and to improve the control performance of the closed-loop system with the state feedback gain tuned by the method. The method is applied to a state feedback control system for an inverted pendulum with an inertia rotor and its usefulness is illustrated through experiments.

  4. The Policy Iteration Algorithm for Average Continuous Control of Piecewise Deterministic Markov Processes

    SciTech Connect

    Costa, O. L. V.; Dufour, F.

    2010-10-15

    The main goal of this paper is to apply the so-called policy iteration algorithm (PIA) for the long run average continuous control problem of piecewise deterministic Markov processes (PDMP's) taking values in a general Borel space and with compact action space depending on the state variable. In order to do that we first derive some important properties for a pseudo-Poisson equation associated to the problem. In the sequence it is shown that the convergence of the PIA to a solution satisfying the optimality equation holds under some classical hypotheses and that this optimal solution yields to an optimal control strategy for the average control problem for the continuous-time PDMP in a feedback form.

  5. Online Supplementary ADP Learning Controller Design and Application to Power System Frequency Control With Large-Scale Wind Energy Integration.

    PubMed

    Guo, Wentao; Liu, Feng; Si, Jennie; He, Dawei; Harley, Ronald; Mei, Shengwei

    2016-08-01

    The emergence of smart grids has posed great challenges to traditional power system control given the multitude of new risk factors. This paper proposes an online supplementary learning controller (OSLC) design method to compensate the traditional power system controllers for coping with the dynamic power grid. The proposed OSLC is a supplementary controller based on approximate dynamic programming, which works alongside an existing power system controller. By introducing an action-dependent cost function as the optimization objective, the proposed OSLC is a nonidentifier-based method to provide an online optimal control adaptively as measurement data become available. The online learning of the OSLC enjoys the policy-search efficiency during policy iteration and the data efficiency of the least squares method. For the proposed OSLC, the stability of the controlled system during learning, the monotonic nature of the performance measure of the iterative supplementary controller, and the convergence of the iterative supplementary controller are proved. Furthermore, the efficacy of the proposed OSLC is demonstrated in a challenging power system frequency control problem in the presence of high penetration of wind generation. PMID:26087500

  6. Online learning control by association and reinforcement.

    PubMed

    Si, J; Wang, Y T

    2001-01-01

    This paper focuses on a systematic treatment for developing a generic online learning control system based on the fundamental principle of reinforcement learning or more specifically neural dynamic programming. This online learning system improves its performance over time in two aspects: 1) it learns from its own mistakes through the reinforcement signal from the external environment and tries to reinforce its action to improve future performance; and 2) system states associated with the positive reinforcement is memorized through a network learning process where in the future, similar states will be more positively associated with a control action leading to a positive reinforcement. A successful candidate of online learning control design is introduced. Real-time learning algorithms is derived for individual components in the learning system. Some analytical insight is provided to give guidelines on the learning process took place in each module of the online learning control system. PMID:18244383

  7. Real-time sawtooth control and neoclassical tearing mode preemption in ITER

    SciTech Connect

    Kim, D. Goodman, T. P.; Sauter, O.

    2014-06-15

    Real-time control of multiple plasma actuators is a requirement in advanced tokamaks; for example, for burn control, plasma current profile control and MHD stabilization—electron cyclotron (EC) wave absorption is ideally suited especially for the latter. On ITER, 24 EC sources can be switched between 56 inputs at the torus. In the torus, 5 launchers direct the power to various locations across the plasma profile via 11 steerable mirrors. For optimal usage of the available power, the aiming and polarization of the beams must be adapted to the plasma configuration and the needs of the scenario. Since the EC system performs many competing tasks, present day systems should demonstrate the ability of an EC plant to deal with several targets in parallel and/or to switch smoothly between goals to attain overall satisfaction. Based on pacing and locking experiments performed on TCV (Tokamak à Configuration Variable), the real-time sawtooth control of ITER with this complex set of actuators is analyzed, as an example. It is shown that sawtooth locking and pacing are possible with various levels of powers, leading to different time delays between the end of the EC power phase and the next sawtooth crash. This timing is important since it allows use of the same launchers for neoclassical tearing mode (NTM) preemption at the q = 1.5 or 2 surface, avoiding the need to switch power between launchers. These options are presented. It is also demonstrated that increasing the total EC power does not necessarily increase the range of control because of the geometry of the launchers.

  8. Real-time sawtooth control and neoclassical tearing mode preemption in ITER

    NASA Astrophysics Data System (ADS)

    Kim, D.; Goodman, T. P.; Sauter, O.

    2014-06-01

    Real-time control of multiple plasma actuators is a requirement in advanced tokamaks; for example, for burn control, plasma current profile control and MHD stabilization—electron cyclotron (EC) wave absorption is ideally suited especially for the latter. On ITER, 24 EC sources can be switched between 56 inputs at the torus. In the torus, 5 launchers direct the power to various locations across the plasma profile via 11 steerable mirrors. For optimal usage of the available power, the aiming and polarization of the beams must be adapted to the plasma configuration and the needs of the scenario. Since the EC system performs many competing tasks, present day systems should demonstrate the ability of an EC plant to deal with several targets in parallel and/or to switch smoothly between goals to attain overall satisfaction. Based on pacing and locking experiments performed on TCV (Tokamak à Configuration Variable), the real-time sawtooth control of ITER with this complex set of actuators is analyzed, as an example. It is shown that sawtooth locking and pacing are possible with various levels of powers, leading to different time delays between the end of the EC power phase and the next sawtooth crash. This timing is important since it allows use of the same launchers for neoclassical tearing mode (NTM) preemption at the q = 1.5 or 2 surface, avoiding the need to switch power between launchers. These options are presented. It is also demonstrated that increasing the total EC power does not necessarily increase the range of control because of the geometry of the launchers.

  9. 17th Workshop on MHD Stability Control: addressing the disruption challenge for ITER

    NASA Astrophysics Data System (ADS)

    Buttery, Richard

    2013-08-01

    This annual workshop on magnetohydrodynamic stability control was held on 5-7 November 2012 at Columbia University in the city of New York, in the aftermath of a violent hydrodynamic instability event termed 'Hurricane Sandy'. Despite these challenging circumstances, Columbia University managed an excellent meeting, enabling the full participation of the community. This Workshop has been held since 1996 to help in the development of understanding and control of magnetohydrodynamic (MHD) instabilities for future fusion reactors. It covers a wide range of stability topics—from disruptions, to tearing modes, error fields, edge-localized modes (ELMs), resistive wall modes (RWMs) and ideal MHD—spanning many device types (tokamaks, stellarators and reversed field pinches) to identify commonalities in the physics and a means of control. The theme for 2012 was 'addressing the disruption challenge for ITER', and thus the first day had a heavy focus on both the avoidance and mitigation of disruptions in ITER. Key elements included understanding how to apply 3D fields to maintain stability, as well as managing the disruption process itself through mitigating loads in the thermal quench and handling so called 'runaway electrons'. This culminated in a panel discussion on the disruption mitigation strategy for ITER, which noted that heat load asymmetries during the thermal quench appear to be an artifact of MHD processes, and that runaway electron generation may be inevitable, suggesting research should focus on control and dissipation of the runaway beam. The workshop was combined this year with the annual US-Japan MHD Workshop, with a special section looking more deeply at 'Fundamentals of 3D Perturbed Equilibrium Control', with interesting sessions on 3D equilibrium reconstruction, RWM physics, novel control concepts such as non-magnetic sensing, adaptive control, q < 2 tokamak operation, and the effects of flow. The final day turned to tearing mode interactions

  10. Simultaneous Updating of Model and Controller Based on Fictitious Reference Iterative Tuning

    NASA Astrophysics Data System (ADS)

    Kaneko, Osamu; Miyachi, Makoto; Fujii, Takao

    In this paper, we provide a new method for updating a mathematical model of a plant and a controller, simultaneously, by using only one-shot experimental data. Here, we propose a fictitious controller which is described by the following triple: a nominal model of the plant, an initial controller designed for the nominal model, and a parameterized model of the plant. In addition, we introduce a cost function which involves the fictitious controller, the nominal model, and the actual experimental data of the closed loop with the initial controller. Then, for this very reason of the minimization of the introduced cost function, we show that utilizing the fictitious reference iterative tuning, which was proposed by the authors, enables us to obtain both a more accurate model and a more desirable controller than the nominal model and the initial controller, respectively. We also give quantitative evaluation of the difference between the nominal model and the updated one, and that between the initial controller and the updated one. Finally, we give examples in order to illustrate the validity of the proposed method.

  11. A thioesterase from an iterative fungal polyketide synthase shows macrocylization and cross-coupling activity, and may play a role in controlling iterative cycling through product off loading†

    PubMed Central

    Wang, Meng; Zhou, Hui; Wirz, Monica; Tang, Yi; Boddy, Christopher N.

    2009-01-01

    Zearalenone, a fungal macrocyclic polyketide, is a member of the resorcylic acid lactone family. Herein, we characterize in vitro the thioesterase from PKS13 in zearalenone biosynthesis (Zea TE). The excised Zea TE catalyzes macrocyclization of a linear thioester activated model of zearalenone. Zea TE also catalyzes the cross coupling of a benzoyl thioester with alcohols and amines. Kinetic characterization of the cross coupling is consistent with a ping-pong bi-bi mechanism, confirming an acyl-enzyme intermediate. Finally, the substrate specificity of the Zea TE indicates the TE may help control iterative cycling on PKS13 by rapidly off loading the final resorcylate containing product. PMID:19530704

  12. Examination of the Entry to Burn and Burn Control for the ITER 15 MA Baseline and Other Scenarios

    SciTech Connect

    Kesse, Charles E.; Kim, S-H.; Koechl, F.

    2014-09-01

    The entry to burn and flattop burn control in ITER will be a critical need from the first DT experiments. Simulations are used to address time-dependent behavior under a range of possible conditions that include injected power level, impurity content (W, Ar, Be), density evolution, H-mode regimes, controlled parameter (Wth, Pnet, Pfusion), and actuator (Paux, fueling, fAr), with a range of transport models. A number of physics issues at the L-H transition require better understanding to project to ITER, however, simulations indicate viable control with sufficient auxiliary power (up to 73 MW), while lower powers become marginal (as low as 43 MW).

  13. Integrated modelling of island growth, stabilization and mode locking: consequences for NTM control on ITER

    NASA Astrophysics Data System (ADS)

    van den Brand, H.; de Baar, M. R.; Lopes Cardozo, N. J.; Westerhof, E.

    2012-09-01

    Full suppression of neoclassical tearing modes (NTMs) using electron cyclotron current drive (ECCD) should be reached before mode locking (stop of rotation) makes suppression impossible. For an ITER scenario 2 plasma, the similar time scales for locking and island growth necessitate the combined modelling of the growth of the mode and its slow down due to wall induced drag. Using such a model, the maximum allowed latency between the seeding of the mode and the start of ECCD deposition and maximum deviation in the radial position are determined. The maximum allowed latency is determined for two limiting models for island growth; the polarization model with wmarg = 2 cm, representing the worst case, and the transport model with wmarg = 6 cm, representing the best case. NTMs with seed island widths up to 9.5 cm and 12 cm for the 2/1 and the 3/2 NTM, respectively, are suppressible. The maximum allowed latency is 1.05 s and 2.95 s for the 2/1 and 3/2 NTM, respectively, for the worst case model. Radial misalignment should not exceed 7-10 mm for the 2/1 NTM and 5-16 mm for the 3/2 NTM depending on the model for island growth. As long as the alignment suffices, it does not reduce the maximum allowed latency. Mode locking has serious implications for any real-time NTM control system on ITER that aims to suppress NTMs by ECCD.

  14. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

    PubMed Central

    Heffernan, Rhys; Paliwal, Kuldip; Lyons, James; Dehzangi, Abdollah; Sharma, Alok; Wang, Jihua; Sattar, Abdul; Yang, Yuedong; Zhou, Yaoqi

    2015-01-01

    Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking. PMID:26098304

  15. Joule heating of the ITER TF cold structure: Effects of vertical control coil currents and ELMS

    SciTech Connect

    Radovinsky, A.; Pillsbury, R.D. Jr.

    1993-11-09

    The toroidal field coil and support structures for ITER are maintained at cryogenic temperatures. The time-varying currents in the poloidal field coil system will induce eddy currents in these structures. The associated Joule dissipation will cause local heating and require heat removal which will show up as a load on the cryogenic system. Studies of Joule heating of the ITER TF cold structure (TFCS) due to the currents in the poloidal field coil system are presented. The two regimes considered in this study are the plasma vertical stability control and the Edge Loss Mode (ELM) events. The 3-D, thin-shell, eddy current program, EDDYCUFF was used to analyze the eddy currents and Joule losses in the cold structure. The current versus time scenarios were defined. Four control coil options were studied. All schemes use coils external to the TF cold structure. Analyses of power depositions during the plasma vertical stability control were performed for each of the four options. For each of these options three different recovery times were assumed. The times were 3, 1, and 1/3 seconds. Sets of four sequential ELMs, as well as isolated ELMs have been studied for various sets of active PF coils. The results showed that the lowest average power dissipation in the TF cold structure occurs when a subset of PF2 and PF7 are active, and all the other PF coils are passive. The general conclusion is that to minimize power dissipation in the TF cold structure it is preferable that only coils PF2 and PF7 are active. The other coils (PF3-PF6) should be passive and driven by a condition of constant flux. It is recommended in particular, that coils PF3 and PF5 be allowed to change currents to conserve flux, since they provide the maximum shielding of the TFCS from the fields caused by the active coils.

  16. Learning to Teach Elementary Science through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

    ERIC Educational Resources Information Center

    Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

    2015-01-01

    Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves…

  17. On the Sequential Control of ITER Poloidal Field Converters for Reactive Power Reduction

    NASA Astrophysics Data System (ADS)

    Yuan, Hongwen; Fu, Peng; Gao, Ge; Huang, Liansheng; Song, Zhiquan; He, Shiying; Wu, Yanan; Dong, Lin; Wang, Min; Fang, Tongzhen

    2014-12-01

    Sequential control applied to the International Thermonuclear Experimental Reactor (ITER) poloidal field converter system for the purpose of reactive power reduction is the subject of this investigation. Due to the inherent characteristics of thyristor-based phase-controlled converter, the poloidal field converter system consumes a huge amount of reactive power from the grid, which subsequently results in a voltage drop at the 66 kV busbar if no measure is taken. The installation of a static var compensator rated for 750 MVar at the 66 kV busbar is an essential way to compensate reactive power to the grid, which is the most effective measure to solve the problem. However, sequential control of the multi-series converters provides an additional method to improve the natural power factor and thus alleviate the pressure of reactive power demand of the converter system without any additional cost. In the present paper, by comparing with the symmetrical control technique, the advantage of sequential control in reactive power consumption is highlighted. Simulation results based on SIMULINK are found in agreement with the theoretical analysis.

  18. Nonlinear Control and Online Optimization of the Burn Condition in ITER

    NASA Astrophysics Data System (ADS)

    Schuster, Eugenio; Boyer, Mark D.; Pajares-Martinez, Andres

    2015-11-01

    Regulation of the fusion power through modulation of fueling, external heating sources and non-axisymmetric magnetic fields, referred to as burn control, is one of the fundamental problems in burning plasma research. Active control will be essential for achieving and maintaining desired operating points, responding to changing power demands, and ensuring stable operation in ITER. A volume-averaged nonlinear model for the evolutions of the density of energy, deuterium and tritium fuel ions, alpha-particles, and impurity ions has been used to synthesize a multi-variable nonlinear burn control strategy that can reject large perturbations and move between operating points. The control approach makes use of the different possible actuators for altering the fusion power, including auxiliary heating sources, isotopic fueling, in-vessel coils, and impurity injection. Adaptive control techniques are used to account for uncertainty in model parameters, including particle confinement times and recycling rates. Furthermore, a model-based constrained optimization scheme is proposed to drive the system as close as possible to desired fusion power and temperature references. Supported by the US DOE under DE-SC0010661.

