Research on Robot-Assisted Bathing Based on Impedance Iterative Learning Sliding Mode Control Algorithm

Aiming at providing effective technical means for intelligent bathing and low-intensity care for the semi-disabled elderly, an impedance iterative learning sliding mode control (IILSMC) scheme for robot-assisted bathing with unknown model parameters is investigated in this paper. Firstly, the desired trajectory is adjusted by impedance control in order to ensure the active compliance control of the robot-assisted bathing. Secondly, iterative learning control (ILC) is implemented to dynamically estimate the unknown model parameters, and reconstructed trajectory method is employed to ensure the convergence accuracy of iterative learning with any initial conditions. Thirdly, as for handling nonparametric uncertainties, external disturbances and the human-machine interaction (HMI) torque, adaptive sliding mode control (SMC) is proposed where the chattering problem in output torque 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 and ADAMS, tracking errors of all joint angles can be maintained within 0.002rad with constant HMI force. The simulation results demonstrate that the IILSMC strategy is verified to be effective and superior.