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|Title:||Neural network adaptive dynamic sliding mode formation control of multi-agent systems|
|Citation:||International Journal of Systems Science, 2020; 51(11):2025-2040|
|Publisher:||Taylor & Francis|
|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|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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