  19. Non-iterative adaptive time stepping with truncation error control for simulating variable-density flow

    NASA Astrophysics Data System (ADS)

    Hirthe, E. M.; Graf, T.

    2012-04-01

    Fluid density variations occur due to changes in the solute concentration, temperature and pressure of groundwater. Examples are interaction between freshwater and seawater, radioactive waste disposal, groundwater contamination, and geothermal energy production. The physical coupling between flow and transport introduces non-linearity in the governing mathematical equations, such that solving variable-density flow problems typically requires very long computational time. Computational efficiency can be attained through the use of adaptive time-stepping schemes. The aim of this work is therefore to apply a non-iterative adaptive time-stepping scheme based on local truncation error in variable-density flow problems. That new scheme is implemented into the code of the HydroGeoSphere model (Therrien et al., 2011). The new time-stepping scheme is applied to the Elder (1967) and the Shikaze et al. (1998) problem of free convection in porous and fractured-porous media, respectively. Numerical simulations demonstrate that non-iterative time-stepping based on local truncation error control fully automates the time step size and efficiently limits the temporal discretization error to the user-defined tolerance. Results of the Elder problem show that the new time-stepping scheme presented here is significantly more efficient than uniform time-stepping when high accuracy is required. Results of the Shikaze problem reveal that the new scheme is considerably faster than conventional time-stepping where time step sizes are either constant or controlled by absolute head/concentration changes. Future research will focus on the application of the new time-stepping scheme to variable-density flow in complex real-world fractured-porous rock.

  20. Analysis of Options for Resistive Wall Mode Control Coils for ITER

    NASA Astrophysics Data System (ADS)

    Ulrickson, M.

    2006-10-01

    Several fusion devices have found improvement in plasma performance from the application of either static or dynamic magnetic perturbations from a set of coils. DIII-D has found that static fields can prevent formation of locked modes and create ergodic structures in the plasma edge that decrease the size of ELMS. They have also used such coils in a feedback loop to control the growth of resistive wall modes. Similar effects have been observed on NSTX, C-Mod, ASDEX, and JET. In all cases, the coils were placed close to the plasma either inside the vessel or immediately outside a thin vessel. Because ITER is a burning plasma device with a long pulse length, thick nuclear shielding must be placed between the plasma and the vacuum vessel. If ITER is to realize the confinement and operation benefits of resistive wall mode control coils, locations and coil designs must be found where such coils can be deployed. Two generic locations have been identified. The most accessible location is immediately outside the vessel and around the mid-plane ports. An alternative location closer to the plasma is inside the mid-plane ports but behind the port shield module. We have used an electromagnetic modeling code to evaluate both the static and dynamic field perturbations at the plasma edge for both of these coil options for frequencies from 1 Hz to 6kHz. *Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

  1. SVM-Hustle - An iterative semi-supervised machine learning approach for pairwise protein remote homology detection

    SciTech Connect

    Shah, Anuj R.; Oehmen, Chris S.; Webb-Robertson, Bobbie-Jo M.

    2008-03-15

    Motivation: As the amount of biological sequence data continues to grow exponentially we face the increasing challenge of assigning function to this enormous molecular ‘parts list’. The most popular approaches to this challenge make use of the simplifying assumption that similar functional molecules, or proteins, sometimes have similar composition, or sequence. However, these algorithms often fail to identify remote homologs (proteins with similar function but dissimilar sequence) which often are a significant fraction of the total homolog collection for a given sequence. We introduce a Support Vector Machine (SVM)-based tool to detect Homology Using Semisupervised iTerative LEarning (SVM-HUSTLE) that detects significantly more remote homologs than current state-of-the-art sequence or cluster-based methods. As opposed to building profiles or position specific scoring matrices, SVM-HUSTLE builds an SVM classifier for a query sequence by training on a collection of representative highconfidence training sets. SVM-HUSTLE combines principles of semi-supervised learning theory with statistical sampling to create many concurrent classifiers to iteratively detect and refine on-the-fly patterns indicating homology. Results: When compared against existing methods for identifying protein homologs (BLASTp, PSI-BLAST, RANKPROP, MOTIFPROP and their variants) on the SCOP 1.59 benchmark dataset consisting of 7329 protein sequences, SVM-HUSTLE significantly outperforms each of the above methods using the most stringent ROC1 statistic with p-values less than 1e-20.

  2. Vector fuzzy control iterative algorithm for the design of sub-wavelength diffractive optical elements for beam shaping

    NASA Astrophysics Data System (ADS)

    Lin, Yong; Hu, Jiasheng; Wu, Kenan

    2009-08-01

    The vector fuzzy control iterative algorithm (VFCIA) is proposed for the design of phase-only sub-wavelength diffractive optical elements (SWDOEs) for beam shaping. The vector diffraction model put forward by Mansuripur is applied to relate the field distributions between the SWDOE plane and the output plane. Fuzzy control theory is used to decide the constraint method for each iterative process of the algorithm. We have designed a SWDOE that transforms a circular flat-top beam to a square irradiance pattern. Computer design results show that the SWDOE designed by the VFCIA can produce better results than the vector iterative algorithm (VIA). And the finite difference time-domain method (FDTD), a rigorous electromagnetic analysis technique, is used to analyze the designed SWDOE for further confirming the validity of the proposed method.

  3. Cognitive control: a role for implicit learning?

    PubMed

    Deroost, Natacha; Vandenbossche, Jochen; Zeischka, Peter; Coomans, Daphné; Soetens, Eric

    2012-09-01

    We investigated the influence of implicit learning on cognitive control. In a sequential Stroop task, participants implicitly learned a sequence placed on the color of the Stroop words. In Experiment 1, Stroop conflict was lower in sequenced than in random trials (learning-improved control). However, as these results were derived from an interaction between learning and conflict, they could also be explained by improved implicit learning (difference between random and sequenced trials), under incongruent compared with congruent trials (control-improved learning). Therefore, we further unraveled the direction of the interaction in 2 additional experiments. In Experiment 2, participants who learned the color sequence were no better at resolving conflict than participants who did not undergo sequence training. This shows that implicit knowledge does not directly reduce conflict (no learning-improved control). In Experiment 3, the amount of conflict did not directly improve learning either (no control-improved learning). However, conflict had a significant impact on the expression of implicit learning, as most knowledge was expressed under the highest amount of conflict. Thus, task-optimization was accomplished by an increased reliance on implicit sequence knowledge under high conflict. These findings demonstrate that implicit learning processes can be flexibly recruited to support cognitive control functions. PMID:22428719

  4. Optimal control of polymer flooding based on mixed-integer iterative dynamic programming

    NASA Astrophysics Data System (ADS)

    Lei, Yang; Li, Shurong; Zhang, Xiaodong; Zhang, Qiang; Guo, Lanlei

    2011-11-01

    Polymer flooding is one of the most important technologies for enhanced oil recovery. In this article, a mixed-integer optimal control model of distributed parameter systems (DPS) for the injection strategies is established, which involves the performance index as maximum of the profit, the governing equations as the fluid flow equations of polymer flooding and some inequalities constraints, such as polymer concentration and injection amount limitation. The control variables are the volume size, the injection concentration of each slug and the terminal flooding time. For the constant injection rate, the slug size is determined by the integer time stage length, and thus the integer variables are introduced in the DPS. To cope with the optimal control problem (OCP) of this DPS, a mixed-integer iterative dynamic programming incorporating a special truncation procedure to handle integer restrictions on stage lengths is proposed. First, the OCP with variable time stage lengths is transformed into a fixed time stage problem by introducing a normalised time variable. Then, the optimisation procedure is carried out at each stage and preceded backwards in a systematic way. Finally, the numerical results of an example illustrate the effectiveness of the proposed method.

  5. Learning arm's posture control using reinforcement learning and feedback-error-learning.

    PubMed

    Kambara, H; Kim, J; Sato, M; Koike, Y

    2004-01-01

    In this paper, we propose a learning model using the Actor-Critic method and the feedback-error-learning scheme. The Actor-Critic method, which is one of the major frameworks in reinforcement learning, has attracted attention as a computational learning model in the basal ganglia. Meanwhile, the feedback-error-learning is learning architecture proposed as a computationally coherent model of cerebellar motor learning. This learning architecture's purpose is to acquire a feed-forward controller by using a feedback controller's output as an error signal. In past researches, a predetermined constant gain feedback controller was used for the feedback-error-learning. We use the Actor-Critic method for obtaining a feedback controller in the feedback-error-earning. By applying the proposed learning model to an arm's posture control, we show that high-performance feedback and feed-forward controller can be acquired from only by using a scalar value of reward. PMID:17271719

  6. Open-loop control of SCExAO's MEMS deformable mirror using the Fast Iterative Algorithm: speckle control performances

    NASA Astrophysics Data System (ADS)

    Blain, Célia; Guyon, Olivier; Martinache, Frantz; Bradley, Colin; Clergeon, Christophe

    2012-07-01

    Micro-Electro-Mechanical Systems (MEMS) deformable mirrors (DMs) are widely utilized in astronomical Adaptive Optics (AO) instrumentation. High precision open-loop control of MEMS DMs has been achieved by developing a high accuracy DM model, the Fast Iterative Algorithm (FIA), a physics-based model allowing precise control of the DM shape. Accurate open-loop control is particularly critical for the wavefront control of High- Contrast Imaging (HCI) instruments to create a dark hole area free of most slow and quasi-static speckles which remain the limiting factor for direct detection and imaging of exoplanets. The Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) system is one of these high contrast imaging instruments and uses a 1024-actuator MEMS deformable mirror (DM) both in closed-loop and open-loop. The DM is used to modulate speckles in order to distinguish (i) speckles due to static and slow-varying residual aberrations from (ii) speckles due to genuine structures, such as exoplanets. The FIA has been fully integrated into the SCExAO wavefront control software and we report the FIA’s performance for the control of speckles in the focal plane.

  7. Fast and automatic depth control of iterative bone ablation based on optical coherence tomography data

    NASA Astrophysics Data System (ADS)

    Fuchs, Alexander; Pengel, Steffen; Bergmeier, Jan; Kahrs, Lüder A.; Ortmaier, Tobias

    2015-07-01

    Laser surgery is an established clinical procedure in dental applications, soft tissue ablation, and ophthalmology. The presented experimental set-up for closed-loop control of laser bone ablation addresses a feedback system and enables safe ablation towards anatomical structures that usually would have high risk of damage. This study is based on combined working volumes of optical coherence tomography (OCT) and Er:YAG cutting laser. High level of automation in fast image data processing and tissue treatment enables reproducible results and shortens the time in the operating room. For registration of the two coordinate systems a cross-like incision is ablated with the Er:YAG laser and segmented with OCT in three distances. The resulting Er:YAG coordinate system is reconstructed. A parameter list defines multiple sets of laser parameters including discrete and specific ablation rates as ablation model. The control algorithm uses this model to plan corrective laser paths for each set of laser parameters and dynamically adapts the distance of the laser focus. With this iterative control cycle consisting of image processing, path planning, ablation, and moistening of tissue the target geometry and desired depth are approximated until no further corrective laser paths can be set. The achieved depth stays within the tolerances of the parameter set with the smallest ablation rate. Specimen trials with fresh porcine bone have been conducted to prove the functionality of the developed concept. Flat bottom surfaces and sharp edges of the outline without visual signs of thermal damage verify the feasibility of automated, OCT controlled laser bone ablation with minimal process time.

  8. An adaptive learning control system for aircraft

    NASA Technical Reports Server (NTRS)

    Mekel, R.; Nachmias, S.

    1976-01-01

    A learning control system is developed which blends the gain scheduling and adaptive control into a single learning system that has the advantages of both. An important feature of the developed learning control system is its capability to adjust the gain schedule in a prescribed manner to account for changing aircraft operating characteristics. Furthermore, if tests performed by the criteria of the learning system preclude any possible change in the gain schedule, then the overall system becomes an ordinary gain scheduling system. Examples are discussed.

  9. A robust line search for learning control

    SciTech Connect

    Driessen, B.J.; Kwok, K.S.; Sadegh, N.

    1998-11-01

    In this paper a new line search for a Newton Rhapson learning control algorithm is presented. Theorems and rigorous proofs of its increased robustness over existing line searches are provided, and numerical examples are used to further validate the theorems. Also, the previously posed open question of whether robust optimal trajectory learning is possible is also addressed. It is shown that the answer is generally no, at least for gradient-based learning control algorithms.

  10. Linear System Control Using Stochastic Learning Automata

    NASA Technical Reports Server (NTRS)

    Ziyad, Nigel; Cox, E. Lucien; Chouikha, Mohamed F.

    1998-01-01

    This paper explains the use of a Stochastic Learning Automata (SLA) to control switching between three systems to produce the desired output response. The SLA learns the optimal choice of the damping ratio for each system to achieve a desired result. We show that the SLA can learn these states for the control of an unknown system with the proper choice of the error criteria. The results of using a single automaton are compared to using multiple automata.

  11. Methods for control over learning individual trajectory

    NASA Astrophysics Data System (ADS)

    Mitsel, A. A.; Cherniaeva, N. V.

    2015-09-01

    The article discusses models, methods and algorithms of determining student's optimal individual educational trajectory. A new method of controlling the learning trajectory has been developed as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects.

  12. Vehicle Steering control: A model of learning

    NASA Technical Reports Server (NTRS)

    Smiley, A.; Reid, L.; Fraser, M.

    1978-01-01

    A hierarchy of strategies were postulated to describe the process of learning steering control. Vehicle motion and steering control data were recorded for twelve novices who drove an instrumented car twice a week during and after a driver training course. Car-driver describing functions were calculated, the probable control structure determined, and the driver-alone transfer function modelled. The data suggested that the largest changes in steering control with learning were in the way the driver used the lateral position cue.

  13. Indirect learning control for nonlinear dynamical systems

    NASA Technical Reports Server (NTRS)

    Ryu, Yeong Soon; Longman, Richard W.

    1993-01-01

    In a previous paper, learning control algorithms were developed based on adaptive control ideas for linear time variant systems. The learning control methods were shown to have certain advantages over their adaptive control counterparts, such as the ability to produce zero tracking error in time varying systems, and the ability to eliminate repetitive disturbances. In recent years, certain adaptive control algorithms have been developed for multi-body dynamic systems such as robots, with global guaranteed convergence to zero tracking error for the nonlinear system euations. In this paper we study the relationship between such adaptive control methods designed for this specific class of nonlinear systems, and the learning control problem for such systems, seeking to converge to zero tracking error in following a specific command repeatedly, starting from the same initial conditions each time. The extension of these methods from the adaptive control problem to the learning control problem is seen to be trivial. The advantages and disadvantages of using learning control based on such adaptive control concepts for nonlinear systems, and the use of other currently available learning control algorithms are discussed.

