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Decentralized Stabilization for a Class of Continuous-Time Nonlinear Interconnected Systems Using Online learning Optimal Control Approach

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2014年第2期25卷 418-428页

作者： Liu, Derong Wang, Ding Li, Hongliang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

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.

关键词： adaptive dynamic programming decentralized control large-scale systems neural networks nonlinear interconnected systems optimal control policy iteration reinforcement learning

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Impact of signal transmission delays on power system damping control using heuristic dynamic programming

Impact of signal transmission delays on power system damping...

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2014 ieee symposium Series on Computational Intelligence, ieee SSCI 2014 - 2014 ieee symposium on Computational Intelligence Applications in Smart Grid, CIASG 2014

作者： Tang, Yufei Zhong, Xiangnan Ni, Zhen Yan, Jun He, Haibo Department of Electrical Computer and Biomedical Engineering University of Rhode Island KingstonRI02881 United States

ISBN: (纸本)9781479945474

In this paper, the impact of signal transmission delays on static VAR compensator (SVC) based power system damping control using reinforcement learning is investigated. The SVC is used to damp low-frequency oscillation between interconnected power systems under fault conditions, where measured signals from remote areas are first collected and then transmitted to the controller as the inputs. Inevitable signal transmission delays are introduced into such design that will degrade the dynamic performance of SVC and in the worst case, cause system instability. The adopted reinforcement learning algorithm, called goal representation heuristic dynamic programming (GrHDP), is employed to design the SVC controller. Impact of signal transmission delays on the adopted controller is investigated with fully transient model based time-domain simulation in Matlab/Simulink environment. The simulation results on a four-machine two-area benchmark system with SVC demonstrate the effectiveness of the adopted algorithm on damping control and the impact of signal transmission delays. © 2014 ieee.

关键词： Controllers

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reinforcement learning Based Controller Synthesis for Flexible Aircraft Wings

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ieee/CAA Journal of Automatica Sinica 2014年第4期1卷 435-448页

作者： Manoj Kumar Karthikeyan Rajagopal Sivasubramanya Nadar Balakrishnan Nhan T.Nguyen the Missouri University of Science&Technology the NASA Ames Research Center Moffet Field

Aeroelastic study of flight vehicles has been a subject of great interest and research in the last several years. Aileron reversal and flutter related problems are due in part to the elasticity of a typical airplane. Structural dynamics of an aircraft wing due to its aeroelastic nature are characterized by partial differential equations. Controller design for these systems is very complex as compared to lumped parameter systems defined by ordinary differential equations. In this paper, a stabilizing statefeedback controller design approach is presented for the heave dynamics of a wing-fuselage model. In this study, a continuous actuator in the spatial domain is assumed. A control methodology is developed by combining the technique of “proper orthogonal decomposition” and approximate dynamic programming. The proper orthogonal decomposition technique is used to obtain a low-order nonlinear lumped parameter model of the infinite dimensional system. Then a near optimal controller is designed using the single-network-adaptive-critic technique. Furthermore,to add robustness to the nominal single-network-adaptive-critic controller against matched uncertainties, an identifier based adaptive controller is proposed. Simulation results demonstrate the effectiveness of the single-network-adaptive-critic controller augmented with adaptive controller for infinite dimensional systems.

关键词： Adaptation models adaptive control Aircraft Atmospheric modeling Cost function learning (artificial intelligence) Method of moments Optimal control Single-network-adaptive-critic adaptive control adaptive critic flexible wing partial differential equation proper orthogonal decomposition uncertainty METHOD OF MOMENTS flexible airfoils Optimal control Wings adaptive controller learning Atmospheric models Partial differential equations Differential equations Control Methods adaptive control Aircraft flexible air vehicles Cost functions Adaptation models

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Event-Triggered reinforcement learning Approach for Unknown Nonlinear Continuous-Time System

Event-Triggered Reinforcement Learning Approach for Unknown ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhong, Xiangnan Ni, Zhen He, Haibo Xu, Xin Zhao, Dongbin Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Natl Univ Def Technol Coll Mechatron & Automat Changsha 410073 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781479914845

This paper provides an adaptive event-triggered method using adaptive dynamic programming (ADP) for the nonlinear continuous-time system. Comparing to the traditional method with fixed sampling period, the event-triggered method samples the state only when an event is triggered and therefore the computational cost is reduced. We demonstrate the theoretical analysis on the stability of the event-triggered method, and integrate it with the ADP approach. The system dynamics are assumed unknown. The corresponding ADP algorithm is given and the neural network techniques are applied to implement this method. The simulation results verify the theoretical analysis and justify the efficiency of the proposed event-triggered technique using the ADP approach.

关键词： dynamic programming

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Theoretical analysis of a reinforcement learning based switching scheme

Theoretical analysis of a reinforcement learning based switc...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Ali Heydari Mechanical Engineering Department South Dakota School of Mines and Technology Rapid City SD

A reinforcement learning based scheme for optimal switching with an infinite-horizon cost function is briefly proposed in this paper. Several theoretical questions are shown to arise regarding its convergence, optimality of the result, and continuity of the limit function, to be uniformly approximated using parametric function approximators. The main contribution of the paper is providing rigorous answers for the questions, where, sufficient conditions for convergence, optimality, and continuity are provided.

