Control and trajectory generation of a lower limb exoskeleton for individuals with mobility disorders

Abstract

Mobility disorders affect millions of people worldwide, often leaving them reliant on wheelchairs for everyday living and causing numerous health issues. This work introduces a control structure and method for generating trajectories for a mobility exoskeleton. The AidLeg exoskeleton is used in this study, which features active joints in the sagittal plane at the hip and knee joints, passive ankle joints, and crutches for balance. This study identifies four essential tasks in the daily activities of individuals with mobility disorders: sit-to-stand, walking, stairs, and stand-to-sit. Based on these tasks, a finite state machine is proposed to constrain the transition between movements, and specific gait generation methods are designed for each task. For the sit-to-stand task, a starting posture is designed to reduce upper limb involvement and improve comfort. For the walk task, a novel online trajectory generation method is proposed to aid users in various terrains, including level ground, slopes, and obstacle avoidance. For the stair task, a novel online trajectory generation method is proposed using a finite state machine model, Bézier curve-based path interpolation approach, and time scaling and path reparameterization method. For the stand-to-sit task, a control algorithm using torque control based on velocity feedback is designed to compensate for gravity and make the user feel like they are actively sitting down. Based on the characteristics of these four methods, a low-level controller is designed to compute the continuous closed-loop control output required for the individual motors to produce the desired joint response. Simulations and real exoskeleton experiments demonstrate that the proposed methods effectively help users achieve the four tasks and provide comfort. The robust control structure and novel trajectory generation method provide a comprehensive and realistic mobility option for individuals with mobility disorders.

Date of Award	2023
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Ling SHI (Supervisor)