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Adaptive impedance control method for manipulator based on radial basis function

Shufeng Tang (School of Mechanical Engineering, Inner Mongolia University of Technology – Xincheng District Campus, Hohhot, China and Inner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot, China)
Zhijie Chai (School of Mechanical Engineering, Inner Mongolia University of Technology – Xincheng District Campus, Hohhot, China and Inner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot, China)
Xin Wang (School of Mechanical Engineering, Inner Mongolia University of Technology – Xincheng District Campus, Hohhot, China and Inner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot, China)
Hong Chang (School of Mechanical Engineering, Inner Mongolia University of Technology – Xincheng District Campus, Hohhot, China and Inner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot, China)
Xiaodong Guo (School of Mechanical Engineering, Inner Mongolia University of Technology – Xincheng District Campus, Hohhot, China and Inner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot, China)

Industrial Robot

ISSN: 0143-991X

Article publication date: 3 October 2024

33

Abstract

Purpose

In view of the unknown environmental parameters and uncertain interference during gripping by the manipulator, it is difficult to obtain an effective gripping force with the traditional impedance control method. To avoid this dilemma, the purpose of this study is to propose an adaptive control strategy based on an adaptive neural network and a PID search optimization algorithm for unknown environments.

Design/methodology/approach

The method is based on a variable impedance model, and a new impedance model is established using a radial basis function (RBF) neural network to estimate unknown parameters of the impedance model. The approximation errors of the adaptive neural network and the uncertain disturbance are effectively suppressed by designing the adaptive rate. In the meantime, auxiliary variables are constructed for Lyapunov stability analysis and adaptive controller design, and PSA is used to ensure the stability of the adaptive impedance control system. Based on the Lyapunov stability criterion, the adaptive im-pedance control system is proved to have progressive tracking convergence property.

Findings

Through comparative simulations and experiments, the superiority of the proposed adaptive control strategy in position and force tracking has been verified. For objects with low flexibility and light-weight (such as a coke, a banana and a nectarine), this control method demonstrates errors of less than 10%.

Originality/value

This paper uses RBF neural networks to estimate unknown parameters of the impedance model in real-time, enhancing system adaptability. Neural network weights are updated online to suppress errors and disturbances. Auxiliary variables are designed for Lyapunov stability analysis. The PSA algorithm is used to adjust controller parameters in real-time. Additionally, comparative simulations and experi-ments are designed to analyze and validate the performance of controller.

Keywords

Acknowledgements

Funding: Research supported by Key research projects of military-civilian integration of Inner Mongolia Autonomous Region (Grant No. JMZD202203), Key Technology Research Program of Inner Mongolia (Grant No. 2021GG0258), the Natural Science Foundation of Inner Mongolia (Grant No. 2021MS05005), the National Key R&D Program of China (Grant No. 2018YFB1307501), and the Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region (Grant No. NMGIRT2213). This support is gratefully acknowledged by the authors.

Citation

Tang, S., Chai, Z., Wang, X., Chang, H. and Guo, X. (2024), "Adaptive impedance control method for manipulator based on radial basis function", Industrial Robot, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IR-07-2024-0327

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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