Impedance Iterative Learning Sliding Mode Control for Robot-Assisted Bathing with Non-repetitive Trajectory

Abstract

In this paper, an impedance iterative learning sliding mode control (IILSMC) scheme for robot-assisted bathing with unknown model parameters is investigated. Firstly, impedance control is not only applied to adjust the desired trajectory, but also implemented to ensure the active compliance control of the robot-assisted bathing. Furthermore, iterative learning control is designed to dynamically estimate the unknown model parameters. To release the identical initial condition of iterative learning control (ILC), the trajectory reconstruction method is proposed, such that the convergence of tracking errors can be guaranteed with random initial status. Moreover, adaptive sliding mode control (SMC) is proposed where non-parametric, external disturbances and the human-machine interaction (HMI) torque generated during the bathing process is suppressed by adaptive method. Based on the composite energy function (CEF) method, the convergence of the double closed-loop system in the time domain and iterative domain is proved. Finally, through co-simulation of MATLAB/Simulink and ADAMS, the effectiveness and superiority of the control strategy proposed in this paper is jointly verified.