GIDMP-based Gait Generation and Adaptive Switching Control for Lower Limb Exoskeleton

This article presents a novel gait generation method and a flexible adaptive switching controller for a lower limb exoskeleton. The proposed Gaussian iterative dynamic movement primitives (GIDMP) gait generation method consists of three main components: a Gaussian distribution function based on the wearer’s joint angle data; an iterative process for updating the Gaussian distribution; and the generation of a personalized motion trajectory. Compared with traditional dynamic movement primitives, the GIDMP method optimizes the dynamic generation of trajectory and reduces computational requirements. The switching control strategy dynamically adjusts torque based on the exoskeleton’s current position and velocity. Experimental results confirm the stability and accuracy of gait learning and demonstrate the controller’s excellent assistance capability. Respirometry, pressure, and surface electromyography tests from five participants indicate that the proposed system reduces the average total metabolic by 18.77%, the average pressure by 66.73%, and decreases muscle activation in the rectus femoris, semitendinosus, and soleus by 14.82%, 28.80%, and 15.67%, respectively.