Reference trajectory learning based adaptive iterative impedance control for a lower limb rehabilitation exoskeleton with actuator saturation

In this paper, adaptive iterative learning impedance control is developed for a lower limb rehabilitation exoskeleton subject to unknown reference trajectory, unknown nonlinearities, and actuator saturation. A novel dual-loop learning control strategy is proposed for human-exoskeleton interaction, where the outer control loop is designed to follow a target impedance model and the inner position loop is constructed for tracking a balanced trajectory. First, the contact force between the patient and the exoskeleton is used to learn the reference trajectory in an iterative manner. Second, under the framework of backstepping technique, an adaptive iterative learning controller is developed to deal with the unknown nonlinearities and improve the tracking performance. In order to ensure the safety of patient’s limbs during human-exoskeleton interaction, the actuator saturation is considered and addressed by introducing an auxiliary system. Third, with the design of the reference trajectory learning algorithm and the adaptive iterative controller, the convergence of both the target impedance following and trajectory tracking is proved rigorously, and the boundedness of all the involved signals are guaranteed. Finally, the effectiveness of the proposed control scheme is verified by both simulation and experimental study on a 2-DOF exoskeleton.