L
Luo, Yiping
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Fixed-time formation control of second-order nonlinear multi-agent systems using the neural network dynamic sliding mode

Abstract

The problem of fixed-time formation control for a class of second-order nonlinear multi-agent systems is studied. For a class of such systems, a control algorithm is proposed to maintain the connections among the agents while avoiding collisions. Furthermore, a radial basis function neural network is used in the design to precisely approximate the nonlinear function for the nonlinear terms in the model. Then, a dynamic sliding mode control method is proposed to suppress the chattering phenomenon that may arise due to the sliding mode control. A sufficient condition for the system to achieve fixed-time formation is obtained by using different methods, such as Lyapunov stability. Finally, the effectiveness of the proposed algorithm is verified by example. Simulation experiments reveal that the proposed method has faster error convergence and better robust control than conventional algorithms.

article Article
date_range 2024
language English
link Link of the paper
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L
Luo Yiping
H
Huang Weijie
C
Cao Jinde
X
Xia Wenhua
W
Wang Tianyu
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Featured Keywords
collision avoidance
connectivity preservation
dynamic SMC
formation tracking
RBF neural network
second-order multi-agent systems
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