An adaptive iterative learning control approach for lower limb rehabilitation robot in noisy environments

Abstract

Rehabilitation training is usually a long-term systematic process for the patients of lower limb paralysis. Using robot-assisted rehabilitation training can improve efficiency, reduce costs and the burden on physical therapists, therefore, the resources for rehabilitation can be saved. For the lower extremity patients with motor dysfunction, a new three degree of freedom (3DOF) lower limb rehabilitation robot (LLRR) is designed, investigated and analyzed which is used in the passive phase of patient rehabilitation training. At first, a simple LLRR structure is designed that can be adjusted to fit the patient at the hip, knee, and ankle joints. Then, an adaptive iterative learning control (AILC) approach is designed for the kinematics model of the LLRR. Moreover, the asymptotic stability of the LLRR system is verified via a Lyapunov-like function. Furthermore, the desired trajectories are tracked for each joint of the LLRR. In addition, the AILC also overcomes disturbance in noisy environments and eliminates patient interference. Finally, LLRR is experimentally verified by the MATLAB and OpenSim software, which verifies that the proposed control approach is feasible and effective for the lower extremity patients. Thereby, the developed control approach has illustrated high efficiency and robustness for the patient's passive rehabilitation training in real-time.