Adaptive CPG-Based Gait Planning With Learning-Based Torque Estimation and Control for Exoskeletons

In this letter, a new adaptable gait trajectory shaping method is proposed for lower-limb exoskeletons by defining central pattern generators (CPGs). These CPGs are synchronized across different joints and updated online in response to the human users' physical behavior to enhance their safety and comfort. In this CPG structure of a high-level control scheme, an overall locomotion frequency is defined for all joint motions that can be modulated as a function of the human-robot interaction (HRI) energy. The amplitude and equilibrium position of oscillation for each joint can be adjusted in real-time based on the HRI torque. Logarithmic barrier functions are also formulated for these connected CPGs to avoid exceeding safe bounds of the joints' motion. A supervised learning algorithm is employed to identify the exoskeleton-limb dynamics and estimate the active HRI torque on different joints based on an autoregressive network with exogenous inputs (NARX) model. In order to track the reference trajectories generated by CPGs, a proportional derivative (PD) controller with torque compensation is designed. In the experimental evaluation of this intelligent control strategy, an able-bodied person wearing the Indego exoskeleton could amend and personalize the gait features considerably over a short period of time by applying active torques on different joints. In these experiments, the user increased the motion amplitude of the hip and knee joints up to 14% and made more than 200% variation in the gait frequency, which implies a considerable level of flexibility in locomotion planning for further user studies.