  14. Circuit model of the ITER-like antenna for JET and simulation of its control algorithms

    NASA Astrophysics Data System (ADS)

    Durodié, Frédéric; Dumortier, Pierre; Helou, Walid; Křivská, Alena; Lerche, Ernesto

    2015-12-01

    The ITER-like Antenna (ILA) for JET [1] is a 2 toroidal by 2 poloidal array of Resonant Double Loops (RDL) featuring in-vessel matching capacitors feeding RF current straps in conjugate-T manner, a low impedance quarter-wave impedance transformer, a service stub allowing hydraulic actuator and water cooling services to reach the aforementioned capacitors and a 2nd stage phase-shifter-stub matching circuit allowing to correct/choose the conjugate-T working impedance. Toroidally adjacent RDLs are fed from a 3dB hybrid splitter. It has been operated at 33, 42 and 47MHz on plasma (2008-2009) while it presently estimated frequency range is from 29 to 49MHz. At the time of the design (2001-2004) as well as the experiments the circuit models of the ILA were quite basic. The ILA front face and strap array Topica model was relatively crude and failed to correctly represent the poloidal central septum, Faraday Screen attachment as well as the segmented antenna central septum limiter. The ILA matching capacitors, T-junction, Vacuum Transmission Line (VTL) and Service Stubs were represented by lumped circuit elements and simple transmission line models. The assessment of the ILA results carried out to decide on the repair of the ILA identified that achieving routine full array operation requires a better understanding of the RF circuit, a feedback control algorithm for the 2nd stage matching as well as tighter calibrations of RF measurements. The paper presents the progress in modelling of the ILA comprising a more detailed Topica model of the front face for various plasma Scrape Off Layer profiles, a comprehensive HFSS model of the matching capacitors including internal bellows and electrode cylinders, 3D-EM models of the VTL including vacuum ceramic window, Service stub, a transmission line model of the 2nd stage matching circuit and main transmission lines including the 3dB hybrid splitters. A time evolving simulation using the improved circuit model allowed to design and

  15. Circuit model of the ITER-like antenna for JET and simulation of its control algorithms

    SciTech Connect

    Durodié, Frédéric Křivská, Alena; Helou, Walid; Collaboration: EUROfusion Consortium

    2015-12-10

    The ITER-like Antenna (ILA) for JET [1] is a 2 toroidal by 2 poloidal array of Resonant Double Loops (RDL) featuring in-vessel matching capacitors feeding RF current straps in conjugate-T manner, a low impedance quarter-wave impedance transformer, a service stub allowing hydraulic actuator and water cooling services to reach the aforementioned capacitors and a 2nd stage phase-shifter-stub matching circuit allowing to correct/choose the conjugate-T working impedance. Toroidally adjacent RDLs are fed from a 3dB hybrid splitter. It has been operated at 33, 42 and 47MHz on plasma (2008-2009) while it presently estimated frequency range is from 29 to 49MHz. At the time of the design (2001-2004) as well as the experiments the circuit models of the ILA were quite basic. The ILA front face and strap array Topica model was relatively crude and failed to correctly represent the poloidal central septum, Faraday Screen attachment as well as the segmented antenna central septum limiter. The ILA matching capacitors, T-junction, Vacuum Transmission Line (VTL) and Service Stubs were represented by lumped circuit elements and simple transmission line models. The assessment of the ILA results carried out to decide on the repair of the ILA identified that achieving routine full array operation requires a better understanding of the RF circuit, a feedback control algorithm for the 2nd stage matching as well as tighter calibrations of RF measurements. The paper presents the progress in modelling of the ILA comprising a more detailed Topica model of the front face for various plasma Scrape Off Layer profiles, a comprehensive HFSS model of the matching capacitors including internal bellows and electrode cylinders, 3D-EM models of the VTL including vacuum ceramic window, Service stub, a transmission line model of the 2nd stage matching circuit and main transmission lines including the 3dB hybrid splitters. A time evolving simulation using the improved circuit model allowed to design and

  16. The Iterative Design of a Mobile Learning Application to Support Scientific Inquiry

    ERIC Educational Resources Information Center

    Marty, Paul F.; Mendenhall, Anne; Douglas, Ian; Southerland, Sherry A.; Sampson, Victor; Kazmer, Michelle M.; Alemanne, Nicole; Clark, Amanda; Schellinger, Jennifer

    2013-01-01

    The ubiquity of mobile devices makes them well suited for field-based learning experiences that require students to gather data as part of the process of developing scientific inquiry practices. The usefulness of these devices, however, is strongly influenced by the nature of the applications students use to collect data in the field. To…

  17. Iterative tyrosine phosphorylation controls non-canonical domain utilization in Crk.

    PubMed

    Sriram, G; Jankowski, W; Kasikara, C; Reichman, C; Saleh, T; Nguyen, K-Q; Li, J; Hornbeck, P; Machida, K; Liu, T; Li, H; Kalodimos, C G; Birge, R B

    2015-08-01

    Crk, the prototypical member of a class of Src homology-2 (SH2) and Src homology-3 (SH3) domain containing proteins that controls the coordinated assembly of signaling complexes, is regulated by phosphorylation of Y221 in the linker region, which forms an intramolecular SH2-pY221 auto-clamp to interrupt SH2-N-terminal SH3 domain (SH3N) signaling. Here, we show using LC-MS/MS and by generating phospho-specific antibodies that, iteratively with Y221, the Crk C-terminal SH3 domain (SH3C) is routinely phosphorylated on Y239 and/or Y251 by several extracellular stimuli known to engage Crk. Although phosphorylation at Y221 auto-inhibits the Crk SH2, phosphorylation of the SH3C generates an unconventional phosphoSH3C-SH3N unit in which the SH3N is fully functional to bind polyproline type II ligands and the phosphoSH3C binds de novo to other SH2 domains. Using high-throughput SH2 domain profiling, artificial neural network and position-specific scoring matrix-based bioinformatics approaches, and unbiased mass spectometry, we found that the phosphoSH3C binds several SH2 domain containing proteins, including specific non-receptor tyrosine kinases-Abl via pY251 and C-terminal Src kinase via pY239. Functionally, we show that the phosphoSH3C modulates the Abl-mediated phenotypes of cell spreading and motility. Together, these studies describe a versatile mechanism wherein phosphorylation of Crk at Y221 is not an off switch but redirects signaling from the SH2-SH3N axis to a phosphoSH3C-SH3N axis, with the SH3N as a common denominator. PMID:25381819

  18. Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.

    PubMed

    Zhao, Xiaowei; Ning, Qiao; Chai, Haiting; Ma, Zhiqiang

    2015-06-01

    As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/. PMID:25843215

  19. Learning styles: The learning methods of air traffic control students

    NASA Astrophysics Data System (ADS)

    Jackson, Dontae L.

    In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.

  20. Version Control in Project-Based Learning

    ERIC Educational Resources Information Center

    Milentijevic, Ivan; Ciric, Vladimir; Vojinovic, Oliver

    2008-01-01

    This paper deals with the development of a generalized model for version control systems application as a support in a range of project-based learning methods. The model is given as UML sequence diagram and described in detail. The proposed model encompasses a wide range of different project-based learning approaches by assigning a supervisory…

  1. Feedback Error Learning in neuromotor control

    NASA Astrophysics Data System (ADS)

    Ishihara, Abraham K.

    This thesis is concerned with adaptive human motor control. Adaptation is a highly desirable characteristic of any biological system. Failure is an undesirable, yet very real, characteristic of the human motor control systems. Variability is a ubiquitous observation in human movements that has no direct analogue in the design and analysis of robotic control algorithms. This thesis attempts to link these three aspects of motor control under the constraints of a biologically inspired control framework termed Feedback Error Learning (FEL). Utilizing nonlinear and adaptive control methods we prove conditions for which the FEL framework is stable and successful learning can occur. Utilizing singular perturbation methods, we derive conditions for which the system is guaranteed to fail. Variability is analyzed using Ito Calculus and stochastic Lyapunov functionals where signal dependent noise, a commonly observed phenomenon, enters in the learning algorithm. We also show how signal dependent noise might benefit biological control systems despite the inherent variability introduced into the motor control loops. Lastly, we investigate a force tracking control task, where subjects are asked to track a time-varying plant. Using basic control and system identification techniques, we probe the human motor learning system and extract learning rates with respect to the FEL model.

  2. Grounding cognitive control in associative learning.

    PubMed

    Abrahamse, Elger; Braem, Senne; Notebaert, Wim; Verguts, Tom

    2016-07-01

    Cognitive control covers a broad range of cognitive functions, but its research and theories typically remain tied to a single domain. Here we outline and review an associative learning perspective on cognitive control in which control emerges from associative networks containing perceptual, motor, and goal representations. Our review identifies 3 trending research themes that are shared between the domains of conflict adaptation, task switching, response inhibition, and attentional control: Cognitive control is context-specific, can operate in the absence of awareness, and is modulated by reward. As these research themes can be envisaged as key characteristics of learning, we propose that their joint emergence across domains is not coincidental but rather reflects a (latent) growth of interest in learning-based control. Associative learning has the potential for providing broad-scaled integration to cognitive control theory, and offers a promising avenue for understanding cognitive control as a self-regulating system without postulating an ill-defined set of homunculi. We discuss novel predictions, theoretical implications, and immediate challenges that accompany an associative learning perspective on cognitive control. (PsycINFO Database Record PMID:27148628

  3. Non-iterative adaptive time-stepping scheme with temporal truncation error control for simulating variable-density flow

    NASA Astrophysics Data System (ADS)

    Hirthe, Eugenia M.; Graf, Thomas

    2012-12-01

    The automatic non-iterative second-order time-stepping scheme based on the temporal truncation error proposed by Kavetski et al. [Kavetski D, Binning P, Sloan SW. Non-iterative time-stepping schemes with adaptive truncation error control for the solution of Richards equation. Water Resour Res 2002;38(10):1211, http://dx.doi.org/10.1029/2001WR000720.] is implemented into the code of the HydroGeoSphere model. This time-stepping scheme is applied for the first time to the low-Rayleigh-number thermal Elder problem of free convection in porous media [van Reeuwijk M, Mathias SA, Simmons CT, Ward JD. Insights from a pseudospectral approach to the Elder problem. Water Resour Res 2009;45:W04416, http://dx.doi.org/10.1029/2008WR007421.], and to the solutal [Shikaze SG, Sudicky EA, Schwartz FW. Density-dependent solute transport in discretely-fractured geological media: is prediction possible? J Contam Hydrol 1998;34:273-91] problem of free convection in fractured-porous media. Numerical simulations demonstrate that the proposed scheme efficiently limits the temporal truncation error to a user-defined tolerance by controlling the time-step size. The non-iterative second-order time-stepping scheme can be applied to (i) thermal and solutal variable-density flow problems, (ii) linear and non-linear density functions, and (iii) problems including porous and fractured-porous media.

  4. Refining fuzzy logic controllers with machine learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1994-01-01

    In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

  5. Learning to Control Advanced Life Support Systems

    NASA Technical Reports Server (NTRS)

    Subramanian, Devika

    2004-01-01

    Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for

  6. A learning controller for nonrepetitive robotic operation

    NASA Technical Reports Server (NTRS)

    Miller, W. T., III

    1987-01-01

    A practical learning control system is described which is applicable to complex robotic and telerobotic systems involving multiple feedback sensors and multiple command variables. In the controller, the learning algorithm is used to learn to reproduce the nonlinear relationship between the sensor outputs and the system command variables over particular regions of the system state space, rather than learning the actuator commands required to perform a specific task. The learned information is used to predict the command signals required to produce desired changes in the sensor outputs. The desired sensor output changes may result from automatic trajectory planning or may be derived from interactive input from a human operator. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables. The algorithm is well suited for real time implementation, requiring only fixed point addition and logical operations. The results of learning experiments using a General Electric P-5 manipulator interfaced to a VAX-11/730 computer are presented. These experiments involved interactive operator control, via joysticks, of the position and orientation of an object in the field of view of a video camera mounted on the end of the robot arm.

  7. Discrete time learning control in nonlinear systems

    NASA Technical Reports Server (NTRS)

    Longman, Richard W.; Chang, Chi-Kuang; Phan, Minh

    1992-01-01

    In this paper digital learning control methods are developed primarily for use in single-input, single-output nonlinear dynamic systems. Conditions for convergence of the basic form of learning control based on integral control concepts are given, and shown to be satisfied by a large class of nonlinear problems. It is shown that it is not the gross nonlinearities of the differential equations that matter in the convergence, but rather the much smaller nonlinearities that can manifest themselves during the short time interval of one sample time. New algorithms are developed that eliminate restrictions on the size of the learning gain, and on knowledge of the appropriate sign of the learning gain, for convergence to zero error in tracking a feasible desired output trajectory. It is shown that one of the new algorithms can give guaranteed convergence in the presence of actuator saturation constraints, and indicate when the requested trajectory is beyond the actuator capabilities.

  8. An adaptive learning control system for aircraft

    NASA Technical Reports Server (NTRS)

    Mekel, R.; Nachmias, S.

    1978-01-01

    A learning control system and its utilization as a flight control system for F-8 Digital Fly-By-Wire (DFBW) research aircraft is studied. The system has the ability to adjust a gain schedule to account for changing plant characteristics and to improve its performance and the plant's performance in the course of its own operation. Three subsystems are detailed: (1) the information acquisition subsystem which identifies the plant's parameters at a given operating condition; (2) the learning algorithm subsystem which relates the identified parameters to predetermined analytical expressions describing the behavior of the parameters over a range of operating conditions; and (3) the memory and control process subsystem which consists of the collection of updated coefficients (memory) and the derived control laws. Simulation experiments indicate that the learning control system is effective in compensating for parameter variations caused by changes in flight conditions.

  9. Nearly data-based optimal control for linear discrete model-free systems with delays via reinforcement learning

    NASA Astrophysics Data System (ADS)

    Zhang, Jilie; Zhang, Huaguang; Wang, Binrui; Cai, Tiaoyang

    2016-05-01

    In this paper, a nearly data-based optimal control scheme is proposed for linear discrete model-free systems with delays. The nearly optimal control can be obtained using only measured input/output data from systems, by reinforcement learning technology, which combines Q-learning with value iterative algorithm. First, we construct a state estimator by using the measured input/output data. Second, the quadratic functional is used to approximate the value function at each point in the state space, and the data-based control is designed by Q-learning method using the obtained state estimator. Then, the paper states the method, that is, how to solve the optimal inner kernel matrix ? in the least-square sense, by value iteration algorithm. Finally, the numerical examples are given to illustrate the effectiveness of our approach.

  10. Fictitious Reference Iterative Tuning for Non-Minimum Phase Systems in the IMC Architecture: Simultaneous Attainment of Controllers and Models

    NASA Astrophysics Data System (ADS)

    Kaneko, Osamu; Nguyen, Hien Thi; Wadagaki, Yusuke; Yamamoto, Shigeru

    This paper provides a practical and meaningful application of controller parameter tuning. Here, we propose a simultaneous attainment of a desired controller and a mathematical model of a plant by utilizing the fictitious reference iterative tuning (FRIT), which is a useful method of controller parameter tuning with only one-shot experimental data, in the internal model control (IMC) architecture. Particularly, this paper focuses on systems with unstable zeros which cannot be eliminated in many applications. We explain how the utilization of the FRIT is effective for obtaining not only the desired control parameter values but also an appropriate mathematical model of the plant. In order to show the effectiveness and the validity of the proposed method, we give illustrative examples.

  11. Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

    NASA Astrophysics Data System (ADS)

    Wei, Qing-Lai; Song, Rui-Zhuo; Sun, Qiu-Ye; Xiao, Wen-Dong

    2015-09-01

    This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton-Jacobi-Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method. Project supported by the National Natural Science Foundation of China (Grant Nos. 61304079 and 61374105), the Beijing Natural Science Foundation, China (Grant Nos. 4132078 and 4143065), the China Postdoctoral Science Foundation (Grant No. 2013M530527), the Fundamental Research Funds for the Central Universities, China (Grant No. FRF-TP-14-119A2), and the Open Research Project from State Key Laboratory of Management and Control for Complex Systems, China (Grant No. 20150104).

