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adaptive dynamic programming Approach on Optimal Control for Affinely Pseudo-Linearized Nonlinear System

IEEE ACCESS 2019年 7卷 75132-75142页

作者： Zhang, Lin Zhu, Yian Shi, Haobin Hwang, Kao-Shing Northwestern Polytech Univ Sch Comp Sci Xian 710129 Shaanxi Peoples R China Northwestern Polytech Univ Minist Ind & Informat Technol Key Lab Big Data Storage & Management Xian 710072 Shaanxi Peoples R China Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 80424 Taiwan

This paper concerns the optimal control problem of a general affinely pseudo-linearized nonlinear system and puts forward the solving scheme based on adaptive dynamic programming and neural network. In detail, a new more general affinely pseudo-linearized nonlinear system model is presented and covers more practical scenarios than existing models in engineering. In consideration of the Bellman optimal principle, the corresponding model particulars are then analyzed and its adaptive dynamic programming solving scheme is derived. That is, the modified policy iteration is used to estimate the value function and optimal control at each sampled state, and then the calculated state-control pairs and state critic values are utilized to train two neural networks (one works as state evaluator and the other works as optimal control generator, respectively). Through the above, the discrete state space is smoothed into a continuous state space, and the notorious dimensional curse problem could be addressed. Moreover, in order to improve the efficiency and precision of the network training, the samples calculating and network training processes are simultaneously carried out in this paper. In the end, a related simulation experiment is applied and the results demonstrate that the proposed method has more effectiveness and validation for the optimal control problem of the newly proposed general system model than the compared methods.

关键词： Optimal control adaptive dynamic programming system identification neural network

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Event-Triggered Decentralized Tracking Control of Modular Reconfigurable Robots Through adaptive dynamic programming

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2020年第4期67卷 3054-3064页

作者： Zhao, Bo Liu, Derong Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

This paper develops an event-triggered decentralized tracking control (DTC) approach for modular reconfigurable robots (MRRs) by using adaptive dynamic programming. By establishing a decentralized neural network (NN) observer, which uses local input-output data and desired states of coupling subsystems, the local dynamics of MRR subsystem can be obtained. In order to obtain the DTC, the tracking error subsystem is augmented by the exosystem with the desired trajectory. Based on the event-triggered mechanism and a modified local cost function, the DTC is derived by solving the local Hamilton-Jacobi-Bellman equation via a local critic NN with asymptotically stable structure. The stability of the entire closed-loop MRR system is analyzed by Lyapunovs direct method. The simulation of a two-degree of freedom MRR system ensures that the developed event-triggered DTC scheme is effective.

关键词： Decentralized control Couplings Optimal control Robots dynamic programming Artificial neural networks Trajectory adaptive dynamic programming decentralized tracking control event-triggered mechanism modular reconfigurable robots optimal control reinforcement learning

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Internal reinforcement adaptive dynamic programming for optimal containment control of unknown continuous-time multi-agent systems

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NEUROCOMPUTING 2020年 413卷 85-95页

作者： Zhang, Jiefu Peng, Zhinan Hu, Jiangping Zhao, Yiyi Luo, Rui Ghosh, Bijoy Kumar Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Southwestern Univ Finance & Econ Sch Business Adm Chengdu 611130 Peoples R China Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA

In this paper, a novel control scheme is developed to solve an optimal containment control problem of unknown continuous-time multi-agent systems. Different from traditional adaptive dynamic programming (ADP) algorithms, this paper proposes an internal reinforcement ADP algorithm (IR-ADP), in which the internal reinforcement signals are added in order to facilitate the learning process. Then a distributed containment control law is designed for each agent with the internal reinforcement signal. The convergence of this IR-ADP algorithm and the stability of the closed-loop multi-agent system are analyzed theoretically. For the implementation of the optimal controllers, three neural networks (NNs), namely internal reinforcement NNs, critic NNs and actor NNs, are utilized to approximate the internal reinforcement signals, the performance indices and optimal control laws, respectively. Finally, some simulation results are provided to demonstrate the effectiveness of the proposed algorithm. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Optimal containment control Multi-agent system Internal reinforcement learning adaptive dynamic programming Neural network

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Critic-only adaptive dynamic programming algorithms' applications to the secure control of cyber-physical systems

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ISA TRANSACTIONS 2020年 104卷 138-144页

作者： Jiang, He Zhang, Huaguang Xie, Xiangpeng Northeastern Univ Coll Informat Sci & Engn Box 134 Shenyang 110819 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China

