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

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 paper presents the modular, multi-task 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, hip/knee) of the modular backdrivable lower limb unloading exoskeleton (M-BLUE) exoskeleton on eight able-bodied users navigating a multi-activity circuit. The two unilateral conditions significantly lowered overall muscle activation across all tasks and subjects (p < 0.001). In contrast, bilateral configurations had a minimal impact, possibly attributable to device weight and physical constraints.