Generalized Policy Iteration Adaptive Dynamic Programming Algorithm for Optimal Tracking Control of a Class of Nonlinear Systems

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 > 平行控制

	Generalized Policy Iteration Adaptive Dynamic Programming Algorithm for Optimal Tracking Control of a Class of Nonlinear Systems
	Lin Q(林桥)1 ; Qinglai Wei1 ; Derong Liu 2
	2016-08
会议日期	2016-5-28
会议地点	Yinchuan, China
英文摘要	This paper deals with optimal tracking control problems for a class of discrete-time nonlinear systems using a generalized policy iteration adaptive dynamic programming (ADP) algorithm. First, by system transformation, the optimal tracking control problem is transformed into an optimal regulation problem. Then the generalized policy iteration ADP algorithm is employed to obtain the optimal tracking controller with convergence and optimality analysis. The developed algorithm uses the idea of two iteration procedures to obtain the iterative tracking control laws and the iterative value functions. Three neural networks, including model network, critic network and action network, are used to implement the developed algorithm. At last, an simulation example is given to demonstrate the effectiveness of the developed method.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/14347]
专题	复杂系统管理与控制国家重点实验室_平行控制
作者单位	1.Institute of Automation, Chinese Academy of Sciences 2.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Lin Q,Qinglai Wei,Derong Liu. Generalized Policy Iteration Adaptive Dynamic Programming Algorithm for Optimal Tracking Control of a Class of Nonlinear Systems[C]. 见:. Yinchuan, China. 2016-5-28.