Data-Driven Nonlinear Near-Optimal Regulation Based on Multi-Dimensional Taylor Network Dynamic Programming

IEEE Access

Jing-Jing Yu¹  Lei Xu²  Qi-Ming Sun²  Nan-Yun Jiang³  Chao Zhang⁴ 

[1] China Railway Shanghai Design Institute Group Co., Ltd., Shanghai, China;College of Information Science and Technology, Nanjing Forestry University, Nanjing, China;Department of Economics and Management, Nanjing Tech University, Nanjing, China;School of Electrical Engineering and Automation, Henan Institute of Technology, Xinxiang, China;

关键词: Adaptive dynamic programming;    data-driven control;    multi-dimensional Taylor network;    nonlinear control;   

DOI  :  10.1109/ACCESS.2020.2975391

来源: DOAJ

【 摘 要 】

Using the data-driven control formulation, an iterative dynamic programming approach which is based on a multi-dimensional Taylor network is established to design the near optimal regulation of discrete-time nonlinear systems. For discrete-time general nonlinear systems, the iterative adaptive dynamic programming algorithm is developed and proved to guarantee the property of convergence and optimality. Three networks are constructed, namely, the identification network, critic network and action network. Moreover, a globalized dual heuristic programming technique with detailed implementation is developed. The cost function and its derivative can be approximated by this novel architecture. Besides, without the consideration of the system dynamics, this technique can learn the near-optimal control law simultaneously and adaptively. In addition, this technique greatly improves the existing results of the iterative adaptive dynamic programming algorithm in terms of reducing the requirement of the control matrix. Furthermore, because of the approach that is based on the multi-dimensional Taylor network, the amount of calculation needed is also greatly reduced. The simulation experiment is described to illustrate the effectiveness of the data-driven optimal regulation method proposed in this paper.

【 授权许可】

Unknown