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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 9/2018

20-05-2017 | Original Article

Neural network observer-based leader-following consensus of heterogenous nonlinear uncertain systems

Authors: Zhilin Liu, Li Su, Zongyang Ji

Published in: International Journal of Machine Learning and Cybernetics | Issue 9/2018

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Abstract

This paper considers the leader-following consensus of heterogeneous multiple agents with high-order nonlinear uncertain systems. Previous results in this field consider the leader’s dynamics as disturbances, which may lead to oscillation, overshoot, or even unstability of the whole system due to high-gain consensus control. This study considers neural network (NN) observer-based leader-following consensus which can avoid high gain at the consensus control. First of all, distributed NN-based leader observers are designed to estimate the leader’s states and nonlinear dynamics. Theoretical analysis by Lyapunov theory is followed to illustrate the effectiveness of the observers. Then, to obtain the leader-following consensus, NN controllers are designed for the following agents to track the corresponding leader observers. Theoretical proof and simulation results illustrate that the leader-following consensus errors are uniformly ultimately bounded (UUB) and can be made arbitrarily small by an appropriate choice of corresponding gains.
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Metadata
Title
Neural network observer-based leader-following consensus of heterogenous nonlinear uncertain systems
Authors
Zhilin Liu
Li Su
Zongyang Ji
Publication date
20-05-2017
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 9/2018
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0654-z

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