Adaptive Neural Network Force Tracking Control of Flexible Joint Robot With an Uncertain Environment
Xinbo Yu, Sisi Liu, Shuang Zhang, Wei He, Haifeng Huang
Abstract
In this paper, a control scheme of the flexible joint robot contacting with an unknown environment is proposed to realize force tracking. Tracking performance is ensured by designing the force-based outer loop and the position-based inner loop of the controller. The reference trajectory is obtained from the outer loop based on interaction force error and the estimated environment stiffness. The inner loop controller of the flexible joint robot based on the singular perturbation method is designed to achieve precise position tracking performance, and neural network is utilized to compensate for uncertainties in robotic dynamics. The stability of the control system is strictly proven by the Lyapunov method. The effectiveness of the proposed method is verified by simulations and experiments on the flexible joint robot.