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Finite Horizon Robust Optimal Tracking Control Based on Approximate Dynamic Programming for Switched Systems with Uncertainties

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  • Control Theory and Applications
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Abstract

In this paper, an approximate dynamic programming (ADP)-based approach is developed to handle the robust optimal tracking control problem for switched systems with uncertainties in the finite horizon. The switched systems with unknown matched uncertainties are formulated by virtue of system dynamics and reference trajectory, where the complicated tracking problem is converted to a stabilizing robust optimal control problem. To avoid the requirement of system dynamics knowledge, a neural network (NN)-based identifier is utilized to estimate the unknown switched systems dynamics. The actor-critic NNs are constructed to approximate the optimal control input and the corresponding performance index, where the weights are trained backward-in-time in an off-line manner. Benefiting from the Lipschitz continuous condition, the convergence of the proposed approach is proved, which illustrates the iteration approach will converge to the unique solution under a small enough sampling time interval. Finally, two numerical simulation cases are employed to verify the effectiveness of the proposed approach.

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Funding

This work is supported by National Natural Science Foundation of China (No. 61633019).

Author information

Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China

    Shangwei Zhao, Jingcheng Wang, Haotian Xu & Hongyuan Wang

  2. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China

    Shangwei Zhao, Jingcheng Wang, Haotian Xu & Hongyuan Wang

  3. Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai, 200240, China

    Shangwei Zhao, Jingcheng Wang, Haotian Xu & Hongyuan Wang

  4. Autonomous Systems and Intelligent Control International Joint Research Center, Xi’an Technological University, Xi’an, China

    Jingcheng Wang

Authors
  1. Shangwei Zhao
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  2. Jingcheng Wang
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  3. Haotian Xu
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  4. Hongyuan Wang
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Corresponding author

Correspondence to Jingcheng Wang.

Additional information

Shangwei Zhao received his B.S. degree in automation from Northeastern University, Shenyang, China, in 2018. He is currently pursuing a Ph.D. degree with the Department of Automation, Shanghai Jiao Tong University, Shanghai, China from 2018. His current research interests include adaptive dynamic programming and switched systems.

Jingcheng Wang received his B.S. and M.S. degrees from Northwestern Polytechnic University, Xi’an, China, in 1992 and 1995, respectively, and a Ph.D. degree from Zhejiang University, Hangzhou, China, in 1998. Now, he is a Professor with Shanghai Jiao Tong University, Shanghai, China. His research interests include robust control and intelligent control.

Haotian Xu received his B.S. degree from the School of Mathematics, Shandong University, China, in 2016. He is currently pursuing a Ph.D. degree with the Department of Automation, Shanghai Jiao Tong University, Shanghai, China from 2016. His current research interests include distributed control and observer design, and multi-agent system.

Hongyuan Wang received his B.S. degree in automation, Hunan University, Changsha, China, in 2015. He is currently pursuing a Ph.D. degree in the Department of Automation, Shanghai Jiao Tong University, Shanghai, China. Now, his research focuses on stochastic/robust model predictive control.

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Zhao, S., Wang, J., Xu, H. et al. Finite Horizon Robust Optimal Tracking Control Based on Approximate Dynamic Programming for Switched Systems with Uncertainties. Int. J. Control Autom. Syst. 20, 1051–1062 (2022). https://doi.org/10.1007/s12555-020-0982-8

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  • DOI: https://doi.org/10.1007/s12555-020-0982-8

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