Iterative learning control with input sharing for multi-agent consensus tracking

2016 ◽  
Vol 94 ◽  
pp. 97-106 ◽  
Author(s):  
Shiping Yang ◽  
Jian-Xin Xu ◽  
Xuefang Li
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Consensus Tracking ◽  
Multi Agent ◽  
Input Sharing

Distributed adaptive iterative learning control for the consensus tracking of heterogeneous nonlinear multi-agent systems

2020 ◽  
Vol 42 (13) ◽  
pp. 2396-2409
Author(s):  
Xiongfeng Deng ◽  
Xiuxia Sun
Keyword(s):  
Neural Network ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Tracking Problem ◽  
Multi Agent Systems ◽  
Adaptive Iterative Learning Control ◽  
Agent Systems ◽  
Consensus Tracking ◽  
Multi Agent

This paper addresses the consensus tracking problem of leader-following heterogeneous multi-agent systems with iterative learning control. The model of heterogeneous multi-agent systems consists of first-order and second-order nonlinear dynamics. It is assumed that only a portion of following agents can receive the leader’s information. The radial basis function neural network is introduced to deal with the nonlinear terms of following agents. Then, the distributed adaptive iterative learning control protocols with neural network are designed for following agents with different dynamics. Meanwhile, the adaptive update control laws for the time-varying parameters are proposed. Theoretical analysis shows that the consensus tracking problem of the given multi-agent systems can be guaranteed on the time domain and iterative domain. Finally, the validity of theoretical results is verified by a simulation example.

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