TP273

Robust containment control of multi-agent networks based on zero-sum game
Author:
Affiliation:

Beijing Information Science and Technology University

Fund Project:

Supported by the Subject Group Construction Project of Beijing Information Science and Technology University

• 摘要
• |
• 图/表
• |
• 访问统计
• |
• 参考文献
• |
• 相似文献
• |
• 引证文献
• |
• 资源附件
• |
• 文章评论
摘要:

针对受扰非线性多智能体网络, 本文研究分布式鲁棒包容控制方法. 采用微分博弈理论将有界$\mathcal {L}_{2}$增益包容控制问题描述成多玩家零和博弈问题. 对于每个跟随者，至少存在一个领航者与其存在有向路径通信时, 基于局部邻居信息定义每个跟随者的性能指标, 从而得出包容误差$\mathcal {L}_{2}$有界且零和博弈解存在的结论. 在系统动态完全未知的情况下, 采用积分强化学习算法和执行—评价—干扰网络, 在线得到近似最优策略. 仿真结果表明了本文所提方案的有效性和正确性.

Abstract:

The distributed robust containment control methods are investigated in the paper for disturbed nonlinear multi-agent networks. Applying differential game theory, the bounded $\mathcal {L}_{2}$ gain containment control problem is described as multi-player zero-sum game one. Suppose there exists at least one leader that has a directed path from it to each follower, its performance index is defined based on the information of local neighbors. Furthermore, it is proved that the containment errors are $\mathcal {L}_{2}$ bounded and there exists Nash equilibrium solution. With the completely unknown system dynamics, the integral reinforcement learning method and critic-actor-disturbance neural networks are used to solve the approximate optimal strategy online. Simulation results verify the effectiveness and validity of the proposed scheme.

参考文献
相似文献
引证文献

• 点击次数:
• 下载次数:
• HTML阅读次数:
• 引用次数:
##### 历史
• 收稿日期:2019-09-25
• 最后修改日期:2020-07-29
• 录用日期:2020-05-12
• 在线发布日期:
• 出版日期: