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.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
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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