Adaptive Consensus Tracking Control for Nonlinear Multi-Agent Systems with Input Saturation and Dead-Zone

This paper investigates the adaptive consensus tracking control problem for multi-agent systems (MASs) with input saturation and dead-zone. Radial basis function neural networks (RBF NNs) are utilized to estimate the unknown nonlinear functions. An adaptive consensus tracking control scheme is propo...

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Bibliographic Details
Published in2019 IEEE Symposium Series on Computational Intelligence (SSCI) pp. 3281 - 3286
Main Authors Li, Shubo, Pan, Yingnan, Liang, Hongjing
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Backstepping
Complexity theory
consensus tracking control
dynamic surface control
Explosions
input saturation and dead-zone
Lyapunov methods
Multi-agent systems
Protocols
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Summary:This paper investigates the adaptive consensus tracking control problem for multi-agent systems (MASs) with input saturation and dead-zone. Radial basis function neural networks (RBF NNs) are utilized to estimate the unknown nonlinear functions. An adaptive consensus tracking control scheme is proposed by the backstepping technique and Lyapunov stability theory. The proposed controller guarantees that all the closed-loop system signals are bounded and the outputs of followers can track the leader. Finally, numerical example demonstrates the effectiveness of the obtained results.
DOI:10.1109/SSCI44817.2019.9002951