ARISE Control of an Uncertain Lower-Limb Hybrid Exoskeleton

Neurological conditions (NCs) such as spinal cord injury, stroke, and Parkinson's disease have been affecting many individuals by deteriorating their standard of living by not letting them perform physical activities, resulting in inactive lifestyles that cause conditions such as diabetes, obesity, cardiovascular disease, etc. Thus, there is a need to overcome the inactive lifestyle caused by NCs. Many potential solutions have been introduced, but each are limited in someway. For example, functional electrical stimulation (FES), which bypasses typical neurological processes to activate an individual's muscles (active therapy), is one of the capable solutions, but it results in the patient becoming fatigued quicker than normal. In an effort to improve FES-based therapies, this paper develops a novel robust and adaptive control structure for a combined FES and robotic system called a hybrid exoskeleton. Specifically, for the first time, a recently developed ARISE control approach is modified for a hybrid exoskeleton. Furthermore, the control law is augmented with neural networks that learn uncertainties in the dynamic model. To evaluate the performance of the control system, a Lyapunov-based stability analysis is performed.