Adaptive hybrid-mode assist-as-needed control of upper limb exoskeleton for rehabilitation training

Abstract

In this paper, a novel adaptive hybrid-mode assist-as-needed (AHMAAN) control algorithm is designed for the exoskeleton-assisted upper limb rehabilitation training. The overall control framework includes an outer control loop to calculate the required interaction force, and an inner control loop to drive the exoskeleton track subject’s motion, and provide specific target interaction force obtained from the outer control loop. In the outer control loop, a hybrid control mode is proposed, which consists of resistive mode and assistive mode. In this regard, a virtual tunnel is firstly established around the defined training task path, and the training mode is switched according to the deviation between the subject’s position and the boundary of the virtual tunnel. Furthermore, for tuning the strength of the resistance or assistance to the subjects with different motor capabilities, two adjustable gain factors are designed, whose values are adaptively adjusted according to the subject’s training performance by using a fuzzy logic. Then, for the inner control loop, a barrier Lyapunov function-based controller is designed to constrain exoskeleton tracking errors within the defined boundary. Meanwhile, time delay estimation (TDE) technology is used to estimate the uncertain terms of the system, and a robust adaption law is developed to compensate TDE error. Experimental tests have been performed on an upper limb exoskeleton with three healthy subjects to evaluate the effectiveness of the developed method. The results show that the proposed control scheme can be effectively applied in a variety of rehabilitation requirements and achieves better training performance than classical hybrid-mode assist-as-needed control method.