Title: Compliance control method for robot joint with variable stiffness

Authors: Jifu Wen; Gang Wang; Jingchao Jia; Wenjun Li; Chengyao Zhang; Xin Wang

Addresses: School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China ' School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China ' School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China ' School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China ' School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China ' School of Engineering, Monash University Malaysia, Selangor 47500, Malaysia

Abstract: Aiming at the disadvantages of insufficient flexibility and poor stability of the traditional control methods of variable stiffness joint robots, a new multi-degree-of-freedom robot joint compliance control method is proposed. The multi-degree-of-freedom robot joint system is introduced, and the multi-degree-of-freedom robot dynamic model is constructed using the Lagrangian method. On this basis, the control algorithm based on feedback linearisation and adaptive RBF neural network realises the compliance control of the multi-degree-of-freedom robot manipulator wrist joint. First, the dynamic model of the robot joint is analysed, and the nonlinear state-space model is linearised using the feedback linearisation method. Then, the fourth-order Runge-Kutta method is used to improve the flexibility of robot joint control when solving the dynamic model, and carried out simulation verification. The simulation results show that the proposed method can converge faster in the control process of the desired angle and the desired stiffness of the variable stiffness joint, and it is robust to the uncertainty of the robot system and the changing external interference.

Keywords: robot; variable stiffness; dynamics; radial basis function neural network; radial basis function; RBF.

DOI: 10.1504/IJHM.2023.129125

International Journal of Hydromechatronics, 2023 Vol.6 No.1, pp.45 - 58

Received: 12 May 2022
Accepted: 07 Jun 2022
Published online: 21 Feb 2023 *

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