  12. Physics-based control-oriented modeling and robust feedback control of the plasma safety factor profile and stored energy dynamics in ITER

    NASA Astrophysics Data System (ADS)

    Barton, Justin E.; Besseghir, Karim; Lister, Jo; Schuster, Eugenio

    2015-11-01

    Many challenging plasma control problems still need to be addressed in order for the ITER plasma control system (PCS) to be able to maintain the plasma within a predefined operational space and optimize the plasma state evolution in the tokamak, which will greatly aid in the successful achievement of ITER’s goals. Firstly in this work, a general control-oriented, physics-based modeling approach is developed to obtain first-principles-driven (FPD) models of the plasma magnetic profile and stored energy evolutions valid for high performance, high confinement (H-mode) scenarios, with the goal of developing model-based closed-loop algorithms to control the safety factor profile (q profile) and stored energy evolutions in the tokamak. The FPD model is tailored to H-mode burning plasma scenarios in ITER by employing the DINA-CH & CRONOS free-boundary tokamak simulation code, and the FPD model’s prediction capabilities are demonstrated by comparing the prediction to data obtained from DINA-CH & CRONOS. Secondly, a model-based feedback control algorithm is designed to simultaneously track target q profile and stored energy evolutions in H-mode burning plasma scenarios in ITER by embedding the developed FPD model of the magnetic profile evolution into the control design process. The feedback controller is designed to ensure that the closed-loop system is robust to uncertainties in the electron density, electron temperature and plasma resistivity, and is tested in simulations with the developed FPD model. The effectiveness of the controller is demonstrated by first tracking nominal q profile and stored energy target evolutions, and then modulating the generated fusion power while maintaining the q profile in a stationary condition. In the process, many key practical issues for plasma profile control in ITER are investigated, which will be useful for the development of the ITER PCS that has recently been initiated. Some of the more pertinent investigated issues are the

  13. Enhanced Confinement Scenarios Without Large Edge Localized Modes in Tokamaks: Control, Performance, and Extrapolability Issues for ITER

    SciTech Connect

    Maingi, R

    2014-07-01

    Large edge localized modes (ELMs) typically accompany good H-mode confinement in fusion devices, but can present problems for plasma facing components because of high transient heat loads. Here the range of techniques for ELM control deployed in fusion devices is reviewed. The two baseline strategies in the ITER baseline design are emphasized: rapid ELM triggering and peak heat flux control via pellet injection, and the use of magnetic perturbations to suppress or mitigate ELMs. While both of these techniques are moderately well developed, with reasonable physical bases for projecting to ITER, differing observations between multiple devices are also discussed to highlight the needed community R & D. In addition, recent progress in ELM-free regimes, namely Quiescent H-mode, I-mode, and Enhanced Pedestal H-mode is reviewed, and open questions for extrapolability are discussed. Finally progress and outstanding issues in alternate ELM control techniques are reviewed: supersonic molecular beam injection, edge electron cyclotron heating, lower hybrid heating and/or current drive, controlled periodic jogs of the vertical centroid position, ELM pace-making via periodic magnetic perturbations, ELM elimination with lithium wall conditioning, and naturally occurring small ELM regimes.

  14. Effect of plasma response on the fast ion losses due to ELM control coils in ITER

    NASA Astrophysics Data System (ADS)

    Varje, Jari; Asunta, Otto; Cavinato, Mario; Gagliardi, Mario; Hirvijoki, Eero; Koskela, Tuomas; Kurki-Suonio, Taina; Liu, Yueqiang; Parail, Vassili; Saibene, Gabriella; Sipilä, Seppo; Snicker, Antti; Särkimäki, Konsta; Äkäslompolo, Simppa

    2016-04-01

    Mitigating edge localized modes (ELMs) with resonant magnetic perturbations (RMPs) can increase energetic particle losses and resulting wall loads, which have previously been studied in the vacuum approximation. This paper presents recent results of fusion alpha and NBI ion losses in the ITER baseline scenario modelled with the Monte Carlo orbit following code ASCOT in a realistic magnetic field including the effect of the plasma response. The response was found to reduce alpha particle losses but increase NBI losses, with up to 4.2% of the injected power being lost. Additionally, some of the load in the divertor was found to be shifted away from the target plates toward the divertor dome.

  15. Predictive and reinforcement learning for magneto-hydrodynamic control of hypersonic flows

    NASA Astrophysics Data System (ADS)

    Kulkarni, Nilesh Vijay

    Increasing needs for autonomy in future aerospace systems and immense progress in computing technology have motivated the development of on-line adaptive control techniques to account for modeling errors, changes in system dynamics, and faults occurring during system operation. After extensive treatment of the inner-loop adaptive control dealing mainly with stable adaptation towards desired transient behavior, adaptive optimal control has started receiving attention in literature. Motivated by the problem of optimal control of the magneto-hydrodynamic (MHD) generator at the inlet of the scramjet engine of a hypersonic flight vehicle, this thesis treats the general problem of efficiently combining off-line and on-line optimal control methods. The predictive control approach is chosen as the off-line method for designing optimal controllers using all the existing system knowledge. This controller is then adapted on-line using policy-iteration-based Q-learning, which is a stable model-free reinforcement learning approach. The combined approach is first illustrated in the optimal control of linear systems, which helps in the analysis as well as the validation of the method. A novel neural-networks-based parametric predictive control approach is then designed for the off-line optimal control of non-linear systems. The off-line approach is illustrated by applications to aircraft and spacecraft systems. This is followed by an extensive treatment of the off-line optimal control of the MHD generator using this neuro-control approach. On-line adaptation of the controller is implemented using several novel schemes derived from the policy-iteration-based Q-learning. The implementation results demonstrate the success of these on-line algorithms for adapting towards modeling errors in the off-line design.

  16. Model-free constrained data-driven iterative reference input tuning algorithm with experimental validation

    NASA Astrophysics Data System (ADS)

    Radac, Mircea-Bogdan; Precup, Radu-Emil

    2016-05-01

    This paper presents the design and experimental validation of a new model-free data-driven iterative reference input tuning (IRIT) algorithm that solves a reference trajectory tracking problem as an optimization problem with control signal saturation constraints and control signal rate constraints. The IRIT algorithm design employs an experiment-based stochastic search algorithm to use the advantages of iterative learning control. The experimental results validate the IRIT algorithm applied to a non-linear aerodynamic position control system. The results prove that the IRIT algorithm offers the significant control system performance improvement by few iterations and experiments conducted on the real-world process and model-free parameter tuning.

  17. Model learning for robot control: a survey.

    PubMed

    Nguyen-Tuong, Duy; Peters, Jan

    2011-11-01

    Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies. PMID:21487784

  18. ITER's woes

    NASA Astrophysics Data System (ADS)

    jjeherrera; Duffield, John; ZoloftNotWorking; esromac; protogonus; mleconte; cmfluteguy; adivita

    2014-07-01

    In reply to the physicsworld.com news story “US sanctions on Russia hit ITER council” (20 May, http://ow.ly/xF7oc and also June p8), about how a meeting of the fusion experiment's council had to be moved from St Petersburg and the US Congress's call for ITER boss Osamu Motojima to step down.

  19. Safety-factor profile tailoring by improved electron cyclotron system for sawtooth control and reverse shear scenarios in ITER

    SciTech Connect

    Zucca, C.; Sauter, O.; Fable, E.; Henderson, M. A.; Polevoi, A.; Saibene, G.

    2008-11-01

    The effect of the predicted local electron cyclotron current driven by the optimized electron cyclotron system on ITER is discussed. A design variant was recently proposed to enlarge the physics program covered by the upper and equatorial launchers. By extending the functionality range of the upper launcher, significant control capabilities of the sawtooth period can be obtained. The upper launcher improvement still allows enough margin to exceed the requirements for neoclassical tearing mode stabilization, for which it was originally designed. The analysis of the sawtooth control is carried on with the ASTRA transport code, coupled with the threshold model by Por-celli, to study the control capabilities of the improved upper launcher on the sawtooth instability. The simulations take into account the significant stabilizing effect of the fusion alpha particles. The sawtooth period can be increased by a factor of 1.5 with co-ECCD outside the q = 1 surface, and decreased by at least 30% with co-ECCD inside q = 1. The present ITER base-line design has the electron cyclotron launchers providing only co-ECCD. The variant for the equatorial launcher proposes the possibility to drive counter-ECCD with 1 of the 3 rows of mirrors: the counter-ECCD can then be balanced with co-ECCD and provide pure ECH with no net driven current. The difference between full co-ECCD off-axis using all 20MW from the equatorial launcher and 20MW co-ECCD driven by 2/3 from the equatorial launcher and 1/3 from the upper launcher is shown to be negligible. Cnt-ECCD also offers greater control of the plasma current density, therefore this analysis addresses the performance of the equatorial launcher to control the central q profile. The equatorial launcher is shown to control very efficiently the value of q{sub 0.2}-q{sub min} in advanced scenarios, if one row provides counter-ECCD.

  20. Reinforcement learning output feedback NN control using deterministic learning technique.

    PubMed

    Xu, Bin; Yang, Chenguang; Shi, Zhongke

    2014-03-01

    In this brief, a novel adaptive-critic-based neural network (NN) controller is investigated for nonlinear pure-feedback systems. The controller design is based on the transformed predictor form, and the actor-critic NN control architecture includes two NNs, whereas the critic NN is used to approximate the strategic utility function, and the action NN is employed to minimize both the strategic utility function and the tracking error. A deterministic learning technique has been employed to guarantee that the partial persistent excitation condition of internal states is satisfied during tracking control to a periodic reference orbit. The uniformly ultimate boundedness of closed-loop signals is shown via Lyapunov stability analysis. Simulation results are presented to demonstrate the effectiveness of the proposed control. PMID:24807456

  1. Quality Control & Design in Science Learning

    ERIC Educational Resources Information Center

    Sumrall, William J.; Schillinger, Don

    2003-01-01

    One area of science education that is, at times, neglected involves lessons on technological concepts of these principles--designing, testing, and quality control. Instead, a focus upon science concepts from a pure, and unapplied, perspective is the norm. Thus, while students may learn the equation "mass divided by volume equals density," the…

  2. An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment

    SciTech Connect

    Manoli, Gabriele; Rossi, Matteo; Pasetto, Damiano; Deiana, Rita; Ferraris, Stefano; Cassiani, Giorgio; Putti, Mario

    2015-02-15

    The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequential inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment.

  3. An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment

    NASA Astrophysics Data System (ADS)

    Manoli, Gabriele; Rossi, Matteo; Pasetto, Damiano; Deiana, Rita; Ferraris, Stefano; Cassiani, Giorgio; Putti, Mario

    2015-02-01

    The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequential inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment.

  4. Connectionist reinforcement learning of robot control skills

    NASA Astrophysics Data System (ADS)

    Araújo, Rui; Nunes, Urbano; de Almeida, A. T.

    1998-07-01

    Many robot manipulator tasks are difficult to model explicitly and it is difficult to design and program automatic control algorithms for them. The development, improvement, and application of learning techniques taking advantage of sensory information would enable the acquisition of new robot skills and avoid some of the difficulties of explicit programming. In this paper we use a reinforcement learning approach for on-line generation of skills for control of robot manipulator systems. Instead of generating skills by explicit programming of a perception to action mapping they are generated by trial and error learning, guided by a performance evaluation feedback function. The resulting system may be seen as an anticipatory system that constructs an internal representation model of itself and of its environment. This enables it to identify its current situation and to generate corresponding appropriate commands to the system in order to perform the required skill. The method was applied to the problem of learning a force control skill in which the tool-tip of a robot manipulator must be moved from a free space situation, to a contact state with a compliant surface and having a constant interaction force.

  5. Simultaneous gains tuning in boiler/turbine PID-based controller clusters using iterative feedback tuning methodology.

    PubMed

    Zhang, Shu; Taft, Cyrus W; Bentsman, Joseph; Hussey, Aaron; Petrus, Bryan

    2012-09-01

    Tuning a complex multi-loop PID based control system requires considerable experience. In today's power industry the number of available qualified tuners is dwindling and there is a great need for better tuning tools to maintain and improve the performance of complex multivariable processes. Multi-loop PID tuning is the procedure for the online tuning of a cluster of PID controllers operating in a closed loop with a multivariable process. This paper presents the first application of the simultaneous tuning technique to the multi-input-multi-output (MIMO) PID based nonlinear controller in the power plant control context, with the closed-loop system consisting of a MIMO nonlinear boiler/turbine model and a nonlinear cluster of six PID-type controllers. Although simplified, the dynamics and cross-coupling of the process and the PID cluster are similar to those used in a real power plant. The particular technique selected, iterative feedback tuning (IFT), utilizes the linearized version of the PID cluster for signal conditioning, but the data collection and tuning is carried out on the full nonlinear closed-loop system. Based on the figure of merit for the control system performance, the IFT is shown to deliver performance favorably comparable to that attained through the empirical tuning carried out by an experienced control engineer. PMID:22633781

  6. Power requirements for electron cyclotron current drive and ion cyclotron resonance heating for sawtooth control in ITER

    NASA Astrophysics Data System (ADS)

    Chapman, I. T.; Graves, J. P.; Sauter, O.; Zucca, C.; Asunta, O.; Buttery, R. J.; Coda, S.; Goodman, T.; Igochine, V.; Johnson, T.; Jucker, M.; La Haye, R. J.; Lennholm, M.; Contributors, JET-EFDA

    2013-06-01

    13 MW of electron cyclotron current drive (ECCD) power deposited inside the q = 1 surface is likely to reduce the sawtooth period in ITER baseline scenario below the level empirically predicted to trigger neoclassical tearing modes (NTMs). However, since the ECCD control scheme is solely predicated upon changing the local magnetic shear, it is prudent to plan to use a complementary scheme which directly decreases the potential energy of the kink mode in order to reduce the sawtooth period. In the event that the natural sawtooth period is longer than expected, due to enhanced α particle stabilization for instance, this ancillary sawtooth control can be provided from >10MW of ion cyclotron resonance heating (ICRH) power with a resonance just inside the q = 1 surface. Both ECCD and ICRH control schemes would benefit greatly from active feedback of the deposition with respect to the rational surface. If the q = 1 surface can be maintained closer to the magnetic axis, the efficacy of ECCD and ICRH schemes significantly increases, the negative effect on the fusion gain is reduced, and off-axis negative-ion neutral beam injection (NNBI) can also be considered for sawtooth control. Consequently, schemes to reduce the q = 1 radius are highly desirable, such as early heating to delay the current penetration and, of course, active sawtooth destabilization to mediate small frequent sawteeth and retain a small q = 1 radius. Finally, there remains a residual risk that the ECCD + ICRH control actuators cannot keep the sawtooth period below the threshold for triggering NTMs (since this is derived only from empirical scaling and the control modelling has numerous caveats). If this is the case, a secondary control scheme of sawtooth stabilization via ECCD + ICRH + NNBI, interspersed with deliberate triggering of a crash through auxiliary power reduction and simultaneous pre-emptive NTM control by off-axis ECCD has been considered, permitting long transient periods with high fusion

  7. Controlling working memory with learned instructions.

    PubMed

    Sylvester, J C; Reggia, J A; Weems, S A; Bunting, M F

    2013-05-01

    Many neural network models of cognition rely heavily on the modeler for control over aspects of model behavior, such as when to learn and whether an item is judged to be present in memory. Developing neurocomputational methods that allow these cognitive control mechanisms to be performed autonomously has proven to be surprisingly difficult. Here we present a general purpose framework called GALIS that we believe is amenable to developing a broad range of cognitive control models. Models built using GALIS consist of a network of interacting "regions" inspired by the organization of primate cerebral cortex. Each region is an attractor network capable of learning temporal sequences, and the individual regions not only exchange task-specific information with each other, but also gate the others' functions and interactions. As a result, GALIS models can learn both task-specific content and also the necessary cognitive control procedures (instructions) needed to perform a task in the first place. As an initial test of this approach, we use GALIS to implement a model that is trained simultaneously to perform five versions of the n-Back task. Not only does the resulting n-Back model function correctly, determining when to learn or remove items in working memory, but its accuracy and response times correlate strongly with those of human subjects performing the same task. The n-Back model also makes testable predictions about how human accuracy would be affected by intra-trial changes in n's value. We conclude that GALIS opens a potentially effective pathway toward developing a range of cognitive control models with improved autonomy. PMID:23465563

  8. Design of an iterative auto-tuning algorithm for a fuzzy PID controller

    NASA Astrophysics Data System (ADS)

    Saeed, Bakhtiar I.; Mehrdadi, B.

    2012-05-01

    Since the first application of fuzzy logic in the field of control engineering, it has been extensively employed in controlling a wide range of applications. The human knowledge on controlling complex and non-linear processes can be incorporated into a controller in the form of linguistic terms. However, with the lack of analytical design study it is becoming more difficult to auto-tune controller parameters. Fuzzy logic controller has several parameters that can be adjusted, such as: membership functions, rule-base and scaling gains. Furthermore, it is not always easy to find the relation between the type of membership functions or rule-base and the controller performance. This study proposes a new systematic auto-tuning algorithm to fine tune fuzzy logic controller gains. A fuzzy PID controller is proposed and applied to several second order systems. The relationship between the closed-loop response and the controller parameters is analysed to devise an auto-tuning method. The results show that the proposed method is highly effective and produces zero overshoot with enhanced transient response. In addition, the robustness of the controller is investigated in the case of parameter changes and the results show a satisfactory performance.

