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2017 IEEE 6th Data Driven Control and Learning Systems Conference (DDCLS’17)

作者： Qinglai Wei Ruizhuo Song Yancai Xu Derong Liu Qiao Lin The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (纸本)9781509054626

In this paper, a novel discrete-time iterative zero-sum adaptive dynamic programming(ADP) algorithm is developed for solving the optimal control problems of nonlinear systems. Two iteration processes, which are lower and upper iterations, are employed to solve the lower and upper value functions, respectively. Arbitrary positive semi-definite functions are acceptable to initialize the upper and lower iterations of the iterative zero-sum ADP algorithm. It is proven that the upper and lower value functions converge to the optimal performance index function if the optimal performance index function exists, where the existence criterion of the optimal performance index function is unnecessary. Simulation examples are given to illustrate the effective performance of the present method.

关键词： Approximate dynamic programming adaptive dynamic programming zero-sum game optimal control

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Online adaptive dynamic programming Control of Urban Open-channel Flow System 7

Online Adaptive Dynamic Programming Control of Urban Open-ch...

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7th International Conference on Information Science and Technology (ICIST)

作者： Zhong, Zhiguang Ouyang, Yuxuan Yang, Qinmin Ningbo Univ Coll Sci & Technol Ningbo Zhejiang Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781509054015

An online adaptive dynamic programming (ADP) design is proposed for the control of urban open-channel flow systems, whose topographic parameters are not assumed to be accessible. According to the Saint-Venant continuity equation, a simplified model is firstly built. Subsequently, an adaptive dynamic programming control scheme is implemented, whose purpose is to track the desired water level, as well as to decrease the control cost. The design contains two RBF neural networks (NN). One action NN is employed to generate the control signal. Another critic NN is designed to approximate the long-term cost function. The two NNs are coordinated to approach an optimal solution. Finally, the adaptive dynamic programming controller is validated in rainstorm situation in simulation environment. The results demonstrate that the designed scheme outperforms the traditional PID counterpart.

关键词： adaptive dynamic programming open-channel flow unknown system neural networks

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Event-Triggered adaptive dynamic programming for Uncertain Nonlinear Systems 1

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3rd International Conference on Cognitive Systems and Information Processing (ICCSIP)

作者： Zhang, Qichao Zhao, Dongbin Wang, Ding Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (数字)9789811052309

ISBN: (纸本)9789811052309;9789811052293

In this paper, the robust control for a class of continuous-time nonlinear system with unmatched uncertainties is investigated using an event-triggered adaptive dynamic programming method. First, the robust control problem is solved using the optimal control method. Under the event-triggered mechanism, the solution of the optimal control problem can asymptotically stabilize the uncertain system with an designed triggering condition. That is, the designed event-triggered controller is robust to the original uncertain system. Then, a single critic network structure with experience replay technique is constructed to approach the optimal control policies. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed control scheme.

关键词： adaptive dynamic programming Event-triggered control Robust control Neural network

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Data-Driven adaptive dynamic programming for Two-Player Nonzero-Sum Game 29

Data-Driven Adaptive Dynamic Programming for Two-Player Nonz...

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第29届中国控制与决策会议

作者： Qichao Zhang Dongbin Zhao Yafei Zhou The state Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (纸本)9781509046584

In this paper, we propose a data-driven adaptive dynamic programming approach to solve the Hamilton-Jacobi(HJ) equations for the two-player nonzero-sum(NZS) game with completely unknown dynamics. First, the model-based policy iteration(PI) algorithm is given, where the knowledge of system dynamics is required. To relax this requirement,a data-driven adaptive dynamic programming(ADP) is proposed in this paper to solve the unknown nonlinear NZS game with only online data. Neural network approximators are constructed to approach the solution of the HJ equations. The online data is collected under the two initial admissible control policies. Then, the NN weights are updated based on the least-squares method using the collected online data repeatedly, which is a kind of the off-policy learning ***, a simulation example is provided to demonstrate the effectiveness of the proposed control scheme.

关键词： adaptive dynamic programming data-driven neural network unknown dynamics off-policy

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Decentralized optimal control for modular and reconfigurable robots based on adaptive dynamic programming 36

Decentralized optimal control for modular and reconfigurable...

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第36届中国控制会议

作者： Zixu Wang Bo Dong Hongbing Xia Yuanchun Li Department of Control Science and Engineering Changchun University of Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper presents a decentralized optimal control method for modular and reconflgurable robots（MRRs） based on adaptive dynamic ***,the dynamic model of MRRs is formulated by using the Newton-Euler iterative algorithm,and then the state space description is ***,the optimal control policy of the MRRs system is obtained based on the policy iteration algorithm,which is used to solve the Hamilton-Jacobi-Bellman（HJB） equation via the critic neural ***,the stability of the closed-loop system is proved by using the Lyapunov ***,simulations are conducted to illustrate the effectiveness for the 2-DOF MRRs.

