Knowledge Commons of Institute of Automation,CAS
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 | 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 | Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, 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,Li, Hongliang,Liu, Derong. Neural-Network-Based Finite Horizon Optimal Control for Partially Unknown Linear Continuous-Time Systems[C],2015. |
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