A Modular Framework for Task-Agnostic, Energy Shaping Control of Lower Limb Exoskeletons

Abstract

Various backdrivable lower limb exoskeletons have demonstrated the electromechanical capability to assist volitional motions of able-bodied users and people with mild to moderate gait disorders, but there does not exist a control framework that can be deployed on any joint(s) to assist any activity of daily life in a provably stable manner. This article presents the modular, multitask optimal energy shaping (M-TOES) framework, which uses a convex, data-driven optimization to train an analytical control model to instantaneously determine assistive joint torques across activities for any lower limb exoskeleton joint configuration. The presented modular energy basis is sufficiently descriptive to fit normative human joint torques (given normative feedback from signals available to a given joint configuration) across sit-stand transitions, stair ascent/descent, ramp ascent/descent, and level walking at different speeds. We evaluated controllers for four joint configurations (unilateral/bilateral and hip/knee) of the modular backdrivable lower limb unloading exoskeleton (M-BLUE) exoskeleton on eight able-bodied users navigating a multiactivity circuit. The two unilateral conditions significantly lowered overall muscle activation across all tasks and subjects (p $\mathbf {\lt }$ 0.001). In contrast, bilateral configurations had a minimal impact, possibly attributable to device weight and physical constraints.