CASIA OpenIR  > 复杂系统认知与决策实验室  > 飞行器智能技术
Adaptive flocking of multi-agent system with uncertain nonlinear dynamics and unknown disturbances using neural networks
Shiguang Wu1,2; Zhiqiang Pu1,2; Jianqiang Yi1,2; Jinlin Su1,2; Tianyi Xiong1,2; Tenghai Qiu3
2020
Conference Name16th IEEE International Conference on Automation Science and Engineering (CASE)
Conference DateAugust 20-21
Conference PlaceOnline
Abstract

 Collective behavior of multi-agent systems brings some new problems in control theory and application. Especially, flocking problem of multi-agent systems with uncertain nonlinear dynamics and unknown external disturbances is a challenging problem. Some existing works assume that the intrinsic nonlinear dynamics of virtual leader is the same as those of the agents, which is unreasonable and impractical. To solve this issue, we consider an adaptive flocking problem of multi-agent systems with uncertain nonlinear dynamics and unknown external disturbances in this paper, where the intrinsic nonlinear dynamics of virtual leader is allowed to be different from the agents. Firstly, to approximate the uncertain nonlinear dynamics of each agent, an adaptive neural network is used, whose weights are updated online. Furthermore, an adaptive robust signal is designed to counteract the unknown external disturbances and neural network approximation errors, which is independent with the upper bound of the unknown external disturbances and neural network approximation errors. Moreover, an adaptive flocking control law is designed, which is proved that the flocking can be realized and the velocity errors converge to a small neighbor of the origin based on Lyapunov stability theory. Finally, the robustness and superiority of the proposed robust adaptive flocking control law are validated by two representative simulations.
 

Indexed ByEI
Language英语
Sub direction classification多智能体系统
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47439
Collection复杂系统认知与决策实验室_飞行器智能技术
Corresponding AuthorZhiqiang Pu
Affiliation1.School of Artificail Intelligence, University of Chinese Academy of Sciences
2.Institute of Automation, Chinese Academy of Sciences
3.College of Mechanical and Electronic Engineering, Nanjing Forestry University
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shiguang Wu,Zhiqiang Pu,Jianqiang Yi,et al. Adaptive flocking of multi-agent system with uncertain nonlinear dynamics and unknown disturbances using neural networks[C],2020.
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