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- Title
Sliding mode control based on particle swarm optimization neural network and adaptive reaching law.
- Authors
Chen, Jiqing; Zhang, Haiyan; Pan, Shangtao; Tang, Qingsong
- Abstract
This paper presents a sliding mode control based on particle swarm optimization neural network and adaptive reaching law, and the proposed control method solves the problem of chattering and tracking performance degradation of a multi-joint manipulator caused by uncertainties such as external disturbances and modeling error. First, to address the problem that the precise dynamic system of the manipulator is difficult to establish, the radial basis function neural network (RBFNN) is used to approximate the uncertainty of the manipulator model, and the parameters of the neural network are optimized through the adaptive natural selection particle swarm optimization algorithm (ASelPSO) to improve the approximation ability and reduce the approximation error. Second, to eliminate chattering, adaptive reaching law is selected to improve the dynamic quality of approaching motion. Finally, a comparative simulation experiment is carried out with a 3-DOF manipulator as the research object. The results show that the control method has obvious improvements in eliminating chattering, improving tracking accuracy, and increasing convergence speed, which verifies the feasibility and superiority of the control scheme.
- Subjects
SLIDING mode control; PARTICLE swarm optimization; RADIAL basis functions; DYNAMICAL systems; APPROXIMATION error
- Publication
Transactions of the Institute of Measurement & Control, 2024, Vol 46, Issue 4, p741
- ISSN
0142-3312
- Publication type
Article
- DOI
10.1177/01423312231186214