  9. Nonlinear control and online optimization of the burn condition in ITER via heating, isotopic fueling and impurity injection

    NASA Astrophysics Data System (ADS)

    Boyer, Mark D.; Schuster, Eugenio

    2014-10-01

    The ITER tokamak, the next experimental step toward the development of nuclear fusion reactors, will explore the burning plasma regime in which the plasma temperature is sustained mostly by fusion heating. Regulation of the fusion power through modulation of fueling and external heating sources, referred to as burn control, is one of the fundamental problems in burning plasma research. Active control will be essential for achieving and maintaining desired operating points, responding to changing power demands, and ensuring stable operation. Most existing burn control efforts use either non-model-based control techniques or designs based on linearized models. These approaches must be designed for particular operating points and break down for large perturbations. In this work, we utilize a spatially averaged (zero-dimensional) nonlinear model to synthesize a multi-variable nonlinear burn control strategy that can reject large perturbations and move between operating points. The controller uses all of the available actuation techniques in tandem to ensure good performance, even if one or more of the actuators saturate. Adaptive parameter estimation is used to improve the model parameter estimates used by the feedback controller in real-time and ensure asymptotic tracking of the desired operating point. In addition, we propose the use of a model-based online optimization algorithm to drive the system to a state that minimizes a given cost function, while respecting input and state constraints. A zero-dimensional simulation study is presented to show the performance of the adaptive control scheme and the optimization scheme with a cost function weighting the fusion power and temperature tracking errors.

  10. Analysis of the phase control of the ITER ICRH antenna array. Influence on the load resilience and radiated power spectrum

    NASA Astrophysics Data System (ADS)

    Messiaen, A.; Swain, D.; Ongena, J.; Vervier, M.

    2015-12-01

    The paper analyses how the phasing of the ITER ICRH 24 strap array evolves from the power sources up to the strap currents of the antenna. The study of the phasing control and coherence through the feeding circuits with prematching and automatic matching and decoupling network is made by modeling starting from the TOPICA matrix of the antenna array for a low coupling plasma profile and for current drive phasing (worst case for mutual coupling effects). The main results of the analysis are: (i) the strap current amplitude is well controlled by the antinode Vmax amplitude of the feeding lines, (ii) the best toroidal phasing control is done by the adjustment of the mean phase of Vmax of each poloidal straps column, (iii) with well adjusted system the largest strap current phasing error is ±20°, (iv) the effect on load resilience remains well below the maximum affordable VSWR of the generators, (v) the effect on the radiated power spectrum versus k// computed by means of the coupling code ANTITER II remains small for the considered cases.

  11. Analysis of the phase control of the ITER ICRH antenna array. Influence on the load resilience and radiated power spectrum

    SciTech Connect

    Messiaen, A. Ongena, J.; Vervier, M.; Swain, D.

    2015-12-10

    The paper analyses how the phasing of the ITER ICRH 24 strap array evolves from the power sources up to the strap currents of the antenna. The study of the phasing control and coherence through the feeding circuits with prematching and automatic matching and decoupling network is made by modeling starting from the TOPICA matrix of the antenna array for a low coupling plasma profile and for current drive phasing (worst case for mutual coupling effects). The main results of the analysis are: (i) the strap current amplitude is well controlled by the antinode V{sub max} amplitude of the feeding lines, (ii) the best toroidal phasing control is done by the adjustment of the mean phase of V{sub max} of each poloidal straps column, (iii) with well adjusted system the largest strap current phasing error is ±20°, (iv) the effect on load resilience remains well below the maximum affordable VSWR of the generators, (v) the effect on the radiated power spectrum versus k{sub //} computed by means of the coupling code ANTITER II remains small for the considered cases.

  12. Heritability of motor control and motor learning

    PubMed Central

    Missitzi, Julia; Gentner, Reinhard; Misitzi, Angelica; Geladas, Nickos; Politis, Panagiotis; Klissouras, Vassilis; Classen, Joseph

    2013-01-01

    Abstract The aim of this study was to elucidate the relative contribution of genes and environment on individual differences in motor control and acquisition of a force control task, in view of recent association studies showing that several candidate polymorphisms may have an effect on them. Forty‐four healthy female twins performed brisk isometric abductions with their right thumb. Force was recorded by a transducer and fed back to the subject on a computer screen. The task was to place the tracing of the peak force in a force window defined between 30% and 40% of the subject's maximum force, as determined beforehand. The initial level of proficiency was defined as the number of attempts reaching the force window criterion within the first 100 trials. The difference between the number of successful trials within the last and the first 100 trials was taken as a measure of motor learning. For motor control, defined by the initial level of proficiency, the intrapair differences in monozygotic (MZ) and dizygotic (DZ) twins were 6.8 ± 7.8 and 13.8 ± 8.4, and the intrapair correlations 0.77 and 0.39, respectively. Heritability was estimated at 0.68. Likewise for motor learning intrapair differences in the increment of the number of successful trials in MZ and DZ twins were 5.4 ± 5.2 and 12.8 ± 7, and the intrapair correlations 0.58 and 0.19. Heritability reached 0.70. The present findings suggest that heredity accounts for a major part of existing differences in motor control and motor learning, but uncertainty remains which gene polymorphisms may be responsible. PMID:24744865

  13. A Case Study of Modern PLC and LabVIEW Controls: Power Supply Controls for the ORNL ITER ECH Test Stand

    SciTech Connect

    Barker, Alan M; Killough, Stephen M; Bigelow, Tim S; White, John A; Munro Jr, John K

    2011-01-01

    Power Supply Controls are being developed at Oak Ridge National Laboratory (ORNL) to test transmission line components of the Electron Cyclotron Heating (ECH) system, with a focus on gyrotrons and waveguides, in support of the International Thermonuclear Experimental Reactor (ITER). The control is performed by several Programmable Logic Controllers (PLC s) located near the different equipment. A technique of Supervisory Control and Data Acquisition (SCADA) is presented to monitor, control, and log actions of the PLC s on a PC through use of Allen Bradley s Remote I/O communication interface coupled with an Open Process Control/Object Linking and Embedding [OLE] for Process Control (OPC) Server/Client architecture. The OPC data is then linked to a National Instruments (NI) LabVIEW system for monitoring and control. Details of the architecture and insight into applicability to other systems are presented in the rest of this paper. Future integration with an EPICS (Experimental Physics Industrial Control System) based mini-CODAC (Control, Data Access and Communication) SCADA system is under consideration, and integration considerations will be briefly introduced.

  14. Learning Dynamic Control of Body Roll Orientation

    PubMed Central

    Vimal, Vivekanand Pandey; Lackner, James R.; DiZio, Paul

    2016-01-01

    Our objective was to examine how the control of orientation is learned in a task involving dynamically balancing about an unstable equilibrium point, the gravitational vertical, in the absence of leg reflexes and muscle stiffness. Subjects (n=10) used a joystick to set themselves to the gravitational vertical while seated in a multi-axis rotation system device (MARS) programmed with inverted pendulum dynamics. The MARS is driven by powerful servomotors and can faithfully follow joystick commands up to 2.5 Hz with a 30 ms latency. To make the task extremely difficult, the pendulum constant was set to 600°/sec2. Each subject participated in 5 blocks of 4 trials, with a trial ending after a cumulative 100 s of balancing, excluding reset times when a subject lost control. To characterize performance and learning, we used metrics derived from joystick movements, phase portraits (joystick deflections vs MARS position and MARS velocity vs angular position), and stabilogram diffusion functions. We found that as subjects improved their balancing performance they did so by making fewer destabilizing joystick movements and reducing the number and duration of joystick commands. The control strategy they acquired involved making more persistent short-term joystick movements, waiting longer before making changes to ongoing motion, and only intervening intermittently. PMID:26525709

  15. Space Station Control Moment Gyroscope Lessons Learned

    NASA Technical Reports Server (NTRS)

    Gurrisi, Charles; Seidel, Raymond; Dickerson, Scott; Didziulis, Stephen; Frantz, Peter; Ferguson, Kevin

    2010-01-01

    Four 4760 Nms (3510 ft-lbf-s) Double Gimbal Control Moment Gyroscopes (DGCMG) with unlimited gimbal freedom about each axis were adopted by the International Space Station (ISS) Program as the non-propulsive solution for continuous attitude control. These CMGs with a life expectancy of approximately 10 years contain a flywheel spinning at 691 rad/s (6600 rpm) and can produce an output torque of 258 Nm (190 ft-lbf)1. One CMG unexpectedly failed after approximately 1.3 years and one developed anomalous behavior after approximately six years. Both units were returned to earth for failure investigation. This paper describes the Space Station Double Gimbal Control Moment Gyroscope design, on-orbit telemetry signatures and a summary of the results of both failure investigations. The lessons learned from these combined sources have lead to improvements in the design that will provide CMGs with greater reliability to assure the success of the Space Station. These lessons learned and design improvements are not only applicable to CMGs but can be applied to spacecraft mechanisms in general.

  16. Tunnel Ventilation Control Using Reinforcement Learning Methodology

    NASA Astrophysics Data System (ADS)

    Chu, Baeksuk; Kim, Dongnam; Hong, Daehie; Park, Jooyoung; Chung, Jin Taek; Kim, Tae-Hyung

    The main purpose of tunnel ventilation system is to maintain CO pollutant concentration and VI (visibility index) under an adequate level to provide drivers with comfortable and safe driving environment. Moreover, it is necessary to minimize power consumption used to operate ventilation system. To achieve the objectives, the control algorithm used in this research is reinforcement learning (RL) method. RL is a goal-directed learning of a mapping from situations to actions without relying on exemplary supervision or complete models of the environment. The goal of RL is to maximize a reward which is an evaluative feedback from the environment. In the process of constructing the reward of the tunnel ventilation system, two objectives listed above are included, that is, maintaining an adequate level of pollutants and minimizing power consumption. RL algorithm based on actor-critic architecture and gradient-following algorithm is adopted to the tunnel ventilation system. The simulations results performed with real data collected from existing tunnel ventilation system and real experimental verification are provided in this paper. It is confirmed that with the suggested controller, the pollutant level inside the tunnel was well maintained under allowable limit and the performance of energy consumption was improved compared to conventional control scheme.

  17. Learning dynamic control of body roll orientation.

    PubMed

    Vimal, Vivekanand Pandey; Lackner, James R; DiZio, Paul

    2016-02-01

    Our objective was to examine how the control of orientation is learned in a task involving dynamically balancing about an unstable equilibrium point, the gravitational vertical, in the absence of leg reflexes and muscle stiffness. Subjects (n = 10) used a joystick to set themselves to the gravitational vertical while seated in a multi-axis rotation system (MARS) device programmed with inverted pendulum dynamics. The MARS is driven by powerful servomotors and can faithfully follow joystick commands up to 2.5 Hz with a 30-ms latency. To make the task extremely difficult, the pendulum constant was set to 600°/s(2). Each subject participated in five blocks of four trials, with a trial ending after a cumulative 100 s of balancing, excluding reset times when a subject lost control. To characterize performance and learning, we used metrics derived from joystick movements, phase portraits (joystick deflections vs MARS position and MARS velocity vs angular position), and stabilogram diffusion functions. We found that as subjects improved their balancing performance, they did so by making fewer destabilizing joystick movements and reducing the number and duration of joystick commands. The control strategy they acquired involved making more persistent short-term joystick movements, waiting longer before making changes to ongoing motion, and only intervening intermittently. PMID:26525709

  18. A quasi-analytical method for non-iterative computation of nonlinear controls

    NASA Technical Reports Server (NTRS)

    Junkins, J. L.; Thompson, R. C.; Turner, J. D.

    1987-01-01

    An optimal control solution process was developed for a general class of nonlinear dynamical systems. The method combines control theory, perturbation methods, and Van Loan's recent matrix exponential results. A variety of applications support the practical utility of this method. Nonlinear rigid body optimal maneuvers are routinely solved. Flexible body dynamical systems of an order greater than 40 were solved. The method fails occasionally due to poor convergence of the perturbation expansion or numerical difficulties associated with computing the matrix exponential. The method is attractive because it appears to be a good candidate for semi-automation; no initial guess is required, and it usually converges at 2nd or 3rd order in minutes of machine time.

  19. Iterative reconstruction of volumetric modulated arc radiotherapy plans using control point basis vectors

    NASA Astrophysics Data System (ADS)

    Barbiere, Joseph C.; Kapulsky, Alexander; Ndlovu, Alois

    2014-03-01

    Volumetric Modulated Arc Radiotherapy is an innovative technique currently utilized to efficiently deliver complex treatments. Dose rate, speed of rotation, and field shape are continuously varied as the radiation source rotates about the patient. Patient specific quality assurance is performed to verify that the delivered dose distribution is consistent with the plan formulated in a treatment planning system. The purpose of this work is to present novel methodology using a Gafchromic EBT3 film image of a patient plan in a cylindrical phantom and calculating the delivered MU per control point. Images of two dimensional plan dose matrices and film scans are analyzed using MATLAB with the imaging toolbox. Dose profiles in a ring corresponding to the film position are extracted from the plan matrices for comparison with the corresponding measured film dose. The plan is made up of a series of individual static Control Points. If we consider these Control Points a set of basis vectors, then variations in the plan can be represented as the weighted sum of the basis. The weighing coefficients representing the actual delivered MU can be determined by any available optimization tool, such as downhill simplex or non-linear programming. In essence we reconstruct an image of the delivered dose. Clinical quality assurance is performed with this technique by computing a patient plan with the measured monitor units and standard plan evaluation tools such as Dose Volume Histograms. Testing of the algorithm with known changes in the reference images indicated a correlation coefficient greater than 0.99.

  20. Air pollution control system research: An iterative approach to developing affordable systems

    NASA Technical Reports Server (NTRS)

    Watt, Lewis C.; Cannon, Fred S.; Heinsohn, Robert J.; Spaeder, Timothy A.

    1995-01-01

    This paper describes a Strategic Environmental Research and Development Program (SERDP) funded project led jointly by the Marine Corps Multi-Commodity Maintenance Centers, and the Air and Energy Engineering Research Laboratory (AEERL) of the USEPA. The research focuses on paint booth exhaust minimization using recirculation, and on volatile organic compound (VOC) oxidation by the modules of a hybrid air pollution control system. The research team is applying bench, pilot and full scale systems to accomplish the goals of reduced cost and improved effectiveness of air treatment systems for paint booth exhaust.

  1. Air pollution control system research: An iterative approach to developing affordable systems

    SciTech Connect

    Watt, L.C.; Cannon, F.S.; Heinsohn, R.J.; Spaeder, T.A.; Darvin, C.H.

    1993-12-31

    The research will be accomplished on lab scale, pilot scale, and production air pollution control systems (APCS). The production system, to be installed at Marine Corps Logistics Base (MCLB) Barstow, CA, will treat the exhaust from three paint booths which will be modified to recirculate a large percentage of their exhaust. These recirculation systems are, themselves, a critical element in the overall R and D effort. The goal of the program is to conduct an R and D effort which will improve and demonstrate a combination of technologies intended to make VOC treatment both effective and affordable. The US Marine Corps, the other services and industry will each benefit.

  2. Modeling of divertor particle and heat loads during application of resonant magnetic perturbation fields for ELM control in ITER

    NASA Astrophysics Data System (ADS)

    Schmitz, O.; Becoulet, M.; Cahyna, P.; Evans, T. E.; Feng, Y.; Frerichs, H.; Kirschner, A.; Kukushkin, A.; Laengner, R.; Lunt, T.; Loarte, A.; Pitts, R.; Reiser, D.; Reiter, D.; Saibene, G.; Samm, U.

