Gait Planning and Multimodal Human-Exoskeleton Cooperative Control Based on Central Pattern Generator
Jiange Kou, Yixuan Wang, Zhenlei Chen, Yan Shi, Qing Guo
Abstract
This study presents a multimodal human-exoskeleton cooperative control method to realize different control modes smoothly switching each other with satisfactory stable performance. Considering existed mismatch gaits of the operator comparison with the exoskeleton, the corresponding operator's gait is planned by central pattern generators (CPGs) to reduce human-exoskeleton impedance and generate real-time desired trajectory, which are used as the trajectory demand input of the exoskeleton control. Then, the admittance modulation factors is proposed to realize three motion control modes of lower limb exoskeleton, i.e., active, passive, and assist-as-needed. Meanwhile, an adaptive backstepping controller with the radial basis function neyral network estimation law is designed to guarantee the position tracking errors in uniformly ultimately boundedness under model uncertainty. Finally, the experimental studies are performed with an able-bodied operator by regulating the CPGs model parameters and modulation factors to verify the proposed multimodal human-exoskeleton cooperative control.