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https://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.C. |
Citation: | International Journal of Systems Science, 2020; 51(11):2025-2040 |
Publisher: | Taylor & Francis |
Issue Date: | 2020 |
ISSN: | 0020-7721 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 |
DOI: | 10.1080/00207721.2020.1783385 |
Grant ID: | http://purl.org/au-research/grants/arc/DP170102644 |
Published version: | http://dx.doi.org/10.1080/00207721.2020.1783385 |
Appears in Collections: | Aurora harvest 8 Electrical and Electronic Engineering publications |
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