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Neural-network-based optimal tracking control scheme for a class of unknown discrete-time nonlinear systems using iterative ADP algorithm

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NEUROCOMPUTING 2014年 125卷 46-56页

作者： Huang, Yuzhu Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, an optimal tracking control scheme is proposed for a class of unknown discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) algorithm. First, in order to obtain the dynamics of the system, an identifier is constructed by a three-layer feedforward neural network (NN). Second, a feedforward neuro-controller is designed to get the desired control input of the system. Third, via system transformation, the original tracking problem is transformed into a regulation problem with respect to the state tracking error. Then, the iterative ADP algorithm based on heuristic dynamic programming is introduced to deal with the regulation problem with convergence analysis. In this scheme, feedforward NNs are used as parametric structures for facilitating the implementation of the iterative algorithm. Finally, simulation results are also presented to demonstrate the effectiveness of the proposed scheme. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Adaptive dynamic programming Convergence analysis Heuristic dynamic programming Neural networks Optimal tracking control Reinforcement learning

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Convergence analysis and application of fuzzy-HDP for nonlinear discrete-time HJB systems

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NEUROCOMPUTING 2015年第PartA期149卷 124-131页

作者： Zhu, Yuanheng Zhao, Dongbin Liu, Derong Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

In this paper, a type of fuzzy system structure is applied to heuristic dynamic programming (HDP) algorithm to solve nonlinear discrete-time Hamilton-Jacobi-Bellman (DT-HJB) problems. The fuzzy system here is adopted as a 0-order T-S fuzzy system using triangle membership functions (MFs). The convergence of HDP and approximability of the multivariate 0-order T-S fuzzy system is analyzed in this paper. It is derived that the cost function and control policy of HDP can be iterated to the DT-1-1113 solution and optimal policy. The multivariate 0-order T-S (Tanaka-Sugeno) fuzzy system using triangle MFs is proven as a universal approximator, to guarantee the convergence of the Fuzzy-HDP mechanism. Some simulations are implemented to observe the performance of the proposed method both in mathematical solution and practical issue. It is concluded that Fuzzy-HDP outperforms traditional optimal control in more complex systems. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Discrete-time nonlinear system T-S fuzzy system HDP

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Online approximate optimal control for affine non-linear systems with unknown internal dynamics using adaptive dynamic programming

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IET control THEORY AND APPLICATIONS 2014年第16期8卷 1676-1688页

作者： Yang, Xiong Liu, Derong Wei, Qinglai Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this study, a novel online adaptive dynamic programming (ADP)-based algorithm is developed for solving the optimal control problem of affine non-linear continuous-time systems with unknown internal dynamics. The present algorithm employs an observer-critic architecture to approximate the Hamilton-Jacobi-Bellman equation. Two neural networks (NNs) are used in this architecture: an NN state observer is constructed to estimate the unknown system dynamics and a critic NN is designed to derive the optimal control instead of typical action-critic dual networks employed in traditional ADP algorithms. Based on the developed architecture, the observer NN and the critic NN are tuned simultaneously. Meanwhile, unlike existing tuning laws for the critic, the newly developed critic update rule not only ensures convergence of the critic to the optimal control but also guarantees stability of the closed-loop system. No initial stabilising control is required, and by using recorded and instantaneous data simultaneously for the adaptation of the critic, the restrictive persistence of excitation condition is relaxed. In addition, Lyapunov direct method is utilised to demonstrate the uniform ultimate boundedness of the weights of the observer NN and the critic NN. Finally, an example is provided to verify the effectiveness of the present approach.

关键词： critic Affine Optimal control System dynamics Observers In-law uniform ultimate boundedness dynamic programming

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Toward Patients' Motion Intention Recognition: Dynamics Modeling and Identification of iLeg-An LLRR Under Motion Constraints

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2016年第7期46卷 980-992页

作者： Wang, Weiqun Hou, Zeng-Guang Cheng, Long Tong, Lina Peng, Liang Peng, Long Tan, Min Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

In order to implement model-based recognition of human motion intention, dynamics modeling and identification of a lower limb rehabilitation robot named iLeg is investigated. Due to the relatively strong motion constraints, the traditional identification methods become insufficient for iLeg in three aspects: 1) the coupling factors among joints have not been considered in the traditional joint friction models, which makes the structural error and the torque estimation errors relatively large;2) because of the small and complicated feasible region caused by the motion constraints, the traditional initialization strategy, for searching the valid initial solutions of the optimization problem for the exciting trajectories, becomes very inefficient;and 3) the condition number of the observation matrix, calculated from the preliminary dynamic model and the associated optimized exciting trajectory, is too large for the identification, and, however, further reduction of the condition number has not been considered in the literature. Therefore, corresponding contributions are presented to overcome the limitation. First, the coupling factors among joints are considered in the joint friction model by using the Palmgren empirical formulation and a polynomial fitting method. Then, an indirectly generating strategy is designed, by which the valid initial solutions of the optimization problem can be found with good efficiency. Moreover, a recursive optimization method based on the optimization of the dynamic model and the exciting trajectories, is proposed to further reduce the condition number. Finally, the performance of the proposed methods is demonstrated by several experiments.

