Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/127314
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Type: Journal article
Title: Neural network adaptive dynamic sliding mode formation control of multi-agent systems
Author: Fei, Y.
Shi, P.
Lim, C.
Citation: International Journal of Systems Science, 2020; 51(11):2025-2040
Publisher: Taylor & Francis
Issue Date: 2020
ISSN: 1464-5319
1464-5319
Statement of
Responsibility: 
Yang Fei , Peng Shi and Cheng-Chew Lim
Abstract: This paper considers the problem of achieving time-varying formation for second-order multi-agent systems with actuator hysteresis, unknown system dynamics and external disturbances. A novel adaptive dynamic sliding mode scheme is developed to control a group of agents to follow desired trajectories. First, a dynamic sliding mode approach based on local formation tracking error is utilized to reject external disturbances and obtain smooth and chattering-free control input. Then Chebyshev neural network is employed to estimate the nonlinear function related to the system's dynamic equation. A smooth projection law is also applied to regulate the output of the neural network. Moreover, a Bouc-Wen hysteresis compensator has been added to the current control law to o set the known actuator hysteresis effect. Finally, a numerical simulation based on a multiple omni-directional robot system is presented to illustrate the performance of the proposed control law.
Keywords: Multi-agent systems; dynamic sliding mode control; Chebyshev neural network; formation control; Bouc–Wen hysteresis
Rights: © 2020 Informa UK Limited, trading as Taylor & Francis Group
RMID: 1000023088
DOI: 10.1080/00207721.2020.1783385
Grant ID: http://purl.org/au-research/grants/arc/DP170102644
Appears in Collections:Electrical and Electronic Engineering publications

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