Radial basis function neural network based second-order sliding mode control for robotic manipulator

Chen, Jiqing; Tang, Qingsong; Zhao, Chaoyang; Zhang, Haiyan

doi:10.1177/09544062221104588

Cited by 2 publications

(1 citation statement)

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“…Lewis et al (1996) designed an RBF neural network adaptive controller that used the RBF neural network to approximate the uncertain terms of the manipulator model so that SMC could get rid of the dependence on the dynamic model information and realize joint trajectory tracking control. Chen et al (2019Chen et al ( , 2022 selected different neural network structures to improve SMC and enhance the dynamic performance of the system to varying degrees. Even so, the accuracy of the whole system is inevitably affected by the errors of the neural network.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode control based on particle swarm optimization neural network and adaptive reaching law

Chen,

Zhang,

Pan

et al. 2023

Transactions of the Institute of Measurement and Control

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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.

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Section: Introductionmentioning

confidence: 99%