Neural-Network-Based Finite Horizon Optimal Control for Partially Unknown Linear Continuous-Time Systems
Li, Chao; Li, Hongliang; Liu, Derong
2015
会议名称7th International Conference on Advanced Computational Intelligence
会议日期March 27-29, 2015
会议地点Mount Wuyi, Fujian, China
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.
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/14313
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],2015.
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