Data-driven optimal tracking control of discrete-time multi-agent systems with two-stage policy iteration algorithm
Herein, a novel adaptive dynamic programming (ADP) algorithm is developed to solve the optimal tracking control problem of discrete-time multi-agent systems. Compared to the classical policy iteration ADP algorithm with two components, policy evaluation, and policy improvement, a two-stage policy it...
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| Vydané v: | Information sciences Ročník 481; s. 189 - 202 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Inc
01.05.2019
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | Herein, a novel adaptive dynamic programming (ADP) algorithm is developed to solve the optimal tracking control problem of discrete-time multi-agent systems. Compared to the classical policy iteration ADP algorithm with two components, policy evaluation, and policy improvement, a two-stage policy iteration algorithm is proposed to obtain the iterative control laws and the iterative performance index functions. The proposed algorithm contains a sub-iteration procedure to calculate the iterative performance index functions at the policy evaluation. The convergence proof for the iterative performance index functions and the iterative control laws are provided. Subsequently, the stability of the closed-loop error system is also provided. Further, an actor-critic neural network (NN) is used to approximate both the iterative control laws and the iterative performance index functions. The actor-critic NN can implement the developed algorithm online without knowledge of the system dynamics. Finally, simulation results are provided to illustrate the performance of our method. |
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| AbstractList | Herein, a novel adaptive dynamic programming (ADP) algorithm is developed to solve the optimal tracking control problem of discrete-time multi-agent systems. Compared to the classical policy iteration ADP algorithm with two components, policy evaluation, and policy improvement, a two-stage policy iteration algorithm is proposed to obtain the iterative control laws and the iterative performance index functions. The proposed algorithm contains a sub-iteration procedure to calculate the iterative performance index functions at the policy evaluation. The convergence proof for the iterative performance index functions and the iterative control laws are provided. Subsequently, the stability of the closed-loop error system is also provided. Further, an actor-critic neural network (NN) is used to approximate both the iterative control laws and the iterative performance index functions. The actor-critic NN can implement the developed algorithm online without knowledge of the system dynamics. Finally, simulation results are provided to illustrate the performance of our method. |
| Author | Hu, Jiangping Ghosh, Bijoy Kumar Peng, Zhinan Zhao, Yiyi |
| Author_xml | – sequence: 1 givenname: Zhinan surname: Peng fullname: Peng, Zhinan organization: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China – sequence: 2 givenname: Yiyi surname: Zhao fullname: Zhao, Yiyi organization: School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China – sequence: 3 givenname: Jiangping orcidid: 0000-0002-7559-8604 surname: Hu fullname: Hu, Jiangping email: hujp@uestc.edu.com, hjp_lzu@163.com organization: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China – sequence: 4 givenname: Bijoy Kumar surname: Ghosh fullname: Ghosh, Bijoy Kumar organization: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China |
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| Keywords | Actor-critic networks Optimal tracking control Two-stage policy iteration Data-driven algorithm Multi-agent systems |
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| SubjectTerms | Actor-critic networks Data-driven algorithm Multi-agent systems Optimal tracking control Two-stage policy iteration |
| Title | Data-driven optimal tracking control of discrete-time multi-agent systems with two-stage policy iteration algorithm |
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