Design and Adaptive Control for an Upper Limb Robotic Exoskeleton in Presence of Input Saturation.

This paper addresses the control design for an upper limb exoskeleton in the presence of input saturation. An adaptive controller employing the neural network technology is proposed to approximate the uncertain robotic dynamics. Also, an auxiliary system is designed to deal with the effect of input saturation. Furthermore, we develop both the state feedback and the output feedback control strategies, which effectively estimates the uncertainties online from the measured feedback errors, instead of the model-based control. In addition to the proposed control, a disturbance observer is designed to reject the unknown disturbance online for achieving the trajectory tracking. The method requires a minimal amount of a priori knowledge of system dynamics. Subsequently, the principle of Lyapunov synthesis ensures the stability of the closed-loop system. Finally, the experimental studies are carried out on this robotic exoskeleton.