Saturated Auxiliary Robust Integral of the Sliding ModE (ARISE) Controllers for Hybrid Exoskeletons

Abstract

Abstract Millions of Americans are affected by neurological conditions (NCs), which can be caused by spinal cord injuries, stroke, Parkinson's disease, and more. NCs can significantly reduce the quality of life of those afflicted by impairing motor control, reducing muscle mass, and decreasing bone density while increasing the risk of secondary health conditions. Individuals with NCs often face significant challenges in daily life, demonstrating the need for improved rehabilitation and recovery options. One device used to improve recovery is a hybrid exoskeleton. A hybrid exoskeleton combines robots (to provide assistance or resistance) with functional electrical stimulation (to promote active user participation during the exercise). Although hybrid exoskeletons have proven beneficial for rehabilitation, their nonlinear and often uncertain dynamics can make them difficult to control. Therefore, the focus of this thesis is on the improvement of FES-based therapies. Specifically, this thesis develops a novel robust and adaptive control structure for hybrid exoskeletons: the saturated Auxiliary Robust Integral of the Sliding ModE (ARISE) control method.