关键词： Switches Cost function Convergence Artificial neural networks Schedules Approximation methods Optimal control

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An adaptive dynamic programming algorithm to solve optimal control of uncertain nonlinear systems

An adaptive dynamic programming algorithm to solve optimal c...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Xiaohong Cui Yanhong Luo Huaguang Zhang School of Information Science and Engineering Northeastern University Shenyang Liaoning China

ISBN: (纸本)9781479945511

In this paper, an approximate optimal control method based on adaptive dynamic programming(ADP) is discussed for completely unknown nonlinear system. An online critic-action-identifier algorithm is developed using neural network systems, where the criticaction networks approximate the optimal value function and optimal control and the other two neural networks approximates the unknown system. Furthermore the adaptive tuning laws are given based on Lyapunov approach, which ensures the uniform ultimate bounded stability of the closed-loop system. Finally, the effectiveness is demonstrated by a simulation example.

关键词： Optimal control Artificial neural networks Mathematical model Equations Heuristic algorithms Function approximation

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RLS Algorithms and Convergence Analysis Method for Online DLQR Control Design via Heuristic dynamic programming 16

RLS Algorithms and Convergence Analysis Method for Online DL...

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16th UKSim-AMSS International Conference on Computer Modelling and Simulation (UKSim)

作者： Santos, Watson R. M. Queiroz, Jonathan A. Neto, Joao Viana da F. Rego, Patricia H. M. Santana, Ewaldo Andrade, Gustavo Univ Estadual Maranhao Fed Univ Maranhao Fed Inst Maranhao Embedded Syst & Intelligent Control Lab Sao Luis Maranhao Brazil

ISBN: (纸本)9781479949236

In this paper, a method to design online optimal policies that encompasses Hamilton-Jacobi-Bellman (HJB) equation solution approximation and heuristic dynamic programming (HDP) approach is proposed. Recursive least squares (RLS) algorithms are developed to approximate the HJB equation solution that is supported by a sequence of greedy policies. The proposal investigates the convergence properties of a family of RLS algorithms and its numerical complexity in the context of reinforcement learning and optimal control. The algorithms are computationally evaluated in an electric circuit model that represents an MIMO dynamic system. The results presented herein emphasize the convergence behaviour of the RLS, projection and Kaczmarz algorithms that are developed for online applications.

关键词： Recursive Least Squares Heuristic dynamic programming RLS Convergence MIMO dynamic Systems Optimal Control adaptive dynamic programming

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A data-based online reinforcement learning algorithm with high-efficient exploration

A data-based online reinforcement learning algorithm with hi...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Yuanheng Zhu Dongbin Zhao The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479945511

An online reinforcement learning algorithm is proposed in this paper to directly utilizes online data efficiently for continuous deterministic systems without system parameters. The dependence on some specific approximation structures is crucial to limit the wide application of online reinforcement learning algorithms. We utilize the online data directly with the kd-tree technique to remove this limitation. Moreover, we design the algorithm in the Probably Approximately Correct principle. Two examples are simulated to verify its good performance.

关键词： Approximation algorithms learning (artificial intelligence) Approximation methods Optimal control Upper bound Partitioning algorithms DC motors

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adaptive dynamic programming-based optimal tracking control for nonlinear systems using general value iteration

Adaptive dynamic programming-based optimal tracking control ...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Xiaofeng Lin Qiang Ding Weikai Kong Chunning Song Qingbao Huang School of Electrical Engineering Guangxi University Nanning China

ISBN: (纸本)9781479945511

For the optimal tracking control problem of affine nonlinear systems, a general value iteration algorithm based on adaptive dynamic programming is proposed in this paper. By system transformation, the optimal tracking problem is converted into the optimal regulating problem for the tracking error dynamics. Then, general value iteration algorithm is developed to obtain the optimal control with convergence analysis. Considering the advantages of echo state network, we use three echo state networks with levenberg-Marquardt (LM) adjusting algorithm to approximate the system, the cost function and the control law. A simulation example is given to demonstrate the effectiveness of the presented scheme.

关键词： Cost function Nonlinear systems Optimal control Trajectory dynamic programming Approximation algorithms

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ADP-based optimal control for a class of nonlinear discrete-time systems with inequality constraints

ADP-based optimal control for a class of nonlinear discrete-...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Yanhong Luo Geyang Xiao College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781479945511

In this paper, the adaptive dynamic programming (ADP) approach is utilized to design a neural-network-based optimal controller for a class of nonlinear discrete-time (DT) systems with inequality constraints. To begin with, the initial constrained optimal control problem is transformed into an infinite horizon optimal control problem by introducing the penalty function. Then, the iterative ADP algorithm is developed to handle the nonlinear optimal control problem with two neural networks. The two neural networks are aimed at generating the optimal cost and the optimal control policy respectively. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.

关键词： Optimal control Biological neural networks Nonlinear systems dynamic programming Cost function

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