Bio-inspired Control of Joint Torque and Knee Stiffness in a Robotic Lower Limb Exoskeleton Using a Central Pattern Generator

Abstract

Robotic lower limb exoskeletons are becoming increasingly popular in therapy and recreational use. However, most exoskeletons are still rather limited in their locomotion speed and the activities of daily live they can perform. Furthermore, they typically do not allow for a dynamic adaptation to the environment, as they are often controlled with predefined reference trajectories. Inspired by human leg stiffness modulation during walking, variable stiffness actuators increase flexibility without the need for more complex controllers. Actuation with adaptable stiffness is inspired by the human leg stiffness modulation during walking. However, this actuation principle also introduces the stiffness setpoint as an additional degree of freedom that needs to be coordinated with the joint trajectories. As a potential solution to this issue a bio-inspired controller based on a central pattern generator (CPG) is presented in this work. It generates coordinated joint torques and knee stiffness modulations to produce flexible and dynamic gait patterns for an exoskeleton with variable knee stiffness actuation. The CPG controller is evaluated and optimized in simulation using a model of the exoskeleton. The CPG controller produced stable and smooth gait for walking speeds from 0.4 m/s up to 1.57 m/s with a torso stabilizing force that simulated the use of crutches, which are commonly needed by exoskeleton users. Through the CPG, the knee stiffness intrinsically adapted to the frequency and phase of the gait, when the speed was changed. Additionally, it adjusted to changes in the environment in the form of uneven terrain by reacting to ground contact forces. This could allow future exoskeletons to be more adaptive to various environments, thus making ambulation more robust.