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16th International Symposium on Neural Networks (ISNN)

作者： Dong, Bo An, Tianjiao Zhou, Fan Wang, Shenquan Jiang, Yulian Liu, Keping Liu, Fu Lu, Huiqiu Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Jilin Univ Dept Control Sci & Engn Changchun Peoples R China

ISBN: (纸本)9783030228088;9783030228071

In this paper, a decentralized robust zero-sum optimal control method is proposed for modular robot manipulators (MRMs) based on the adaptive dynamic programming (ADP) approach. The dynamic model of MRMs is formulated via joint torque feedback (JTF) technique that is deployed for each joint module, in which the local dynamic information is used to design the model compensation controller. An uncertainty decomposition-based robust control is developed to compensate the model uncertainties, and then the robust optimal control problem of MRMs with uncertain environments can be transformed into a two-player zero-sum optimal control one. According to the ADP algorithm, the Hamilton-Jacobi-Isaacs (HJI) equation is solved by constructing actioncritic neural networks (NNs) and then the approximate optimal control policy derivation is possible. Experiments are conducted to verify the effectiveness of the proposed method.

关键词： Modular robot manipulators adaptive dynamic programming Optimal control Zero-sum game

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Local Near-Optimal Control for Interconnected Systems with Time-Varying Delays 26th

Local Near-Optimal Control for Interconnected Systems with T...

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26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Wu, Qiuye Lin, Haowei Zhao, Bo Liu, Derong Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

ISBN: (纸本)9783030367114;9783030367107

In this paper, we develop a novel local near-optimal control (LNOC) for continuous-time (CT) time-varying delayed interconnected systems by observer-critic structure based on adaptive dynamic programming. A boundary function which is approximated by local state observer is presented to substitute the unknown interconnection. Through constructing a novel local value function, the LNOC is obtained by solving the local Hamilton-Jacobi-Bellman equation with the implementation of local critic neural network. By using the Lyapunov's direct method, the proposed LNOC ensures the closed-loop CT time-varying delayed interconnected systems to be asymptotically stable. The effectiveness of the proposed LNOC scheme is illustrated using numerical simulation.

关键词： adaptive dynamic programming Local near-optimal control Unknown time-varying delays Observer-critic architecture Interconnected nonlinear systems

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UCT-ADP Progressive Bias Algorithm for Solving Gomoku

UCT-ADP Progressive Bias Algorithm for Solving Gomoku

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Cao, Xu Lin, Yanghao Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (纸本)9781728124858

We combine adaptive dynamic programming (ADP), a reinforcement learning method and UCB applied to trees (UCT) algorithm with a more powerful heuristic function based on Progressive Bias method and two pruning strategies for a traditional board game Gomoku. For the adaptive dynamic programming part, we train a shallow forward neural network to give a quick evaluation of Gomoku board situations. UCT is a general approach in MCTS as a tree policy. Our framework use UCT to balance the exploration and exploitation of Gomoku game trees while we also apply powerful pruning strategies and heuristic function to re-select the available 2-adjacent grids of the state and use ADP instead of simulation to give estimated values of expanded nodes. Experiment result shows that this method can eliminate the search depth defect of the simulation process and converge to the correct value faster than single UCT. This approach can be applied to design new Gomoku AI and solve other Gomoku-like board game.

关键词： adaptive dynamic programming monte carlo tree search gomoku exponential heuristic progressive bias

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Neuro-Control for Continuous-Time Stochastic Nonlinear Systems via Online Policy Iteration Algorithm

Neuro-Control for Continuous-Time Stochastic Nonlinear Syste...

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第32届中国控制与决策会议

作者： Tianmin Zhou Jiaxu Hou HANDong Li Zengru Di Bo Zhao School of Systems Science Beijing Normal University

This paper is concerned with the neuro-control for continuous-time nonlinear systems subject to stochastic *** to the stochastic disturbance,the traditional value function in existing literature cannot meet the stochastic control problems,since mixed second partial derivatives are employed to construct modified value function of conditional *** solve the Hamilton-Jacobi-Bellman equation,a novel online policy iteration algorithm with an Ito correction term is developed with establishing a critic neural network to approximate the optimal value ***,the online optimal control can be obtained in a closed-loop *** closed-loop system is guaranteed to be stable in probability via Lyapunov’s direct ***,numerical example is provided to illustrate the effectiveness of the developed control method.

关键词： adaptive dynamic programming Reinforcement Learning Policy Iteration Stochastic Nonlinear

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A Neural Critic Learning Method for Nonlinear Decentralized Stabilization with Unknown Perturbation

A Neural Critic Learning Method for Nonlinear Decentralized ...

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第31届中国过程控制会议（CPCC 2020）

作者： HUO Yu WANG Ding QIAO Junfei Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology

In order to realize the robust decentralized stabilization of continuous-time nonlinear systems with general uncertainties, a neural-network-based online optimal control strategy is established, where the solution of the corresponding Hamilton-Jacobi-Bellman(HJB) equation is obtained. Specifically, as a necessary part of the control system, a neural network is involved with strong online learning ability. The novelty of this paper is that the well-known adaptive dynamic programming method is extended to deal with the nonlinear feedback control problem under uncertain and large-scale environment. First, the robust decentralized stabilization problem is transformed into the decentralized optimal control design problem. On this basis, a critic neural network is constructed to solve the modified HJB equation corresponding to the nominal system in an online form. Moreover, a novel updating rule compared to the traditional adaptive critic algorithms is proposed, which reduces the requirement of the initial stabilizing controller and brings in particular reinforcements to the adaptive critic control design. Finally, an example is included to show the feasibility and effectiveness of the obtained theoretical results.

