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Adaptive Neural Network Time-varying Formation Tracking Control for Multi-agent Systems via Minimal Learning Parameter Approach
Xiong Tianyi1,2; Pu Zhiqiang1,2; Yi Jianqiang1,2; Sui Zezhi1,2
2019
Conference Name2019 International Joint Conference on Neural Networks (IJCNN 2019)
Conference DateJuly 14-19, 2019
Conference PlaceBudapest, Hungary
PublisherInstitute of Electrical and Electronics Engineers Inc
Abstract

This paper investigates the time-varying formation tracking control problem for multi-agent systems with consideration of model uncertainties. For each dimension of an agent, a radial basis function neural network (RBFNN) is first adopted to approximate the model uncertainties online. Taking the square of the norm of the neural network weight vector as a newly developed adaptive parameter, a novel RBFNN-based adaptive control law with minimal learning parameter (MLP) approach is then constructed to tackle the time-varying formation tracking problem. The uniformly ultimately boundedness (UUB) of formation tracking errors is guaranteed through Lyapunov analysis. Compared with other traditional RBFNN-based formation tracking control laws for multi-agent systems, very few parameters need to be adapted online in our proposed one, which can greatly lessen the computational burden. Finally, comparative simulation results demonstrate the effectiveness and superiority of the proposed adaptive control law.

KeywordFormation Control Minimal Learning Parameter Multi-agent System Neural Network
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23662
Collection综合信息系统研究中心
Affiliation1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
2.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xiong Tianyi,Pu Zhiqiang,Yi Jianqiang,et al. Adaptive Neural Network Time-varying Formation Tracking Control for Multi-agent Systems via Minimal Learning Parameter Approach[C]:Institute of Electrical and Electronics Engineers Inc,2019.
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