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Tracking control optimization scheme for a class of partially unknown fuzzy systems by using integral reinforcement learning architecture

Author

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  • Zhang, Kun
  • Zhang, Huaguang
  • Mu, Yunfei
  • Sun, Shaoxin

Abstract

In this paper, a novel fuzzy integral reinforcement learning (RL) based tracking control algorithm is first proposed for partially unknown fuzzy systems. Firstly, by using the precompensation and augmentation techniques, a new augmented fuzzy tracking system is constructed by combining the fuzzy logic model and desired reference trajectory, where the solution of actual working feedback control policy is converted into a virtual optimal control problem. Secondly, to overcome the requirements of exact original system information, the integral RL technique is utilized to learn the fuzzy control solution, which relaxes the repeatedly transmissions of system matrices during the solving process. Thirdly, compared with the existing standard solution, some crucial and strict aforementioned assumptions are removed and the system can be partially unknown by using the designed algorithm. Besides, under the novel fuzzy control policy, the tracking objective is achieved and the stability is guaranteed by Lyapunov theory. Finally, the developed integral RL tracking control algorithm for partially unknown systems is applied in a mechanical system and the simulation results demonstrate the effectiveness of the proposed new method.

Suggested Citation

  • Zhang, Kun & Zhang, Huaguang & Mu, Yunfei & Sun, Shaoxin, 2019. "Tracking control optimization scheme for a class of partially unknown fuzzy systems by using integral reinforcement learning architecture," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 344-356.
    • Handle: RePEc:eee:apmaco:v:359:y:2019:i:c:p:344-356
    DOI: 10.1016/j.amc.2019.04.084
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    References listed on IDEAS

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    1. Wang, Jing & Hu, Xiaohui & Wei, Yunliang & Wang, Zhen, 2019. "Sampled-data synchronization of semi-Markov jump complex dynamical networks subject to generalized dissipativity property," Applied Mathematics and Computation, Elsevier, vol. 346(C), pages 853-864.
    2. Wang, Bo & Yan, Juan & Cheng, Jun & Zhong, Shouming, 2017. "New criteria of stability analysis for generalized neural networks subject to time-varying delayed signals," Applied Mathematics and Computation, Elsevier, vol. 314(C), pages 322-333.
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    1. Jia, Guolong & Yang, Qing & Liu, Jinxu & Shen, Hao, 2025. "Reinforcement learning-based linear quadratic tracking control for partially unknown Markov jump singular interconnected systems," Applied Mathematics and Computation, Elsevier, vol. 491(C).

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