关键词： adaptive dynamic programming dynamical uncertainty Neural networks Optimal control Robust decentralized stabilization

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Value-Iteration-Based Neuro-Optimal Tracking Control for Affine Systems with Completely Unknown dynamics

Value-Iteration-Based Neuro-Optimal Tracking Control for Aff...

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第三十九届中国控制会议

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology Beijing Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology School of Automation Guangdong University of Technology

In this paper, the versatile value-iteration-based control method, aimed at affine systems with unknown dynamics, is proposed to deal with the optimal tracking control problem. Neural networks are adopted to approximate system dynamics and a novel approach is presented to estimate the steady state control input based on the established identifier. Additionally, two other neural networks, called the critic network and the action network, are used to implement the optimal tracking control ***, based on the proposed method, the tracking controller is designed to control a specific simulation example. It is shown that, for any randomly given initial state vector, the controller is able to make the affine system track the reference trajectory without knowing the system dynamics.

关键词： adaptive dynamic programming Neuro-Optimal Tracking Control Value Iteration Affine Systems with Unknown dynamics

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Optimal Fault Tolerant Control of Reconfigurable Manipulator with Actuator Saturation

Optimal Fault Tolerant Control of Reconfigurable Manipulator...

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第32届中国控制与决策会议

作者： Fujie Nie Fan Zhou Bo Dong Tianjiao An Yuanchun Li Department of Control Science and Engineering Changchun University of Technology

In this paper, an optimal active fault tolerant control（FTC） method is presented for reconfigurable manipulator with actuator saturation based on the adaptive dynamic programming（ADP） approach. First, the saturation constraints of the manipulator systems are tackled by using hyperbolic tangent functions. Second, an actuator fault function, which is estimated via a fault observer, is used to construct the performance index function. Then, the solution of Hamiltonian-Jacobi-Bellman（HJB） equation is solved by using ADP and policy iteration（PI） methods, and the optimal performance index function is approximated by establishing a critic neural network（NN）. Based on the Lyapunov theory, the closed-loop manipulator system is proved to be asymptotic stable under the optimal FTC system. Finally, simulation results are demonstrated the validity of the proposed scheme.

关键词： adaptive dynamic programming reconfigurable manipulators actuator saturation optimal fault tolerant control neural network

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Improved adaptive Critic for Neural Optimal Control of Constrained Nonlinear Discrete-Time Systems

Improved Adaptive Critic for Neural Optimal Control of Const...

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第三十九届中国控制会议

作者： Mingming Zhao Ding Wang Mingming Ha Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology School of Automation and Electrical Engineering University of Science and Technology Beijing

There always exist approximation errors during neural network control processes, which may cause the estimation value to exceed the control constraint when the optimal control input reaches to a neighborhood of the constraint. In this paper,through a new neural network training approach, the near-optimal control problem for a class of nonlinear discrete-time systems with control constraints is solved. Based on the nonquadratic performance index and the dual heuristic dynamic programming scheme, the iterative algorithm is developed with convergence guarantee and is also implemented by using three neural *** last, two examples are given to demonstrate the effectiveness of the proposed optimal control scheme.

关键词： adaptive dynamic programming iterative adaptive critic control constraints neural networks nonlinear discrete-time systems

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Comprehensive comparison of online ADP algorithms for continuous-time optimal control

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ARTIFICIAL INTELLIGENCE REVIEW 2018年第4期49卷 531-547页

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

Online learning is an important property of adaptive dynamic programming (ADP). Online observations contain plentiful dynamics information, and ADP algorithms can utilize them to learn the optimal control policy. This paper reviews the research of online ADP algorithms for the optimal control of continuous-time systems. With the intensive study, ADP has been developed towards model free and data efficient. After separately introducing the algorithms, we compare their performance on the same problem. This paper is desired to provide a comprehensive understanding of continuous-time online ADP algorithms.

关键词： adaptive dynamic programming Policy iteration Integral reinforcement learning Experience replay Off-policy

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Fuzzy adaptive Decentralized Optimal Control for Strict Feedback Nonlinear Large-Scale Systems

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第4期48卷 1326-1339页

作者： Sun, Kangkang Sui, Shuai Tong, Shaocheng Liaoning Univ Technol Dept Basic Math Jinzhou 121001 Peoples R China

This paper considers the optimal decentralized fuzzy adaptive control design problem for a class of interconnected large-scale nonlinear systems in strict feedback form and with unknown nonlinear functions. The fuzzy logic systems are introduced to learn the unknown dynamics and cost functions, respectively, and a state estimator is developed. By applying the state estimator and the backstepping recursive design algorithm, a decentralized feedforward controller is established. By using the backstepping decentralized feedforward control scheme, the considered interconnected large-scale nonlinear system in strict feedback form is changed into an equivalent affine large-scale nonlinear system. Subsequently, an optimal decentralized fuzzy adaptive control scheme is constructed. The whole optimal decentralized fuzzy adaptive controller is composed of a decentralized feedforward control and an optimal decentralized control. It is proved that the developed optimal decentralized controller can ensure that all the variables of the control system are uniformly ultimately bounded, and the cost functions are the smallest. Two simulation examples are provided to illustrate the validity of the developed optimal decentralized fuzzy adaptive control scheme.

关键词： adaptive dynamic programming fuzzy adaptive backstepping control nonlinear large-scale systems optimal decentralized controller

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