Iterative Learning Control for Cascaded Impedance-Controlled Compliant Exoskeleton With Adaptive Reaction to Spasticity
Lin Liu, Mathias Illian, Steffen Leonhardt, Berno J.E. Misgeld
Abstract
The cascade impedance control (CIC) is a wellknown architecture framework for rehabilitation exoskeleton with compliant joint (e.g., a series elastic actuator). The cascaded position-torque-velocity control loop of the CIC system can deal with specific issues, such as stiction or the accuracy of output impedance. In this paper, the control loops of the CIC are extended by the traditional iterative learning control (ILC), and then we examine and compare three types of frameworks, named torque learning, impedance learning, and trajectory learning. Their advantages, such as reducing the lag of the output trajectory with a low-gain impedance controller (safety), are discovered. Furthermore, the exoskeleton system is upgraded with the ability of a variable impedance. In this part, a fuzzy logic system is proposed. This system employs the electromyography signals of the subject and the exoskeleton torque as input, and the decisions on the variable impedance as output. The experiment verifies that the proposed algorithm can effectively decrease the impedance of the exoskeleton when detecting a spasticity from the subject, and can maintain the original dynamics of the system when the subject performs a normal movement. Afterwards, the effectiveness of the fuzzy logic system together with the ILC-based CIC is experimentally verified in the case of subject-exoskeleton collaborative.