Compliant adaptive control of human upper-limb exoskeleton robot with unknown dynamics based on a Modified Function Approximation Technique (MFAT)

Rehabilitation robots have shown a high potential for improving the patients’ mobility, improving their functional movements and assisting in daily activities. However, this technology is still an emerging field and suffers from several challenges like compliance control and dynamic uncertain caused by the human–robot collaboration. The main challenge addressed in this paper is to ensure that the exoskeleton robot provides a suitable compliance control that allows it to cooperate perfectly with humans even if the dynamic model of the exoskeleton robot is uncertain. To achieve that, an adaptive tracking controller based on Modified Function Approximation Technique (FAT)is proposed to approximate the dynamic model of the exoskeleton robot. Unlike a conventional FAT, the required use of basis functions in estimations law of dynamic model and the acceleration feedback is eliminated in the proposed modified FAT. Additionally, the desired trajectory is designed based on the designer's prediction of the motion intention of the human, using the Damped Least Square method in order to reduce the error between the actual position of the robot and the motion intention of the human which help the subject move the exoskeleton arm easily in active rehabilitation tasks. The stability analysis is formulated and demonstrated based on Lyapunov function. An experimental physiotherapy session and comparison study with a healthy subject was performed to test the effectiveness and robustness of the proposed adaptive control.