关键词： Friction model lower limb rehabilitation robot (LLRR) motion intention recognition optimization of exciting trajectories recursive optimization

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Neuro-optimal control for a class of unknown nonlinear dynamic systems using SN-DHP technique

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NEUROCOMPUTING 2013年 121卷 218-225页

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

In this paper, the adaptive dynamic programming (ADP) approach is utilized to design a neural-network-based optimal controller for a class of unknown discrete-time nonlinear systems with quadratic cost function. To begin with, a neural network identifier is constructed to learn the unknown dynamic system with stability proof. Then, the iterative ADP algorithm is developed to handle the nonlinear optimal control problem with convergence analysis. Moreover, the single network dual heuristic dynamic programming (SN-DHP) technique, which eliminates the use of action network, is introduced to implement the iterative ADP algorithm. Finally, two simulation examples are included to illustrate the effectiveness of the present approach. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming Neural networks Optimal control Reinforcement learning

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Adaptive optimal control for a class of continuous-time affine nonlinear systems with unknown internal dynamics

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NEURAL COMPUTING & APPLICATIONS 2013年第7-8期23卷 1843-1850页

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

This paper develops an online algorithm based on policy iteration for optimal control with infinite horizon cost for continuous-time nonlinear systems. In the present method, a discounted value function is employed, which is considered to be a more general case for optimal control problems. Meanwhile, without knowledge of the internal system dynamics, the algorithm can converge uniformly online to the optimal control, which is the solution of the modified Hamilton-Jacobi-Bellman equation. By means of two neural networks, the algorithm is able to find suitable approximations of both the optimal control and the optimal cost. The uniform convergence to the optimal control is shown, guaranteeing the stability of the nonlinear system. A simulation example is provided to illustrate the effectiveness and applicability of the present approach.

关键词： Adaptive dynamic programming Reinforcement learning Policy iteration Adaptive optimal control Neural network Online control Nonlinear system

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Numerical adaptive learning control scheme for discrete-time non-linear systems

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IET control THEORY AND APPLICATIONS 2013年第11期7卷 1472-1486页

作者： Wei, Qinglai Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this study, a novel numerical adaptive learning control scheme based on adaptive dynamic programming (ADP) algorithm is developed to solve numerical optimal control problems for infinite horizon discrete-time non-linear systems. Using the numerical controller, the domain of definition is constrained to a discrete set that makes the approximation errors always exist between the numerical controls and the accurate ones. Convergence analysis of the numerical iterative ADP algorithm is developed to show that the numerical iterative controls can make the iterative performance index functions converge to the greatest lower bound of all performance indices within a finite error bound under some mild assumptions. The stability properties of the system under the numerical iterative controls are proved, which allow the present iterative ADP algorithm to be implemented both on-line and off-line. Finally, two simulation examples are given to illustrate the performance of the present method.

关键词： Domain of Definition Numerical infinite horizon Self tuning Numerical controls Iterative discrete set System stability Converge learning control Approximation error discrete-time performance indicators

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Development of an Underwater Manipulator and Its Free-Floating Autonomous Operation

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2016年第2期21卷 815-824页

作者： Wang, Yu Wang, Shuo Wei, Qingping Tan, Min Zhou, Chao Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper addresses the novel design of an underwater manipulator with a lightweight multilink structure and its free-floating autonomous operation. The concept design reduces the coupling between the manipulator and the vehicle efficiently, even in the case where the vehicle weight in air is not significantly greater than the manipulator weight. The specific implementation of the mechanical structure is elaborated. Moreover, a closed-loop control system based on binocular vision is proposed for underwater manipulation. In the end, experimental results demonstrate that the conceived underwater manipulator can accomplish the autonomous operation quickly.

关键词： Autonomous operation free-floating manipulation underwater manipulator UVMS

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Model-Free Optimal control for Affine Nonlinear systems With Convergence Analysis

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2015年第4期12卷 1461-1468页

作者： Zhao, Dongbin Xia, Zhongpu Wang, Ding Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, a self-learning control scheme is proposed for the infinite horizon optimal control of affine nonlinear systems based on the action dependent heuristic dynamic programming algorithm. The policy iteration technique is introduced to derive the optimal control policy with feasibility and convergence analysis. It shows that the "greedy" control action for each state is uniquely existent, the learned control policy after each policy iteration is admissible, and the optimal control policy is able to be obtained. Two three-layer perceptron neural networks are employed to implement the scheme. The critic network is trained by a novel rule to conform to the Bellman equation, and the action network is trained to yield a better control policy. Both training processes alternate until the optimal control policy is achieved. Two simulation examples are provided to validate the effectiveness of the approach.

关键词： Action dependent heuristic dynamic programming adaptive dynamic programming model-free optimal control neural networks policy iteration

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Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints

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IET control THEORY AND APPLICATIONS 2013年第17期7卷 2037-2047页

作者： Yang, Xiong Liu, Derong Huang, Yuzhu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this study, an online adaptive optimal control scheme is developed for solving the infinite-horizon optimal control problem of uncertain non-linear continuous-time systems with the control policy having saturation constraints. A novel identifier-critic architecture is presented to approximate the Hamilton-Jacobi-Bellman equation using two neural networks (NNs): an identifier NN is used to estimate the uncertain system dynamics and a critic NN is utilised to derive the optimal control instead of typical action-critic dual networks employed in reinforcement learning. Based on the developed architecture, the identifier NN and the critic NN are tuned simultaneously. Meanwhile, unlike initial stabilising control indispensable in policy iteration, there is no special requirement imposed on the initial control. Moreover, by using Lyapunov's direct method, the weights of the identifier NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. Finally, an example is provided to demonstrate the effectiveness of the present approach.

关键词： adaptive control approximation theory closed loop systems continuous time systems Lyapunov methods neurocontrollers nonlinear control systems optimal control robust control uncertain systems neural network-based online adaptive optimal control uncertain nonlinear continuous-time systems control constraints infinite-horizon optimal control problem control policy saturation constraints identifier-critic architecture Hamilton-Jacobi-Bellman equation approximation uncertain system dynamics critic NN action-critic dual networks reinforcement learning identifier NN policy iteration LyapunovaEuros direct method closed loop system stability

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