    2013-07-01

    First results from three-dimensional modeling of the divertor heat and particle flux pattern during application of resonant magnetic perturbation fields as ELM control scheme in ITER with the EMC3-Eirene fluid plasma and kinetic neutral transport code are discussed. The formation of a helical magnetic footprint breaks the toroidal symmetry of the heat and particle fluxes. Expansion of the flux pattern as far as 60 cm away from the unperturbed strike line is seen with vacuum RMP fields, resulting in a preferable heat flux spreading. Inclusion of plasma response reduces the radial extension of the heat and particle fluxes and results in a heat flux peaking closer to the unperturbed level. A strong reduction of the particle confinement is found. 3D flow channels are identified as a consistent reason due to direct parallel outflow from inside of the separatrix. Their radial inward expansion and hence the level of particle pump out is shown to be dependent on the perturbation level.

  3. Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)

    NASA Technical Reports Server (NTRS)

    Niewoehner, Kevin R.; Carter, John (Technical Monitor)

    2001-01-01

    The research accomplishments for the cooperative agreement 'Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)' include the following: (1) previous IFC program data collection and analysis; (2) IFC program support site (configured IFC systems support network, configured Tornado/VxWorks OS development system, made Configuration and Documentation Management Systems Internet accessible); (3) Airborne Research Test Systems (ARTS) II Hardware (developed hardware requirements specification, developing environmental testing requirements, hardware design, and hardware design development); (4) ARTS II software development laboratory unit (procurement of lab style hardware, configured lab style hardware, and designed interface module equivalent to ARTS II faceplate); (5) program support documentation (developed software development plan, configuration management plan, and software verification and validation plan); (6) LWR algorithm analysis (performed timing and profiling on algorithm); (7) pre-trained neural network analysis; (8) Dynamic Cell Structures (DCS) Neural Network Analysis (performing timing and profiling on algorithm); and (9) conducted technical interchange and quarterly meetings to define IFC research goals.

  4. Exploring Learner Autonomy: Language Learning Locus of Control in Multilinguals

    ERIC Educational Resources Information Center

    Peek, Ron

    2016-01-01

    By using data from an online language learning beliefs survey (n?=?841), defining language learning experience in terms of participants' multilingualism, and using a domain-specific language learning locus of control (LLLOC) instrument, this article examines whether more experienced language learners can also be seen as more autonomous language…

  5. Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    1997-01-01

    Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

  6. Neural networks for self-learning control systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Derrick H.; Widrow, Bernard

    1990-01-01

    It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

  7. Active controllers and the time duration to learn a task

    NASA Technical Reports Server (NTRS)

    Repperger, D. W.; Goodyear, C.

    1986-01-01

    An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

  8. Decentralized stabilization for a class of continuous-time nonlinear interconnected systems using online learning optimal control approach.

    PubMed

    Liu, Derong; Wang, Ding; Li, Hongliang

    2014-02-01

    In this paper, using a neural-network-based online learning optimal control approach, a novel decentralized control strategy is developed to stabilize a class of continuous-time nonlinear interconnected large-scale systems. First, optimal controllers of the isolated subsystems are designed with cost functions reflecting the bounds of interconnections. Then, it is proven that the decentralized control strategy of the overall system can be established by adding appropriate feedback gains to the optimal control policies of the isolated subsystems. Next, an online policy iteration algorithm is presented to solve the Hamilton-Jacobi-Bellman equations related to the optimal control problem. Through constructing a set of critic neural networks, the cost functions can be obtained approximately, followed by the control policies. Furthermore, the dynamics of the estimation errors of the critic networks are verified to be uniformly and ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness of the present decentralized control scheme. PMID:24807039

  9. Feedback control by online learning an inverse model.

    PubMed

    Waegeman, Tim; Wyffels, Francis; Schrauwen, Francis

    2012-10-01

    A model, predictor, or error estimator is often used by a feedback controller to control a plant. Creating such a model is difficult when the plant exhibits nonlinear behavior. In this paper, a novel online learning control framework is proposed that does not require explicit knowledge about the plant. This framework uses two learning modules, one for creating an inverse model, and the other for actually controlling the plant. Except for their inputs, they are identical. The inverse model learns by the exploration performed by the not yet fully trained controller, while the actual controller is based on the currently learned model. The proposed framework allows fast online learning of an accurate controller. The controller can be applied on a broad range of tasks with different dynamic characteristics. We validate this claim by applying our control framework on several control tasks: 1) the heating tank problem (slow nonlinear dynamics); 2) flight pitch control (slow linear dynamics); and 3) the balancing problem of a double inverted pendulum (fast linear and nonlinear dynamics). The results of these experiments show that fast learning and accurate control can be achieved. Furthermore, a comparison is made with some classical control approaches, and observations concerning convergence and stability are made. PMID:24808008

  10. A reinforcement learning-based architecture for fuzzy logic control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

  11. Communal learning within a distributed robotic control system

    NASA Astrophysics Data System (ADS)

    Digney, Bruce L.

    2001-09-01

    It is accepted that the ability to learn and adapt is key to prosperity and survival in both individuals and societies. The same is true of populations of robots. Those robots within a population that are able to learn will outperform, survive longer and perhaps exploit their non-learning co- workers. This paper describes the ongoing results of Communal Learning in the Cognitive Colonies Project (CMU/Robotics and DRES), funded jointly by DARPA ITO- Software for Distributed Robotics and DRDC-DRES. Discussed will be how communal learning fits into the free market architecture for distributed control. Techniques for representing experiences, learned behaviors, maps and computational resources as commodities within the market economy will be presented. Once in a commodity structure, the cycle of speculate, act, receive profits or sustain losses and then learn of the market economy. This allows successful control strategies to emerge and the individuals who discovered them to become established as successful. This paper will discuss: learning to predict costs and make better deals, learning transition confidences, learning causes of death, learning with robot sacrifice and learning model parameters.

  12. Learning hybrid force and position control of robot manipulators

    SciTech Connect

    Jeon, Doyoung; Tomizuka, Masayoshi

    1993-08-01

    When a robot performs the same task repeatedly, a learning controller can enhance the performance of the system significantly. The learning control, however, has not been studied in the force control of robot manipulators as extensively as in the position control of robot manipulators. In this paper, the learning control is applied to hybrid force and position control of robot manipulators. When the geometry and position of a constraint surface is known, the hybrid force and position controller and the feedforward compensator can be designed in the constraint coordinates. When the operation is periodic, the learning hybrid force and position control enhances the control performance as the feedforward compensator is updated in each cycle by the force and position error in the preceding trials. This scheme is proved to be asymptotically stable. A two degree of freedom SCARA-type direct-drive robot manipulator is used to test the learning hybrid force and position control. The deburring tool mounted on the upper link of the robot could follow a flat, tilted flat, and curved 1/4 inch aluminum plate with a desired contact force of 10 N (within the root-mean-square force error of 1.95 N) and with a desired tangential velocity. The experiments confirmed the effectiveness of the learning hybrid force and position controller.

  13. Stimulus Control with Computer Assisted Learning

    ERIC Educational Resources Information Center

    Navarro, Jose I.; Marchena, Esperanza; Alcalde, Concepcion; Ruiz, Gonzalo

    2004-01-01

    Computer Assisted Learning (CAL) has been shown to be an efficient learning-teaching procedure. Although there is an extensive educational software tradition using CAL approaches, few of them have demonstrated a better student performance than standard drill and practice methods. The purpose of this study was (a) to evaluate the effectiveness of…

  14. Efficient model learning methods for actor-critic control.

    PubMed

    Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

    2012-06-01

    We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm. PMID:22156998

  15. The Capacity Building programmes of GITEWS - visions, goals, lessons learned, and re-iterated needs and demands

    NASA Astrophysics Data System (ADS)

    Schlurmann, T.; Siebert, M.

    2011-02-01

    hazards is still pending. Local authorities and researchers in tentative affected regions are now trained and enabled to disseminate and apply their knowledge and planning experience to other coastal regions in the area to help facilitating and multiplying effective disaster management plans and strategies. Yet, the Capacity Building framework within GITEWS also elucidated gaps in the early warning chain so that updated and to some extent re-iterated needs and demands in Capacity Building programs in any future research or development cooperation project are presented and discussed.

  16. Towards autonomous neuroprosthetic control using Hebbian reinforcement learning

    NASA Astrophysics Data System (ADS)

    Mahmoudi, Babak; Pohlmeyer, Eric A.; Prins, Noeline W.; Geng, Shijia; Sanchez, Justin C.

    2013-12-01

    Objective. Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Approach. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. Main results. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. Significance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

  17. Robust and fast learning for fuzzy cerebellar model articulation controllers.

    PubMed

    Su, Shun-Feng; Lee, Zne-Jung; Wang, Yan-Ping

    2006-02-01

    In this paper, the online learning capability and the robust property for the learning algorithms of cerebellar model articulation controllers (CMAC) are discussed. Both the traditional CMAC and fuzzy CMAC are considered. In the study, we find a way of embeding the idea of M-estimators into the CMAC learning algorithms to provide the robust property against outliers existing in training data. An annealing schedule is also adopted for the learning constant to fulfill robust learning. In the study, we also extend our previous work of adopting the credit assignment idea into CMAC learning to provide fast learning for fuzzy CMAC. From demonstrated examples, it is clearly evident that the proposed algorithm indeed has faster and more robust learning. In our study, we then employ the proposed CMAC for an online learning control scheme used in the literature. In the implementation, we also propose to use a tuning parameter instead of a fixed constant to achieve both online learning and fine-tuning effects. The simulation results indeed show the effectiveness of the proposed approaches. PMID:16468579

  18. WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

    SciTech Connect

    Bai, T; Yan, H; Shi, F; Jia, X; Jiang, Steve B.; Lou, Y; Xu, Q; Mou, X

    2014-06-15

    Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential

  19. Learned Helplessness: A Theory for the Age of Personal Control.

    ERIC Educational Resources Information Center

    Peterson, Christopher; And Others

    Experiences with uncontrollable events may lead to the expectation that future events will elude control, resulting in disruptions in motivation, emotion, and learning. This text explores this phenomenon, termed learned helplessness, tracking it from its discovery to its entrenchment in the psychological canon. The volume summarizes and integrates…

  20. Short-Term Memory, Executive Control, and Children's Route Learning

    ERIC Educational Resources Information Center

    Purser, Harry R. M.; Farran, Emily K.; Courbois, Yannick; Lemahieu, Axelle; Mellier, Daniel; Sockeel, Pascal; Blades, Mark

    2012-01-01

    The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11 years and to relate route-learning performance to the components of Baddeley's model of working memory. Children carried out tasks that included measures of verbal and visuospatial short-term memory and executive control and also measures of verbal and…

  1. Fuzzy self-learning control for magnetic servo system

    NASA Technical Reports Server (NTRS)

    Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

    1994-01-01

    It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

  2. A Tale of Two Chambers: Iterative Approaches and Lessons Learned from Life Support Systems Testing in Altitude Chambers

    NASA Technical Reports Server (NTRS)

    Callini, Gianluca

    2016-01-01

    With a brand new fire set ablaze by a serendipitous convergence of events ranging from a science fiction novel and movie ("The Martian"), to ground-breaking recent discoveries of flowing water on its surface, the drive for the journey to Mars seems to be in a higher gear than ever before. We are developing new spacecraft and support systems to take humans to the Red Planet, while scientists on Earth continue using the International Space Station as a laboratory to evaluate the effects of long duration space flight on the human body. Written from the perspective of a facility test director rather than a researcher, and using past and current life support systems tests as examples, this paper seeks to provide an overview on how facility teams approach testing, the kind of information they need to ensure efficient collaborations and successful tests, and how, together with researchers and principal investigators, we can collectively apply what we learn to execute future tests.

  3. Internal-External Control, Learning, and Participation in Occupational Education

    ERIC Educational Resources Information Center

    Peters, John Marshall

    1969-01-01

    After examining prison inmates in their participation in occupational education programs, it was concluded that a person's control or lack of control over his environment affects his willingness to learn information or engage in activities that can be expected to increase his chance of control over his environment. (SE)

  4. Learning control for robotic manipulators with sparse data

    NASA Technical Reports Server (NTRS)

    Morita, Atsushi; Dubowsky, Steven; Hootsmans, Norbert A. M.

    1987-01-01

    Learning control algorithms have been proposed for error compensation in repetitive robotic manipulator tasks. It is shown that the performance of such control algorithms can be seriously degraded when the feedback data they use is relatively sparse in time, such as might be provided by vision systems. It is also shown that learning control algorithms can be modified to compensate for the effects of sparse data and thereby yield performance which approaches that of systems without limitations on the sensory information available for control.

  5. Learning, attentional control and action video games

    PubMed Central

    Green, C.S.; Bavelier, D.

    2012-01-01

    While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world training or rehabilitation paradigms, which necessarily seek to promote more general learning. In contrast to these typical findings, research over the past decade has shown that training on ‘action video games’ produces learning that transfers well beyond the training task. This has led to substantial interest among those interested in rehabilitation, for instance, after stroke or to treat amblyopia, or training for various precision-demanding jobs, for instance, endoscopic surgery or piloting unmanned aerial drones. Although the predominant focus of the field has been on outlining the breadth of possible action-game-related enhancements, recent work has concentrated on uncovering the mechanisms that underlie these changes, an important first step towards the goal of designing and using video games for more definite purposes. Game playing may not convey an immediate advantage on new tasks (increased performance from the very first trial), but rather the true effect of action video game playing may be to enhance the ability to learn new tasks. Such a mechanism may serve as a signature of training regimens that are likely to produce transfer of learning. PMID:22440805

  6. Learning, attentional control, and action video games.

    PubMed

    Green, C S; Bavelier, D

    2012-03-20

    While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world training or rehabilitation paradigms, which necessarily seek to promote more general learning. In contrast to these typical findings, research over the past decade has shown that training on 'action video games' produces learning that transfers well beyond the training task. This has led to substantial interest among those interested in rehabilitation, for instance, after stroke or to treat amblyopia, or training for various precision-demanding jobs, for instance, endoscopic surgery or piloting unmanned aerial drones. Although the predominant focus of the field has been on outlining the breadth of possible action-game-related enhancements, recent work has concentrated on uncovering the mechanisms that underlie these changes, an important first step towards the goal of designing and using video games for more definite purposes. Game playing may not convey an immediate advantage on new tasks (increased performance from the very first trial), but rather the true effect of action video game playing may be to enhance the ability to learn new tasks. Such a mechanism may serve as a signature of training regimens that are likely to produce transfer of learning. PMID:22440805

  7. Motor Skill Learning, Retention, and Control Deficits in Parkinson's Disease

    PubMed Central

    Pendt, Lisa Katharina; Reuter, Iris; Müller, Hermann

    2011-01-01

    Parkinson's disease, which affects the basal ganglia, is known to lead to various impairments of motor control. Since the basal ganglia have also been shown to be involved in learning processes, motor learning has frequently been investigated in this group of patients. However, results are still inconsistent, mainly due to skill levels and time scales of testing. To bridge across the time scale problem, the present study examined de novo skill learning over a long series of practice sessions that comprised early and late learning stages as well as retention. 19 non-demented, medicated, mild to moderate patients with Parkinson's disease and 19 healthy age and gender matched participants practiced a novel throwing task over five days in a virtual environment where timing of release was a critical element. Six patients and seven control participants came to an additional long-term retention testing after seven to nine months. Changes in task performance were analyzed by a method that differentiates between three components of motor learning prominent in different stages of learning: Tolerance, Noise and Covariation. In addition, kinematic analysis related the influence of skill levels as affected by the specific motor control deficits in Parkinson patients to the process of learning. As a result, patients showed similar learning in early and late stages compared to the control subjects. Differences occurred in short-term retention tests; patients' performance constantly decreased after breaks arising from poorer release timing. However, patients were able to overcome the initial timing problems within the course of each practice session and could further improve their throwing performance. Thus, results demonstrate the intact ability to learn a novel motor skill in non-demented, medicated patients with Parkinson's disease and indicate confounding effects of motor control deficits on retention performance. PMID:21760898

  8. E-learning: controlling costs and increasing value.

    PubMed

    Walsh, Kieran

    2015-04-01

    E-learning now accounts for a substantial proportion of medical education provision. This progress has required significant investment and this investment has in turn come under increasing scrutiny so that the costs of e-learning may be controlled and its returns maximised. There are multiple methods by which the costs of e-learning can be controlled and its returns maximised. This short paper reviews some of those methods that are likely to be most effective and that are likely to save costs without compromising quality. Methods might include accessing free or low-cost resources from elsewhere; create short learning resources that will work on multiple devices; using open source platforms to host content; using in-house faculty to create content; sharing resources between institutions; and promoting resources to ensure high usage. Whatever methods are used to control costs or increase value, it is most important to evaluate the impact of these methods. PMID:25899197

  9. Self-controlled practice benefits motor learning in older adults.

    PubMed

    Lessa, Helena Thofehrn; Chiviacowsky, Suzete

    2015-04-01

    Providing learners with the chance to choose over certain aspects of practice has been consistently shown to facilitate the acquisition of motor skills in several populations. However, studies investigating the effects of providing autonomy support during the learning process of older adults remain scarce. The objective of the present study was to investigate the effects of self-controlled amount of practice on the learning of a sequential motor task in older adults. Participants in the self-control group were able to choose when to stop practicing a speed cup stacking task, while the number of practice trials for a yoked group was pre-determined, mirroring the self-control group. The opportunity to choose when stop practicing facilitated motor performance and learning compared to the yoked condition. The findings suggest that letting older adult learners choose the amount of practice, supporting their autonomy needs, has a positive influence on motor learning. PMID:25687663

  10. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  11. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  12. Online learning and control of attraction basins for the development of sensorimotor control strategies.

