Constrained Online Optimal Control for Continuous-Time Nonlinear Systems Using Neuro-Dynamic Programming

被引：0

作者：

Yang Xiong ^{[1
]}

Liu Derong ^{[1
]}

Wang Ding ^{[1
]}

Ma Hongwen ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China

来源：

2014 33RD CHINESE CONTROL CONFERENCE (CCC) | 2014年

关键词：

Constrained input; Neuro-dynamic programming; Nonlinear systems; Online control; Optimal control;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper develops an online adaptive optimal control scheme to solve the infinite-horizon optimal control problem of continuous-time nonlinear systems with control constraints. A novel architecture is presented to approximate the Hamilton-Jacobi-Bellman equation. That is, only a critic neural network is used to derive the optimal control instead of typical actioncritic dual networks employed in neuro-dynamic programming methods. Meanwhile, unlike existing tuning laws for the critic, the newly developed critic update rule not only ensures convergence of the critic to the optimal control but also guarantees the closed-loop system to be uniformly ultimately bounded. In addition, no initial stabilizing control is required. Finally, an example is provided to verify the effectiveness of the present approach.

引用

页码：8717 / 8722

页数：6

共 20 条

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