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 computed-torque 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.
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This work was supported by the National Key Research and Development Project of China (Grant No. 2018YFB2101004, 2018YFC0809204).
Xiaobin Shen received his M.S. degree in China Jiliang University in 2020. His research interests include design of modular robot joint and joint torque control.
Kun Zhou received his Ph.D. degree in the School of Electrical Engineering at Southwest Jiaotong University in 2018. He is currently a lecturer in the College of Mechanical and Electrical Engineering at China Jiliang University, China. His research interests include intelligent control algorithm, delayed neural networks, and fuzzy control and applications.
Rui Yu received her B.S. and M.S. degrees in China Jiliang University, in 2017 and 2020, respectively. She is currently pursuing a Ph.D. degree in control science and engineering, TongJi University. Her research interests include adaptive control, nonlinear system, and multi-agents control.
Binrui Wang received his Ph.D. degree in pattern recognition and intelligent system from the School of Information Science and Engineering at Northeastern University in 2005. Currently, he is a professor in the College of Mechanical and Electrical Engineering at China Jiliang University, PRC. His research interests include intelligent control algorithm and humanoid robot.
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Shen, X., Zhou, K., Yu, R. et al. Design of Adaptive RBFNN and Computed-torque Control for Manipulator Joint Considering Friction Modeling. Int. J. Control Autom. Syst. 20, 2340–2352 (2022). https://doi.org/10.1007/s12555-021-0146-5
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DOI: https://doi.org/10.1007/s12555-021-0146-5