关键词： Modular and reconfigurable robot Decentralized control strategy adaptive dynamic programming Neural network

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A Generalized Policy Iteration adaptive dynamic programming Algorithm for Optimal Control of Discrete-Time Nonlinear Systems with Actuator Saturation 14th

A Generalized Policy Iteration Adaptive Dynamic Programming ...

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14th International Symposium on Neural Networks (ISNN)

作者： Lin, Qiao Wei, Qinglai Zhao, Bo Univ Chinese Acad Sci Beijing 100190 Peoples R China

ISBN: (纸本)9783319590813;9783319590806

In this study, a nonquadratic performance function is introduced to overcome the saturation nonlinearity in actuators. Then a novel solution, generalized policy iteration adaptive dynamic programming algorithm, is applied to deal with the problem of optimal control. To achieve this goal, we use two neural networks to approximate control vectors and performance index function. Finally, this paper focuses on an example simulated on Matlab, which verifies the excellent convergence of the mentioned algorithm and feasibility of this scheme.

关键词： adaptive dynamic programming Neural network Optimal control Saturating actuators

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A Hybrid-adaptive dynamic programming Approach for the Model-Free Control of Nonlinear Switched Systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2016年第10期61卷 3203-3208页

作者： Lu, Wenjie Zhu, Pingping Ferrari, Silvia Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA

This paper presents a hybrid adaptive dynamic programming (hybrid-ADP) approach for determining the optimal continuous and discrete control laws of a switched system online, solely from state observations. The new hybrid-ADP recurrence relationships presented are applicable to model-free control of switched hybrid systems that are possibly nonlinear. The computational complexity and convergence of the hybrid-ADP approach are analyzed, and the method is validated numerically showing that the optimal controller and value function can be learned iteratively online from state observations.

关键词： adaptive dynamic programming hybrid systems learning model-free control switched systems

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Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design

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AUTOMATICA 2016年 71卷 348-360页

作者： Bian, Tao Jiang, Zhong-Ping NYU Tandon Sch Engn Dept Elect & Comp Engn Control & Networks LabMetrotech Ctr 5 Brooklyn NY 11201 USA

This paper presents a novel non-model-based, data-driven adaptive optimal controller design for linear continuous-time systems with completely unknown dynamics. Inspired by the stochastic approximation theory, a continuous-time version of the traditional value iteration (VI) algorithm is presented with rigorous convergence analysis. This VI method is crucial for developing new adaptive dynamic programming methods to solve the adaptive optimal control problem and the stochastic robust optimal control problem for linear continuous-time systems. Fundamentally different from existing results, the a priori knowledge of an initial admissible control policy is no longer required. The efficacy of the proposed methodology is illustrated by two examples and a brief comparative study between VI and earlier policy iteration methods. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Value iteration adaptive dynamic programming Optimal control adaptive control Stochastic approximation

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Neural network-based online H_∞ control for discrete-time affine nonlinear system using adaptive dynamic programming

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NEUROCOMPUTING 2016年 198卷 91-99页

作者： Qin, Chunbin Zhang, Huaguang Wang, Yingchun Luo, Yanhong Henan Univ Coll Comp & Informat Engn Kaifeng 475004 Henan Peoples R China Henan Univ Coll Environm & Planning Kaifeng 475004 Henan Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

In this paper, the problem of H-infinity control design for affine nonlinear discrete-time systems is addressed by using adaptive dynamic programming (ADP). First, the nonlinear H-infinity control problem is transformed into solving the two-player zero-sum differential game problem of the nonlinear system. Then, the critic, action and disturbance networks are designed by using neural networks to solve online the Hamilton-Jacobi-Isaacs (HJI) equation associating with the two-player zero-sum differential game. When novel weight update laws for the critic, action and disturbance networks are tuned online by using data generated in real-time along the system trajectories, it is shown that the system states, all neural networks weight estimation errors are uniformly ultimately bounded by using Lyapunov techniques. Further, it is shown that the output of the action network approaches the optimal control input with small bounded error and the output of the disturbance network approaches the worst disturbance with small bounded error. At last, simulation results are presented to demonstrate the effectiveness of the new ADP based method. (C) 2016 Elsevier B.V. All rights reserved.

关键词： H-infinity control adaptive dynamic programming Neural networks Nonlinear discrete-time system Two-person zero-sum game

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Neuro-optimal tracking control for a class of discrete-time nonlinear systems via generalized value iteration adaptive dynamic programming approach

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SOFT COMPUTING 2016年第2期20卷 697-706页

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

In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called "generalized value iteration ADP" algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. Convergence property is developed to guarantee that the iterative performance index function will converge to the optimum. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, a simulation example is given to illustrate the performance of the developed algorithm.

关键词： adaptive dynamic programming Approximate dynamic programming adaptive critic designs Optimal control Neural networks Nonlinear systems Reinforcement learning

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