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Model-free adaptive control design for nonlinear discrete-time processes with reinforcement learning techniques

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  • Dong Liu
  • Guang-Hong Yang

Abstract

This paper deals with the model-free adaptive control (MFAC) based on the reinforcement learning (RL) strategy for a family of discrete-time nonlinear processes. The controller is constructed based on the approximation ability of neural network architecture, a new actor-critic algorithm for neural network control problem is developed to estimate the strategic utility function and the performance index function. More specifically, the novel RL-based MFAC scheme is reasonable to design the controller without need to estimate y(k+1) information. Furthermore, based on Lyapunov stability analysis method, the closed-loop systems can be ensured uniformly ultimately bounded. Simulations are shown to validate the theoretical results.

Suggested Citation

  • Dong Liu & Guang-Hong Yang, 2018. "Model-free adaptive control design for nonlinear discrete-time processes with reinforcement learning techniques," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(11), pages 2298-2308, August.
    • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:11:p:2298-2308
    DOI: 10.1080/00207721.2018.1498557
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    Cited by:

    1. Li, Jianshen & Wang, Shuangxin & Li, Yaguang, 2020. "A model-free adaptive controller with tracking error differential for collective pitching of wind turbines," Renewable Energy, Elsevier, vol. 161(C), pages 435-447.

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