Industrial cyber-physical systems generally suffer from the malicious attacks and unmatched perturbation, and thus the security issue is always the core research topic in the related fields. This paper proposes a novel intelligent secure control scheme, which integrates optimal control theory, zero-sum game theory, reinforcement learning and neural networks. First, the secure control problem of the compromised system is converted into the zero-sum game issue of the nominal auxiliary system, and then both policy-iteration-based and value-iteration-based adaptive dynamic programming methods are introduced to solve the Hamilton-Jacobi-Isaacs equations. The proposed secure control scheme can mitigate the effects of actuator attacks and unmatched perturbation, and stabilize the compromised cyber-physical systems by tuning the system performance parameters, which is proved through the Lyapunov stability theory. Finally, the proposed approach is applied to the Quanser helicopter to verify the effectiveness. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Cyber-physical systems adaptive dynamic programming Reinforcement learning Neural networks

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Optimal Guaranteed Cost Tracking of Uncertain Nonlinear Systems Using adaptive dynamic programming with Concurrent Learning

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2020年第5期18卷 1116-1127页

作者： Xu, Dengguo Wang, Qinglin Li, Yuan Beijing Inst Technol Sch Automat 5 South Zhong Guan Cun St Beijing 100081 Peoples R China Liupanshui Normal Univ Sch Elect Engn 288 Minghu Rd Liupanshui 553000 Peoples R China

In this paper, based on adaptive dynamic programming (ADP) with concurrent learning, the problem of optimal guaranteed cost tracking is studied for a class of uncertain continuous-time nonlinear systems. First, the original uncertain system and a reference system are combined into an augmented uncertain system, and the performance index is transformed into the corresponding augmented form. Afterwards, the solution of the optimal control problem consisting of the nominal augmented system with the modified performance index is proven to be the optimal guaranteed cost tracking control law of the original uncertain system. Moreover, a concurrent learning tuning algorithm based on ADP is presented to approximate the solution of corresponding Hamilton-Jacobi-Bellman (HJB) equation, which relaxes the condition of persistent excitation (PE). A neural network-based approximate optimal guaranteed cost tracking design is developed not only to ensure tracking error convergence to zero for all admissible uncertainties but also to achieve the minimal guaranteed cost. Finally, two simulation examples are considered to verify the effectiveness of the theoretical results.

关键词： adaptive dynamic programming concurrent learning optimal control optimal guaranteed cost tracking uncertain nonlinear system

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Integrated adaptive dynamic programming for data -driven optimal controller design

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NEUROCOMPUTING 2020年 403卷 143-152页

作者： Li, Guoqiang Goerges, Daniel Mu, Chaoxu Univ Kaiserslautern Dept Elect & Comp Engn Erwin Schrodinger Str 12 D-67663 Kaiserslautern Germany Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

In this paper a novel integrated adaptive dynamic programming method with an advantage function is developed to solve model-free optimal control problems and improve the control performance. The advantage function is utilized to evaluate the cost resulting from the action (control variables) which does not follow the optimal control policy. The Q function in Q-learning can thus be built from a value function and the advantage function. The control policy is then improved through minimizing the Q function. To employ the proposed algorithm, an integrated multi-layer neural network (INN) is designed for the value function and the control variables. Only one single neural network requires adaption. This avoids the iterative learning of two separate networks in the heuristic dynamic programming-based methods. Simulation for linear and non-linear optimal control problems is studied. Comparing to the optimal solutions resulting from the linear quadratic regulator and dynamic programming (DP), the proposed INN design can lead to closer control performance than the ones with action dependent heuristic dynamic programming (ADHDP). Furthermore INN is applied to optimize the energy management strategy of hybrid electric vehicles for fuel economy. The fuel consumption based on INN is lower than the one from ADHDP and much closer to the optimal results by DP. The result indicates the near fuel-optimality and an effective practical application.