    PubMed

    de Rengervé, Antoine; Andry, Pierre; Gaussier, Philippe

    2015-04-01

    Imitation and learning from humans require an adequate sensorimotor controller to learn and encode behaviors. We present the Dynamic Muscle Perception-Action(DM-PerAc) model to control a multiple degrees-of-freedom (DOF) robot arm. In the original PerAc model, path-following or place-reaching behaviors correspond to the sensorimotor attractors resulting from the dynamics of learned sensorimotor associations. The DM-PerAc model, inspired by human muscles, permits one to combine impedance-like control with the capability of learning sensorimotor attraction basins. We detail a solution to learn incrementally online the DM-PerAc visuomotor controller. Postural attractors are learned by adapting the muscle activations in the model depending on movement errors. Visuomotor categories merging visual and proprioceptive signals are associated with these muscle activations. Thus, the visual and proprioceptive signals activate the motor action generating an attractor which satisfies both visual and proprioceptive constraints. This visuomotor controller can serve as a basis for imitative behaviors. In addition, the muscle activation patterns can define directions of movement instead of postural attractors. Such patterns can be used in state-action couples to generate trajectories like in the PerAc model. We discuss a possible extension of the DM-PerAc controller by adapting the Fukuyori's controller based on the Langevin's equation. This controller can serve not only to reach attractors which were not explicitly learned, but also to learn the state/action couples to define trajectories. PMID:25576394

  13. Facts and fiction of learning systems. [decision making intelligent control

    NASA Technical Reports Server (NTRS)

    Saridis, G. N.

    1975-01-01

    The methodology that will provide the updated precision for the hardware control and the advanced decision making and planning in the software control is called learning systems and intelligent control. It was developed theoretically as an alternative for the nonsystematic heuristic approaches of artificial intelligence experiments and the inflexible formulation of modern optimal control methods. Its basic concepts are discussed and some feasibility studies of some practical applications are presented.

  14. Novel reinforcement learning approach for difficult control problems

    NASA Astrophysics Data System (ADS)

    Becus, Georges A.; Thompson, Edward A.

    1997-09-01

    We review work conducted over the past several years and aimed at developing reinforcement learning architectures for solving difficult control problems and based on and inspired by associative control process (ACP) networks. We briefly review ACP networks able to reproduce many classical instrumental conditioning test results observed in animal research and to engage in real-time, closed-loop, goal-seeking interactions with their environment. Chronologically, our contributions include the ideally interfaced ACP network which is endowed with hierarchical, attention, and failure recognition interface mechanisms which greatly enhanced the capabilities of the original ACP network. When solving the cart-pole problem, it achieves 100 percent reliability and a reduction in training time similar to that of Baird and Klopf's modified ACP network and additionally an order of magnitude reduction in number of failures experienced for successful training. Next we introduced the command and control center/internal drive (Cid) architecture for artificial neural learning systems. It consists of a hierarchy of command and control centers governing motor selection networks. Internal drives, similar hunger, thirst, or reproduction in biological systems, are formed within the controller to facilitate learning. Efficiency, reliability, and adjustability of this architecture were demonstrated on the benchmark cart-pole control problem. A comparison with other artificial learning systems indicates that it learns over 100 times faster than Barto, et al's adaptive search element/adaptive critic element, experiencing less failures by more than an order of magnitude while capable of being fine-tuned by the user, on- line, for improved performance without additional training. Finally we present work in progress on a 'peaks and valleys' scheme which moves away from the one-dimensional learning mechanism currently found in Cid and shows promises in solving even more difficult learning control

  15. Impact of the plasma response in three-dimensional edge plasma transport modelling for RMP ELM control scenarios at ITER

    NASA Astrophysics Data System (ADS)

    Schmitz, Oliver

    2014-10-01

    The constrains used in magneto-hydrodynamic (MHD) modeling of the plasma response to external resonant magnetic perturbation (RMP) fields have a profound impact on the three-dimensional (3-D) shape of the plasma boundary induced by RMP fields. In this contribution, the consequences of the plasma response on the actual 3D boundary structure and transport during RMP application at ITER are investigated. The 3D fluid plasma and kinetic neutral transport code EMC3-Eirene is used for edge transport modeling. Plasma response modeling is conducted with the M3D-C1 code using a single fluid, non-linear and a two fluid, linear MHD constrain. These approaches are compared to results with an ideal MHD like plasma response. A 3D plasma boundary is formed for all cases consisting of magnetic finger structures at the X-point intersecting the divertor surface in a helical footprint pattern. The width of the helical footprint pattern is largely reduced compared to vacuum magnetic fields when using the ideal MHD like screening model. This yields increasing peak heat fluxes in contrast to a beneficial heat flux spreading seen with vacuum fields. The particle pump out as well as loss of thermal energy is reduced by a factor of two compared to vacuum fields. In contrast, the impact of the plasma response obtained from both MHD constrains in M3D-C1 is nearly negligible at the plasma boundary and only a small modification of the magnetic footprint topology is detected. Accordingly, heat and particle fluxes on the target plates as well as the edge transport characteristics are comparable to the vacuum solution. This span of modeling results with different plasma response models highlights the importance of thoroughly validating both, plasma response and 3D edge transport models for a robust extrapolation towards ITER. Supported by ITER Grant IO/CT/11/4300000497 and F4E Grant GRT-055 (PMS-PE) and by Start-Up Funds of the University of Wisconsin - Madison.

  16. Impact on learning of an e-learning module on leukaemia: a randomised controlled trial

    PubMed Central

    2012-01-01

    Background e-learning resources may be beneficial for complex or conceptually difficult topics. Leukaemia is one such topic, yet there are no reports on the efficacy of e-learning for leukaemia. This study compared the learning impact on senior medical students of a purpose-built e-learning module on leukaemia, compared with existing online resources. Methods A randomised controlled trial was performed utilising volunteer senior medical students. Participants were randomly allocated to Study and Control groups. Following a pre-test on leukaemia administered to both groups, the Study group was provided with access to the new e-learning module, while the Control group was directed to existing online resources. A post-test and an evaluation questionnaire were administered to both groups at the end of the trial period. Results Study and Control groups were equivalent in gender distribution, mean academic ability, pre-test performance and time studying leukaemia during the trial. The Study group performed significantly better than the Control group in the post-test, in which the group to which the students had been allocated was the only significant predictor of performance. The Study group’s evaluation of the module was overwhelmingly positive. Conclusions A targeted e-learning module on leukaemia had a significant effect on learning in this cohort, compared with existing online resources. We believe that the interactivity, dialogic feedback and integration with the curriculum offered by the e-learning module contributed to its impact. This has implications for e-learning design in medicine and other disciplines. PMID:22640463

  17. A model for sensorimotor control and learning

    NASA Technical Reports Server (NTRS)

    Raibert, M. H.

    1978-01-01

    A model for motor learning, generalization, and adaptation is presented. It is shown that the equations of motion of a limb can be expressed in a parametric form that facilitates transformation of desired trajectories into plans. These parametric equations are used in conjunction with a quantized multi-dimensional memory organized by state variables. The memory is supplied with data derived from the analysis of practice movements. A small computer and mechanical arm are used to implement the model and study its properties. Results verify the ability to acquire new movements, adapt to mechanical loads, and generalize between similar movements.

  18. A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

    NASA Astrophysics Data System (ADS)

    Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

    This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

  19. Parallel Online Temporal Difference Learning for Motor Control.

    PubMed

    Caarls, Wouter; Schuitema, Erik

    2016-07-01

    Temporal difference (TD) learning, a key concept in reinforcement learning, is a popular method for solving simulated control problems. However, in real systems, this method is often avoided in favor of policy search methods because of its long learning time. But policy search suffers from its own drawbacks, such as the necessity of informed policy parameterization and initialization. In this paper, we show that TD learning can work effectively in real robotic systems as well, using parallel model learning and planning. Using locally weighted linear regression and trajectory sampled planning with 14 concurrent threads, we can achieve a speedup of almost two orders of magnitude over regular TD control on simulated control benchmarks. For a real-world pendulum swing-up task and a two-link manipulator movement task, we report a speedup of 20× to 60× , with a real-time learning speed of less than half a minute. The results are competitive with state-of-the-art policy search. PMID:26111402

  20. Recent developments in learning control and system identification for robots and structures

    NASA Technical Reports Server (NTRS)

    Phan, M.; Juang, J.-N.; Longman, R. W.

    1990-01-01

    This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.

  1. A neural fuzzy controller learning by fuzzy error propagation

    NASA Technical Reports Server (NTRS)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  2. Can we (control) Engineer the degree learning process?

    NASA Astrophysics Data System (ADS)

    White, A. S.; Censlive, M.; Neilsen, D.

    2014-07-01

    This paper investigates how control theory could be applied to learning processes in engineering education. The initial point for the analysis is White's Double Loop learning model of human automation control modified for the education process where a set of governing principals is chosen, probably by the course designer. After initial training the student decides unknowingly on a mental map or model. After observing how the real world is behaving, a strategy to achieve the governing variables is chosen and a set of actions chosen. This may not be a conscious operation, it maybe completely instinctive. These actions will cause some consequences but not until a certain time delay. The current model is compared with the work of Hollenbeck on goal setting, Nelson's model of self-regulation and that of Abdulwahed, Nagy and Blanchard at Loughborough who investigated control methods applied to the learning process.

  3. Enhancing Hebbian Learning to Control Brain Oscillatory Activity.

    PubMed

    Soekadar, Surjo R; Witkowski, Matthias; Birbaumer, Niels; Cohen, Leonardo G

    2015-09-01

    Sensorimotor rhythms (SMR, 8-15 Hz) are brain oscillations associated with successful motor performance, imagery, and imitation. Voluntary modulation of SMR can be used to control brain-machine interfaces (BMI) in the absence of any physical movements. The mechanisms underlying acquisition of such skill are unknown. Here, we provide evidence for a causal link between function of the primary motor cortex (M1), active during motor skill learning and retention, and successful acquisition of abstract skills such as control over SMR. Thirty healthy participants were trained on 5 consecutive days to control SMR oscillations. Each participant was randomly assigned to one of 3 groups that received either 20 min of anodal, cathodal, or sham transcranial direct current stimulation (tDCS) over M1. Learning SMR control across training days was superior in the anodal tDCS group relative to the other 2. Cathodal tDCS blocked the beneficial effects of training, as evidenced with sham tDCS. One month later, the newly acquired skill remained superior in the anodal tDCS group. Thus, application of weak electric currents of opposite polarities over M1 differentially modulates learning SMR control, pointing to this primary cortical region as a common substrate for acquisition of physical motor skills and learning to control brain oscillatory activity. PMID:24626608

  4. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation

    PubMed Central

    Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting. PMID:25729347

  5. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.

    PubMed

    Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting. PMID:25729347

  6. US ITER Moving Forward

    ScienceCinema

    US ITER / ORNL

    2012-03-16

    US ITER Project Manager Ned Sauthoff, joined by Wayne Reiersen, Team Leader Magnet Systems, and Jan Berry, Team Leader Tokamak Cooling System, discuss the U.S.'s role in the ITER international collaboration.

  7. Executive control and learning pattern on the CVLT.

    PubMed

    Lebowitz, Brian K; Touradji, Pegah; Jonen, Lynn; Belanger, Heather G; Curtiss, Glenn; Vanderploeg, Rodney D

    2006-10-01

    We evaluated a 23 year-old man after recovery from encephalitis. In contrast to the expected pattern of increasingly better acquisition across the 5 learning trials of the California Verbal Learning Test (CVLT-2), he produced a "J-shaped" curve (Trials 1-5: 8,6,6,9,11). Because he also demonstrated excessive levels of proactive interference as well as poor divided attention, we hypothesized that his atypical learning pattern was due to a build-up of proactive interference secondary to executive dyscontrol. Using a large sample of 4462 healthy adult men, we identified four groups exhibiting various learning patterns. We found that a learning pattern similar to this patient (i.e., a drop after trial 1 followed by recovery) was rare (1.1% of the sample). Individuals with this learning pattern demonstrated increased perseverative responses, as well as greater difficulty maintaining cognitive set on the Wisconsin Card Sorting Test, decreased attentional control on the Paced Auditory Serial Addition Test, and greater levels of proactive interference on the CVLT. Taken together, the results of the study suggest that an early drop, followed by a recovery in learning trial performance, is associated with executive dyscontrol. PMID:16840246

  8. Neuromorphic learning of continuous-valued mappings from noise-corrupted data. Application to real-time adaptive control

    NASA Technical Reports Server (NTRS)

    Troudet, Terry; Merrill, Walter C.

    1990-01-01

    The ability of feed-forward neural network architectures to learn continuous valued mappings in the presence of noise was demonstrated in relation to parameter identification and real-time adaptive control applications. An error function was introduced to help optimize parameter values such as number of training iterations, observation time, sampling rate, and scaling of the control signal. The learning performance depended essentially on the degree of embodiment of the control law in the training data set and on the degree of uniformity of the probability distribution function of the data that are presented to the net during sequence. When a control law was corrupted by noise, the fluctuations of the training data biased the probability distribution function of the training data sequence. Only if the noise contamination is minimized and the degree of embodiment of the control law is maximized, can a neural net develop a good representation of the mapping and be used as a neurocontroller. A multilayer net was trained with back-error-propagation to control a cart-pole system for linear and nonlinear control laws in the presence of data processing noise and measurement noise. The neurocontroller exhibited noise-filtering properties and was found to operate more smoothly than the teacher in the presence of measurement noise.

  9. New connectionist control structure for fast robot dynamic learning

    NASA Astrophysics Data System (ADS)

    Katic, Dusko; Vukobratovic, Miomir

    1992-09-01

    A major objective in this paper is the application of connectionist architectures for fast and robust on-line learning of dynamic relations used in robot control at the executive hierarchical level. The proposed new connectionist robot controllers as a new feature use decomposition of robot dynamics. In this way, this method enables the training of neural networks on the simpler input/output relations with significant reduction of learning time. The proposed controller structure comprises a form of intelligent feedforward control in the frame of decentralized control algorithm with feedback-error learning method. The other important features of these new algorithms are fast and robust convergence properties, because the problem of adjusting the weights of internal hidden units is considered as a problem of estimating parameters by recursive least squares method. From simulation examples of robot trajectory tracking it is shown that when a sufficiently trained network is desired the learning speed of the proposed algorithms is faster than that of the traditional back propagation algorithm.

  10. Learning Switching Control: A Tank Level-Control Exercise

    ERIC Educational Resources Information Center

    Pasamontes, M.; Alvarez, J. D.; Guzman, J. L.; Berenguel, M.

