Regular Papers

International Journal of Control, Automation and Systems 2022; 20(7): 2340-2352

Published online June 9, 2022

https://doi.org/10.1007/s12555-021-0146-5

© The International Journal of Control, Automation, and Systems

Design of Adaptive RBFNN and Computed-torque Control for Manipulator Joint Considering Friction Modeling

Xiaobin Shen, Kun Zhou, Rui Yu, and Binrui Wang*

China Jiliang University

Abstract

In this paper, we aim to improve the tracking performance of the manipulator joint system by establishing accurate friction model based on the Stribeck model and the cubic polynomial method. Meanwhile, in view of the established system model, an adaptive Radial Basis Function Neural Network (RBFNN) compensation computedtorque controller is designed for the manipulator joint system. Firstly, we consider the friction modeling process at low- and high- velocity regions to advance the model accuracy, and identify the parameters in the friction model equation offline via the particle swarm optimization (PSO) algorithm. Secondly, an adaptive RBFNN algorithm is developed to analyze the unmodeled dynamics online and introduce it to the computed-torque controller design. After that, we further conduct the stability analysis for the proposed controller based on the Lyapunov stability criterion. Finally, the self-developed manipulator joint platform introduction, the simulation experiment and the contradistinctive experiments are given to illustrate the effectiveness of designed controller.

Keywords Adaptive control, computed-torque control, friction model, manipulator joint, radial basis function neural network (RBFNN).

Article

Regular Papers

International Journal of Control, Automation and Systems 2022; 20(7): 2340-2352

Published online July 1, 2022 https://doi.org/10.1007/s12555-021-0146-5

Copyright © The International Journal of Control, Automation, and Systems.

Design of Adaptive RBFNN and Computed-torque Control for Manipulator Joint Considering Friction Modeling

Xiaobin Shen, Kun Zhou, Rui Yu, and Binrui Wang*

China Jiliang University

Abstract

In this paper, we aim to improve the tracking performance of the manipulator joint system by establishing accurate friction model based on the Stribeck model and the cubic polynomial method. Meanwhile, in view of the established system model, an adaptive Radial Basis Function Neural Network (RBFNN) compensation computedtorque controller is designed for the manipulator joint system. Firstly, we consider the friction modeling process at low- and high- velocity regions to advance the model accuracy, and identify the parameters in the friction model equation offline via the particle swarm optimization (PSO) algorithm. Secondly, an adaptive RBFNN algorithm is developed to analyze the unmodeled dynamics online and introduce it to the computed-torque controller design. After that, we further conduct the stability analysis for the proposed controller based on the Lyapunov stability criterion. Finally, the self-developed manipulator joint platform introduction, the simulation experiment and the contradistinctive experiments are given to illustrate the effectiveness of designed controller.

Keywords: Adaptive control, computed-torque control, friction model, manipulator joint, radial basis function neural network (RBFNN).

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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eISSN 2005-4092
pISSN 1598-6446