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Task-invariant control methods for powered exoskeletons provide flexibility in assisting humans across multiple activities and environments. Energy shaping control serves this purpose by altering the human body’s dynamic characteristics in closed loop. Our previous work on potential energy shaping alters the gravitational vector to reduce the user’s perceived gravity, but this method cannot provide velocity-dependent assistance. The interconnection and damping assignment passivity-based control (IDA-PBC) method provides more freedom to shape a dynamical system’s energy through the interconnection structure of a port-controlled Hamiltonian system model. This paper derives a novel energetic control strategy based on IDA-PBC for a backdrivable knee-ankle exoskeleton. The control law provides torques that depend on various basis functions related to gravitational and gyroscopic terms. We optimize a set of constant weighting parameters for these basis functions to obtain a control law that produces able-bodied joint torques during walking on multiple ground slopes. We perform experiments with an able-bodied human subject wearing a knee-ankle exoskeleton to demonstrate reduced activation in certain lower-limb muscles. 

This paper presents the design and implementation of a novel multi-activity control strategy for a backdrivable knee-ankle exoskeleton. Traditionally, exoskeletons have used trajectory-based control of highly geared actuators for complete motion assistance. In contrast, we develop a potential energy shaping controller with ground reaction force (GRF) feedback that facilitates multi-activity assistance from a backdrivable exoskeleton without prescribing pre-defined kinematics. Although potential energy shaping was previously implemented in an exoskeleton to reduce the user’s perceived gravity, this model-based approach assumes the stance leg is fully loaded with the weight of the user, resulting in excessive control torques as weight transfers to the contralateral leg during double support. The presented approach uses GRF feedback to taper the torque control output for any activity involving multiple supports, leading to a closer match with normative joint moments in simulations based on pre-recorded human data during level walking. To implement this strategy, we present a custom foot force sensor that provides GRF feedback to the previously designed exoskeleton. Finally, results from an able-bodied human subject experiment demonstrate that the exoskeleton is able to reduce muscular activation of the primary muscles related to the knee and ankle joints during sit-to-stand, stand-to-sit, level walking, and stair climbing.