Iterative Learning Impedance for Lower Limb Rehabilitation Robot

Abstract EN

This paper discusses the problem of squatting training of stroke patients.

The main idea is to correct the patient’s training trajectory through an iterative learning control (ILC) method.

To obtain better rehabilitation effect, a patient will typically be required to practice a reference posture for many times, while most of active training methods can hardly keep the patients training with correct posture.

Instead of the conventional ILC strategy, an impedance-based iterative learning method is proposed to regulate the impedance value dynamically and smartly which will help patients correct their posture gradually and perform better.

To facilitate impedance-based ILC, we propose two objectives.

The first objective is to find the suitable values of impedance based on the ILC scheme.

The second objective is to search the moderate learning convergence speed and robustness in the iterative domain.

The simulation and experimental results demonstrate that the performance of trajectory tracking will be improved greatly via the proposed algorithm.