Neural-network-based robust optimal control of uncertain nonlinear systems using model-free policy iteration algorithm

CASIA OpenIR > 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

	Neural-network-based robust optimal control of uncertain nonlinear systems using model-free policy iteration algorithm
	Li, Chao1 ; Wang, Ding1 ; Liu, Derong 2
	2016
会议名称	2016 International Joint Conference on Neural Networks
会议日期	24-29 July 2016
会议地点	Vancouver, BC, Canada
摘要	In this paper, we establish a robust optimal control law for a class of continuous-time uncertain nonlinear systems by using a neural-network-based model-free policy iteration approach. The robust control law of the original uncertain nonlinear system is derived by adding a feedback gain to the optimal control law of the nominal system. It is proven that this robust control law can achieve optimality under a specified cost function. Then, the neural-network-based model-free policy iteration algorithm is developed to solve the Hamilton-Jacobi-Bellman equation corresponding to the nominal system without system dynamics. The actor-critic technique and the least squares implementation method are used to obtain the optimal control policy of the nominal system. A numerical simulation is given to verify the applicability of the present robust optimal control scheme.
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14318
专题	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
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Li, Chao,Wang, Ding,Liu, Derong. Neural-network-based robust optimal control of uncertain nonlinear systems using model-free policy iteration algorithm[C],2016.

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