Synergetic learning for unknown nonlinear<i> H.</i> control using neural networks

doi:10.1016/j.neunet.2023.09.029

	Synergetic learning for unknown nonlinear H. control using neural networks
	Zhu, Liao1,2 ; Guo, Ping 1,2; Wei, Qinglai3,4,5
发表期刊	NEURAL NETWORKS
ISSN	0893-6080
	2023-11-01
卷号	168 页码:287-299
通讯作者	Guo, Ping(pguo@bnu.edu.cn)
摘要	The well-known H. control design gives robustness to a controller by rejecting perturbations from the external environment, which is difficult to do for completely unknown affine nonlinear systems. Accordingly, the immediate objective of this paper is to develop an on-line real-time synergetic learning algorithm, so that a data-driven H. controller can be received. By converting the H. control problem into a two-player zero-sum game, a model-free Hamilton-Jacobi-Isaacs equation (MF-HJIE) is first derived using off-policy reinforcement learning, followed by a proof of equivalence between the MF-HJIE and the conventional HJIE. Next, by applying the temporal difference to the MF-HJIE, a synergetic evolutionary rule with experience replay is designed to learn the optimal value function, the optimal control, and the worst perturbation, that can be performed on-line and in real-time along the system state trajectory. It is proven that the synergistic learning system constructed by the system plant and the evolutionary rule is uniformly ultimately bounded. Finally, simulation results on an F16 aircraft system and a nonlinear system back up the tractability of the proposed method.
关键词	H. control Nonlinear systems Adaptive dynamic programming Temporal difference Neural network Data-driven
DOI	10.1016/j.neunet.2023.09.029
关键词[WOS]	STATE-FEEDBACK CONTROL ; ZERO-SUM GAMES ; POLICY UPDATE ALGORITHM ; SYSTEMS ; EQUATION
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA0100203] ; National Key Research and Development Program of China[2021YFE0206100] ; National Natural Science Foun-dation of China[62073321] ; Science and Technology Development Fund, Macau SAR[0060/2021/A]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foun-dation of China ; Science and Technology Development Fund, Macau SAR
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:001086806900001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54335
专题	多模态人工智能系统全国重点实验室多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
通讯作者	Guo, Ping
作者单位	1.Beijing Normal Univ, Int Acad Ctr Complex Syst, Zhuhai 519087, Guangdong, Peoples R China 2.Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 5.Macau Univ Sci & Technol, Inst Syst Engn, Taipa 999078, Macau, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Liao,Guo, Ping,Wei, Qinglai. Synergetic learning for unknown nonlinear H. control using neural networks[J]. NEURAL NETWORKS,2023,168:287-299.
APA	Zhu, Liao,Guo, Ping,&Wei, Qinglai.(2023).Synergetic learning for unknown nonlinear H. control using neural networks.NEURAL NETWORKS,168,287-299.
MLA	Zhu, Liao,et al."Synergetic learning for unknown nonlinear H. control using neural networks".NEURAL NETWORKS 168(2023):287-299.