Knowledge Commons of Institute of Automation,CAS
Neural-network-based robust optimal control of uncertain nonlinear systems using model-free policy iteration algorithm | |
Li, Chao1; Wang, Ding1; Liu, Derong2 | |
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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