Hierarchical optimal control for input-affine nonlinear systems through the formulation of Stackelberg game

doi:10.1016/j.ins.2019.12.078

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 深度强化学习

	Hierarchical optimal control for input-affine nonlinear systems through the formulation of Stackelberg game
	Mu, Chaoxu 1; Wang, Ke 1; Zhang, Qichao 2,3; Zhao, Dongbin 2,3
刊名	INFORMATION SCIENCES
	2020-05-01
卷号	517 页码:1-17
关键词	Nonzero-sum differential game Hierarchical optimization Nonlinear dynamics Stackelberg equilibrium Neural network
ISSN号	0020-0255
DOI	10.1016/j.ins.2019.12.078
通讯作者	Mu, Chaoxu(cxmu@tju.edu.cn)
英文摘要	Substantial efforts have been undertaken to explore nonzero-sum differential games. Most of these studies are devoted to devising algorithms to pursue Nash equilibrium, where all players with the same access to information will take policies synchronously. However, when it comes to hierarchical optimization and asymmetric information, Nash equilibrium is ineffective. The Stackelberg game provides us with an idea of leader-follower strategy to cope with this conundrum. The paper investigates hierarchical optimal control for continuous-time two-player input-affine systems characterized by nonlinear dynamics and quadratic cost functions. By introducing new costates, this optimization problem is formulated as a Stackelberg game in conjunction with a parametric optimization problem. Besides, the closed-loop information is available for both players. An adaptive learning algorithm is thus developed to approximately obtain the open-loop Stackelberg equilibrium while ensuring the uniform ultimate bounded stability of this closed-loop system, and two approximators structured by neural networks put this purpose into practice. Finally, two numerical examples illustrate that the proposed methodology can accurately obtain optimal solutions, and a comparative example illustrates its characteristics. (C) 2020 Elsevier Inc. All rights reserved.
资助项目	National Natural Science Foundation of China[61773284] ; National Natural Science Foundation of China[61803371] ; National Natural Science Foundation of China[61533017]
WOS关键词	STRATEGIES ; ALGORITHM
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000517659200001
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/38382]
专题	复杂系统管理与控制国家重点实验室_深度强化学习
通讯作者	Mu, Chaoxu
作者单位	1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Mu, Chaoxu,Wang, Ke,Zhang, Qichao,et al. Hierarchical optimal control for input-affine nonlinear systems through the formulation of Stackelberg game[J]. INFORMATION SCIENCES,2020,517:1-17.
APA	Mu, Chaoxu,Wang, Ke,Zhang, Qichao,&Zhao, Dongbin.(2020).Hierarchical optimal control for input-affine nonlinear systems through the formulation of Stackelberg game.INFORMATION SCIENCES,517,1-17.
MLA	Mu, Chaoxu,et al."Hierarchical optimal control for input-affine nonlinear systems through the formulation of Stackelberg game".INFORMATION SCIENCES 517(2020):1-17.