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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Published in | 2019 IEEE Symposium Series on Computational Intelligence (SSCI) pp. 3281 - 3286 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.12.2019
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Subjects | |
Online Access | Get full text |
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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. |
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DOI: | 10.1109/SSCI44817.2019.9002951 |