关键词： adaptive dynamic programming Reinforcement learning Integrated neural network Advantage function Learning control Energy management strategy

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Pareto optimal control of the mean-field stochastic systems by adaptive dynamic programming algorithm

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ISA TRANSACTIONS 2020年 102卷 81-90页

作者： Ge, Yingying Liu, Xikui Li, Yan Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China

The Pareto game for the model-free continuous-time stochastic system is studied through approximate/adaptive dynamic programming (ADP) in this paper. Firstly, the model-based online iterative algorithm is proposed, and it is proved that the control iterative sequence converges to the Pareto efficient solution, but the algorithm requires complete system parameters. Then, we derive the modelfree iterative equation and develop the ADP algorithm to calculate the equation by collecting updated states and input information online. From the derivation of the ADP algorithm, the model-free iterative equation and the model-based iterative equation have the same solution, which means that the ADP algorithm can approximate the Pareto optimal solution. Next, the convergence analysis shows that the Pareto optimal strategy is uniquely determined by the ADP algorithm. Finally, two simulation examples confirm the feasibility of the ADP algorithm. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Pareto game adaptive dynamic programming Mean-field system Optimal control

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Optimal torque control of permanent magnet synchronous motors using adaptive dynamic programming

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IET POWER ELECTRONICS 2020年第12期13卷 2442-2449页

作者： Khiabani, Ataollah Gogani Heydari, Ali Southern Methodist Univ Dept Mech Engn 6425 Boaz Lane Dallas TX 75205 USA

In this study, a new approach based on adaptive dynamic programming (ADP) is proposed to control permanent magnet synchronous motors (PMSMs). The objective of this study is to control the torque and consequently the speed of a PMSM when an unknown load torque is applied to it. The proposed controller achieves a fast transient response, low ripples and small steady-state error. The control algorithm uses two neural networks, called critic and actor. The former is utilised to evaluate the cost and the latter is used to generate control signals. The training is done once offline and the calculated optimal weights of actor network are used in online control to achieve fast and accurate torque control of PMSMs. This algorithm is compared with field oriented control (FOC) and direct torque control based on space vector modulation. Simulations and experimental results show that the proposed algorithm provides desirable results under both accurate and uncertain modelled dynamics. Although the performance of FOC method is comparable with ADP under nominal conditions, the torque and speed response of ADP is better than FOC under realistic scenarios, that is, when parameter uncertainties exist.

关键词： angular velocity control transient response torque control synchronous motor drives dynamic programming synchronous motors machine vector control permanent magnet motors actor network online control direct torque control uncertain modelled dynamics ADP optimal torque control permanent magnet synchronous motors adaptive dynamic programming unknown load torque fast transient response steady-state error control algorithm control signals optimal weights PMSM

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adaptive dynamic programming for Minimal Energy Control with Guaranteed Convergence Rate of Linear Systems

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2019年第12期17卷 3140-3148页

作者： Zhang, Kai Ge, Suoliang Ge, Yuling Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China

The traditional linear quadratic optimal control can be summarized as finding the state feedback controller, so that the closed-loop system is stable and the performance index is minimum. And it is well known that the solution of the linear quadratic optimal control problem can be obtained by algebraic Riccati equation (ARE) with the standard assumptions. However, results developed for the traditional linear quadratic optimal control problem cannot be directly applied to solve the problem of minimal energy control with guaranteed convergence rate (MECGCR), because the standard assumptions cannot be satisfied in the MECGCR problem. In this paper, we mainly consider the problem of MECGCR and prove that ARE can be applied to solve the MECGCR problem under some conditions. Furthermore, with the assumption that the system dynamics is unknown, we propose a policy iteration (PI) based adaptive dynamic programming (ADP) algorithm to iteratively solve the ARE using the online information of state and input, without requiring the a priori knowledge of the system matrices. Finally, a numerical example is worked out to show the effectiveness of the proposed approach.

关键词： adaptive dynamic programming guaranteed convergence rate minimal energy control policy iteration

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Data-Based Online Optimal Temperature Tracking Control in Continuous Microwave Heating System by adaptive dynamic programming

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NEURAL PROCESSING LETTERS 2020年第1期51卷 167-191页

作者： Liu, Tong Liang, Shan Xiong, Qingyu Wang, Kai Chongqing Univ Key Lab Complex Syst Safety & Control Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China

Control of continuous microwave heating system (CMHS) is truly a complex problem with time variance, uncertainty and nonlinearity, which becomes prohibitive to use a conventional model-based approach. To overcome this, a novel data-based optimal temperature tracking control is designed for CMHS in this paper. In order to obtain the complex dynamics of CMHS, a neural network model is first constructed driven by process data. After transforming the original temperature tracking problem into an error regulation problem, adaptive dynamic programming is introduced to deal with the regulation problem as well as to decrease operation cost. The design and operation of this controller depend mainly on the online data, and minor prior knowledge is required. Simulation results show that the proposed method can effectively control the CMHS in terms of temperature tracking and energy utilization.

关键词： Continuous microwave heating system Data-based Optimal temperature tracking control adaptive dynamic programming Neural networks

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