A novel adaptive iterative learning control approach and human-in-the-loop control pattern for lower limb rehabilitation robot in disturbances environment

This article presents a novel adaptive iterative learning control (AILC), and designs a human-in-loop control pattern (HIL-CP), which simulates the proposed approach using different lower limb rehabilitation robot models. The stability of the AILC controller is proposed and verified via a Lyapunov-like function, where novel controller shows strong robustness in disturbances environment. Based on AILC, the core of the HIL-CP interactive control mode is to estimate the human surface electromyography by neural network model and get the real-time desired trajectory to iterate out the optimal actual tracking trajectory, which reduce the tracking error quickly and ensure the rehabilitation training effect of patients. Furthermore, the MATLAB software is employed to conduct simulation experiments the proposed approach. The simulation results show that the HIL-CP is highly efficient and rapidly convergent in a satisfied degree. The angle error is 0.25 degrees +/- 0.2 degrees for patients and 0.03 degrees +/- 0.02 degrees for healthy people. Compared with the existing sliding mode controller, it is proven that the AILC controller is much more effective and noise-tolerant ability in the presence of bounded nonlinear disturbance.

Automated summary

This article introduces a novel adaptive iterative learning control (AILC) method and designs a human-in-loop control pattern (HIL-CP) for simulation. By estimating human surface electromyography and obtaining real-time desired trajectories, the AILC controller can quickly reduce tracking errors and ensure effective rehabilitation training for patients. Simulation results demonstrate high efficiency and rapid convergence of the HIL-CP.