Data-driven robust containment control of multi-agent networks

被引：0
|
作者：
Yu D. [1 ]
机构：
[1] College of Automation, Beijing Information Science and Technology University, Beijing
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Containment control; Data-driven; Integral reinforcement learning; Multi-agent networks;
D O I：
10.7641/CTA.2020.90433
中图分类号：
学科分类号：
摘要：
A novel robust containment control scheme is proposed for multi-agent networks with constrained input, including leaders layer, estimation layer, control layer and followers layer. At first, finite time estimators are designed to obtain the desired states of followers. Then a non-quadratic discounted cost function is introduced based on the containment errors, so the robust containment control problem is transformed into a constrained optimal control problem. Moreover, the uniform ultimate bounded stability is verified of whole networks with obtained optimal control policy according to Lyapunov extension theorem. When the dynamics of followers are completely unknown, the approximate optimal control policy is obtained online applying the developed integral reinforcement learning algorithm and actor-critic architecture. Simulation results are provided to demonstrate the effectiveness of the proposed scheme. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：1963 / 1970
页数：7
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