    2012-01-01

    A key topic in multicontroller strategies is the mechanism for switching between controllers, depending on the current operating point. The objective of the switching mechanism is to keep the control action coherent. To help students understand the switching strategy involved in multicontroller schema and the relationship between the system…

  11. Patients with Parkinson's Disease Learn to Control Complex Systems via Procedural as Well as Non-Procedural Learning

    ERIC Educational Resources Information Center

    Osman, Magda; Wilkinson, Leonora; Beigi, Mazda; Castaneda, Cristina Sanchez; Jahanshahi, Marjan

    2008-01-01

    The striatum is considered to mediate some forms of procedural learning. Complex dynamic control (CDC) tasks involve an individual having to make a series of sequential decisions to achieve a specific outcome (e.g. learning to operate and control a car), and they involve procedural learning. The aim of this study was to test the hypothesis that…

  12. Active route learning in virtual environments: disentangling movement control from intention, instruction specificity, and navigation control.

    PubMed

    von Stülpnagel, Rul; Steffens, Melanie C

    2013-09-01

    Active navigation research examines how physiological and psychological involvement in navigation benefits spatial learning. However, existing conceptualizations of active navigation comprise separable, distinct factors. This research disentangles the contributions of movement control (i.e., self-contained vs. observed movement) as a central factor from learning intention (Experiment 1), instruction specificity and instruction control (Experiment 2), as well as navigation control (Experiment 3) to spatial learning in virtual environments. We tested the effects of these factors on landmark recognition (landmark knowledge), tour-integration and route navigation (route knowledge). Our findings suggest that movement control leads to robust advantages in landmark knowledge as compared to observed movement. Advantages in route knowledge do not depend on learning intention, but on the need to elaborate spatial information. Whenever the necessary level of elaboration is assured for observed movement, too, the development of route knowledge is not inferior to that for self-contained movement. PMID:22922991

  13. On-line controlled documents: Lessons learned

    SciTech Connect

    Cochrell, R.C.; Steele, C.M.

    1995-06-01

    Placing Controlled Documents on-line on a computer network seems like the solution to many problems, one being distribution, with a path toward a paperless office. However, many problems presented themselves as we were designing the system and placing the documents on-line. Although we planned and established a Process Management Team to help work out the bugs, we still encountered many obstacles in the process. This presentation will cover the ``trials and tribulations`` of placing Controlled Documents on a computer network at three different sites. We will discuss the process we went through, the problems we encountered, the software we used, and how we got management to buy into the process.

  14. ITER LHe Plants Parallel Operation

    NASA Astrophysics Data System (ADS)

    Fauve, E.; Bonneton, M.; Chalifour, M.; Chang, H.-S.; Chodimella, C.; Monneret, E.; Vincent, G.; Flavien, G.; Fabre, Y.; Grillot, D.

    The ITER Cryogenic System includes three identical liquid helium (LHe) plants, with a total average cooling capacity equivalent to 75 kW at 4.5 K.The LHe plants provide the 4.5 K cooling power to the magnets and cryopumps. They are designed to operate in parallel and to handle heavy load variations.In this proceedingwe will describe the presentstatusof the ITER LHe plants with emphasis on i) the project schedule, ii) the plantscharacteristics/layout and iii) the basic principles and control strategies for a stable operation of the three LHe plants in parallel.

  15. Lessons Learned on the Development and Manufacture of Internal-Tin Nb3Sn Strand from Work on ITER CSMC and Other Fusion and HEP Applications

    SciTech Connect

    Gregory, E.

    2004-06-28

    For many years the fusion community provided the main incentive for the development of internal-tin Nb3Sn. Several tonnes of moderate current density conductor with low AC losses were supplied for the U.S. Section of ITER CSMC. When the US abandoned the ITER project in 1998, the manufacturers began to concentrate on higher current density conductors for High Energy Physics. While there are significant differences between the strands for these two different applications, development in both areas has sufficient work in common to be of benefit to both communities. As the US reconsiders its position on ITER, the paper reviews some of the past work and the present state of development in both fields.

  16. Language Learning and Control in Monolinguals and Bilinguals

    ERIC Educational Resources Information Center

    Bartolotti, James; Marian, Viorica

    2012-01-01

    Parallel language activation in bilinguals leads to competition between languages. Experience managing this interference may aid novel language learning by improving the ability to suppress competition from known languages. To investigate the effect of bilingualism on the ability to control native-language interference, monolinguals and bilinguals…

  17. Adolescents' Communication Styles and Learning about Birth Control.

    ERIC Educational Resources Information Center

    De Pietro, Rocco; Allen, Richard L.

    1984-01-01

    Identified predictors of birth control knowledge resulting from interactant or noninteractant communication styles in 100 adolescents who read a magazine on human sexuality. Data suggested that the interactant style was most beneficial for new learning. Gender and the presence of siblings in the home were important moderators. (JAC)

  18. Fuzzy reinforcement learning control for compliance tasks of robotic manipulators.

    PubMed

    Tzafestas, S G; Rigatos, G G

    2002-01-01

    A fuzzy reinforcement learning (FRL) scheme which is based on the principles of sliding-mode control and fuzzy logic is proposed. The FRL uses only immediate reward. Sufficient conditions for the convergence of the FRL to the optimal task performance are studied. The validity of the method is tested through simulation examples of a robot which deburrs a metal surface. PMID:18238109

  19. Performances of the fractal iterative method with an internal model control law on the ESO end-to-end ELT adaptive optics simulator

    NASA Astrophysics Data System (ADS)

    Béchet, C.; Le Louarn, M.; Tallon, M.; Thiébaut, É.

    2008-07-01

    Adaptive Optics systems under study for the Extremely Large Telescopes gave rise to a new generation of algorithms for both wavefront reconstruction and the control law. In the first place, the large number of controlled actuators impose the use of computationally efficient methods. Secondly, the performance criterion is no longer solely based on nulling residual measurements. Priors on turbulence must be inserted. In order to satisfy these two requirements, we suggested to associate the Fractal Iterative Method for the estimation step with an Internal Model Control. This combination has now been tested on an end-to-end adaptive optics numerical simulator at ESO, named Octopus. Results are presented here and performance of our method is compared to the classical Matrix-Vector Multiplication combined with a pure integrator. In the light of a theoretical analysis of our control algorithm, we investigate the influence of several errors contributions on our simulations. The reconstruction error varies with the signal-to-noise ratio but is limited by the use of priors. The ratio between the system loop delay and the wavefront coherence time also impacts on the reachable Strehl ratio. Whereas no instabilities are observed, correction quality is obviously affected at low flux, when subapertures extinctions are frequent. Last but not least, the simulations have demonstrated the robustness of the method with respect to sensor modeling errors and actuators misalignments.

  20. Structural learning in feedforward and feedback control

    PubMed Central

    Diedrichsen, Jörn

    2012-01-01

    For smooth and efficient motor control, the brain needs to make fast corrections during the movement to resist possible perturbations. It also needs to adapt subsequent movements to improve future performance. It is important that both feedback corrections and feedforward adaptation need to be made based on noisy and often ambiguous sensory data. Therefore, the initial response of the motor system, both for online corrections and adaptive responses, is guided by prior assumptions about the likely structure of perturbations. In the context of correcting and adapting movements perturbed by a force field, we asked whether these priors are hard wired or whether they can be modified through repeated exposure to differently shaped force fields. We found that both feedback corrections to unexpected perturbations and feedforward adaptation to a new force field changed, such that they were appropriate to counteract the type of force field that participants had experienced previously. We then investigated whether these changes were driven by a common mechanism or by two separate mechanisms. Participants experienced force fields that were either temporally consistent, causing sustained adaptation, or temporally inconsistent, causing little overall adaptation. We found that the consistent force fields modified both feedback and feedforward responses. In contrast, the inconsistent force field modified the temporal shape of feedback corrections but not of the feedforward adaptive response. These results indicate that responses to force perturbations can be modified in a structural manner and that these modifications are at least partly dissociable for feedback and feedforward control. PMID:22896725

  1. Multi Car Elevator Control by using Learning Automaton

    NASA Astrophysics Data System (ADS)

    Shiraishi, Kazuaki; Hamagami, Tomoki; Hirata, Hironori

    We study an adaptive control technique for multi car elevators (MCEs) by adopting learning automatons (LAs.) The MCE is a high performance and a near-future elevator system with multi shafts and multi cars. A strong point of the system is that realizing a large carrying capacity in small shaft area. However, since the operation is too complicated, realizing an efficient MCE control is difficult for top-down approaches. For example, “bunching up together" is one of the typical phenomenon in a simple traffic environment like the MCE. Furthermore, an adapting to varying environment in configuration requirement is a serious issue in a real elevator service. In order to resolve these issues, having an autonomous behavior is required to the control system of each car in MCE system, so that the learning automaton, as the solutions for this requirement, is supposed to be appropriate for the simple traffic control. First, we assign a stochastic automaton (SA) to each car control system. Then, each SA varies its stochastic behavior distributions for adapting to environment in which its policy is evaluated with each passenger waiting times. That is LA which learns the environment autonomously. Using the LA based control technique, the MCE operation efficiency is evaluated through simulation experiments. Results show the technique enables reducing waiting times efficiently, and we confirm the system can adapt to the dynamic environment.

  2. Nonlinear Performance Seeking Control using Fuzzy Model Reference Learning Control and the Method of Steepest Descent

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    1997-01-01

    Performance Seeking Control (PSC) attempts to find and control the process at the operating condition that will generate maximum performance. In this paper a nonlinear multivariable PSC methodology will be developed, utilizing the Fuzzy Model Reference Learning Control (FMRLC) and the method of Steepest Descent or Gradient (SDG). This PSC control methodology employs the SDG method to find the operating condition that will generate maximum performance. This operating condition is in turn passed to the FMRLC controller as a set point for the control of the process. The conventional SDG algorithm is modified in this paper in order for convergence to occur monotonically. For the FMRLC control, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for effective tuning of the FMRLC controller.

  3. Attention control learning in the decision space using state estimation

    NASA Astrophysics Data System (ADS)

    Gharaee, Zahra; Fatehi, Alireza; Mirian, Maryam S.; Nili Ahmadabadi, Majid

    2016-05-01

    The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.

  4. Human-level control through deep reinforcement learning

    NASA Astrophysics Data System (ADS)

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-01

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

  5. Human-level control through deep reinforcement learning.

    PubMed

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-26

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks

  6. Decentralized reinforcement-learning control and emergence of motion patterns

    NASA Astrophysics Data System (ADS)

    Svinin, Mikhail; Yamada, Kazuyaki; Okhura, Kazuhiro; Ueda, Kanji

    1998-10-01

    In this paper we propose a system for studying emergence of motion patterns in autonomous mobile robotic systems. The system implements an instance-based reinforcement learning control. Three spaces are of importance in formulation of the control scheme. They are the work space, the sensor space, and the action space. Important feature of our system is that all these spaces are assumed to be continuous. The core part of the system is a classifier system. Based on the sensory state space analysis, the control is decentralized and is specified at the lowest level of the control system. However, the local controllers are implicitly connected through the perceived environment information. Therefore, they constitute a dynamic environment with respect to each other. The proposed control scheme is tested under simulation for a mobile robot in a navigation task. It is shown that some patterns of global behavior--such as collision avoidance, wall-following, light-seeking--can emerge from the local controllers.

  7. Learning and Control Model of the Arm for Loading

    NASA Astrophysics Data System (ADS)

    Kim, Kyoungsik; Kambara, Hiroyuki; Shin, Duk; Koike, Yasuharu

    We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.

  8. The Effectiveness of E-Learning Systems: A Review of the Empirical Literature on Learner Control

    ERIC Educational Resources Information Center

    Sorgenfrei, Christian; Smolnik, Stefan

    2016-01-01

    E-learning systems are considerably changing education and organizational training. With the advancement of online-based learning systems, learner control over the instructional process has emerged as a decisive factor in technology-based forms of learning. However, conceptual work on the role of learner control in e-learning has not advanced…

  9. ECRH System For ITER

    SciTech Connect

    Darbos, C.; Henderson, M.; Gandini, F.; Albajar, F.; Bomcelli, T.; Heidinger, R.; Saibene, G.; Chavan, R.; Goodman, T.; Hogge, J. P.; Sauter, O.; Denisov, G.; Farina, D.; Kajiwara, K.; Kasugai, A.; Kobayashi, N.; Oda, Y.; Ramponi, G.

    2009-11-26

    A 26 MW Electron Cyclotron Heating and Current Drive (EC H and CD) system is to be installed for ITER. The main objectives are to provide, start-up assist, central H and CD and control of MHD activity. These are achieved by a combination of two types of launchers, one located in an equatorial port and the second type in four upper ports. The physics applications are partitioned between the two launchers, based on the deposition location and driven current profiles. The equatorial launcher (EL) will access from the plasma axis to mid radius with a relatively broad profile useful for central heating and current drive applications, while the upper launchers (ULs) will access roughly the outer half of the plasma radius with a very narrow peaked profile for the control of the Neoclassical Tearing Modes (NTM) and sawtooth oscillations. The EC power can be switched between launchers on a time scale as needed by the immediate physics requirements. A revision of all injection angles of all launchers is under consideration for increased EC physics capabilities while relaxing the engineering constraints of both the EL and ULs. A series of design reviews are being planned with the five parties (EU, IN, JA, RF, US) procuring the EC system, the EC community and ITER Organization (IO). The review meetings qualify the design and provide an environment for enhancing performances while reducing costs, simplifying interfaces, predicting technology upgrades and commercial availability. In parallel, the test programs for critical components are being supported by IO and performed by the Domestic Agencies (DAs) for minimizing risks. The wide participation of the DAs provides a broad representation from the EC community, with the aim of collecting all expertise in guiding the EC system optimization. Still a strong relationship between IO and the DA is essential for optimizing the design of the EC system and for the installation and commissioning of all ex-vessel components when several

  10. ECRH System For ITER

    NASA Astrophysics Data System (ADS)

    Darbos, C.; Henderson, M.; Albajar, F.; Bigelow, T.; Bomcelli, T.; Chavan, R.; Denisov, G.; Farina, D.; Gandini, F.; Heidinger, R.; Goodman, T.; Hogge, J. P.; Kajiwara, K.; Kasugai, A.; Kern, S.; Kobayashi, N.; Oda, Y.; Ramponi, G.; Rao, S. L.; Rasmussen, D.; Rzesnicki, T.; Saibene, G.; Sakamoto, K.; Sauter, O.; Scherer, T.; Strauss, D.; Takahashi, K.; Zohm, H.

    2009-11-01

    A 26 MW Electron Cyclotron Heating and Current Drive (EC H&CD) system is to be installed for ITER. The main objectives are to provide, start-up assist, central H&CD and control of MHD activity. These are achieved by a combination of two types of launchers, one located in an equatorial port and the second type in four upper ports. The physics applications are partitioned between the two launchers, based on the deposition location and driven current profiles. The equatorial launcher (EL) will access from the plasma axis to mid radius with a relatively broad profile useful for central heating and current drive applications, while the upper launchers (ULs) will access roughly the outer half of the plasma radius with a very narrow peaked profile for the control of the Neoclassical Tearing Modes (NTM) and sawtooth oscillations. The EC power can be switched between launchers on a time scale as needed by the immediate physics requirements. A revision of all injection angles of all launchers is under consideration for increased EC physics capabilities while relaxing the engineering constraints of both the EL and ULs. A series of design reviews are being planned with the five parties (EU, IN, JA, RF, US) procuring the EC system, the EC community and ITER Organization (IO). The review meetings qualify the design and provide an environment for enhancing performances while reducing costs, simplifying interfaces, predicting technology upgrades and commercial availability. In parallel, the test programs for critical components are being supported by IO and performed by the Domestic Agencies (DAs) for minimizing risks. The wide participation of the DAs provides a broad representation from the EC community, with the aim of collecting all expertise in guiding the EC system optimization. Still a strong relationship between IO and the DA is essential for optimizing the design of the EC system and for the installation and commissioning of all ex-vessel components when several teams