Uncertainty Compensated High-Order Adaptive Iteration Learning Control for Robot-Assisted Upper Limb Rehabilitation

Upper limb rehabilitation robot can assist stroke patients to complete daily activities to promote the recovery of upper-limb motor functions. However, the robot uncertainty and the patient’s unconscious disturbance impose great difficulties on the high-performance trajectory tracking of the rehabilitation robot. In this paper, an uncertainty compensated high-order adaptive iterative learning controller (UCHAILC) is proposed to reduce the impact of uncertainty from inside and outside of the robot during the rehabilitation process. The nonlinear system is converted into a dynamic linearization model with uncertainty compensation, and the optimization criterion method is adopted to estimate the pseudo-partial derivative (PPD) parameters and the uncertainty respectively, then the previous iterations are used to update the current parameters through a high-order learning scheme. The convergence of UCHAILC is theoretically proved. Simulation and control experiments on a rehabilitation robot are given to validate the effectiveness of the proposed method, which is significant to improve the training security and physiotherapy effect of robot-assisted rehabilitation. Note to Practitioners—This paper was motivated by the need to assist stroke patients to restore motor function for executing daily activities. The inherent difficulties lie in reducing the tracking errors of rehabilitation robots caused by uncertainty and involuntary disturbance from patients to avoid secondary injury. The proposed UCHAILC can transform the complex nonlinear system into a dynamic linear model with uncertainty compensation, then the PPD parameters and uncertainty are estimated through high-order learning law. Theoretical analysis, simulation, and experiments verified the feasibility of the method. Furthermore, the proposed controller is not limited to the dynamic model and hardware driving mode of the robot system, which can be easily transplanted to other nonlinear control systems with uncertainties.