Neural-Network-Based Finite Horizon Optimal Control for Partially Unknown Linear Continuous-Time Systems

	Neural-Network-Based Finite Horizon Optimal Control for Partially Unknown Linear Continuous-Time Systems
	Li, Chao; Li, Hongliang; Liu, Derong
	2015
会议日期	March 27-29, 2015
会议地点	Mount Wuyi, Fujian, China
英文摘要	In this paper, we establish a neural-network-based online learning algorithm to solve the finite horizon linear quadratic regulator (FHLQR) problem for partially unknown continuous-time systems. To solve the FHLQR problem with partially unknown system dynamics, we develop a time-varying Riccati equation. A critic neural network is used to approximate the value function and the online learning algorithm is established using the policy iteration technique to solve the time-varying Riccati equation. An integral policy iteration method and a tuning law are used when the algorithm is implemented without the knowledge of the system drift dynamics. We give a simulation example to show the effectiveness of this algorithm.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/14313]
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	Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Li, Chao,Li, Hongliang,Liu, Derong. Neural-Network-Based Finite Horizon Optimal Control for Partially Unknown Linear Continuous-Time Systems[C]. 见:. Mount Wuyi, Fujian, China. March 27-29, 2015.