Autonomous Locomotion Trajectory Shaping and Nonlinear Control for Lower Limb Exoskeletons

This article presents a strategy for autonomous locomotion trajectory planning for high-level control of lower limb exoskeletons by defining a novel set of adaptive central pattern generators (ACPGs) to facilitate safe and compliant interaction with the human. A time-varying bounded-gain adaptive disturbance observer is designed for estimating the human-robot interaction (HRI) needed for online central pattern generator (CPG)-based trajectory shaping and low-level nonlinear trajectory tracking control. The proposed ACPG dynamics for each exoskeleton joint updates the motion frequency and amplitude based on the observed HRI torque, which is also coupled with adjacent joints' CPGs to regulate their phase differences in real time. An integrated Lyapunov analysis is conducted to ensure the closed-loop system's stability and uniformly ultimately boundedness of both the tracking error and the torque estimation error in the controlled exoskeleton. Experimental studies are performed with an able-bodied human wearer by applying arbitrary torques on the exoskeleton's joints in order to evaluate the proposed autonomous control strategy in online adjustment and personalization of the locomotion.