Switched Adaptive Concurrent Learning Control using a Stance Foot Model for Gait Rehabilitation using a Hybrid Exoskeleton

Abstract

Lower-limb hybrid exoskeletons integrate powered mechanisms and functional electrical stimulation (FES) to provide assistive forces and activate muscles for restoring gait function after a neurological injury. Particularly, improving the load-bearing ability is a primary rehabilitation goal. Hence, the control of the exoskeleton and FES is critical within the stance phase of walking to ensure smooth weight transfer between limbs, and achieve a sound loading response and leg propulsion. This paper develops a concurrent learning adaptive control technique to provide torque assistance about the hip and knee joints using a cable-driven exoskeleton, and activate the quadriceps and hamstrings muscle groups via FES for treadmill walking. The human-exoskeleton dynamics are modeled with phase-dependent switching dynamics to strategically update the concurrent learning controller at early stance (heel strike), late stance (toe-off), and the swing phases of walking. Thus, the adaptive switching controller compensates for the dynamic changes within the stance phase and its transition to swing, while achieving joint kinematic tracking and estimating a subset of the leg's uncertain parameters. A multiple Lyapunov function analysis is developed to demonstrate stability of the overall phase-dependent switched system requiring a dwell-time condition that guarantees exponential tracking and parameter estimation convergence.