Model Identification of 2-DOF Lower Limb Exoskeleton with Neighborhood Field Optimization Algorithm

Abstract

For the lower limb exoskeleton, the system control performance and stability of human-robot coordinated movement is often degraded by some model parametric uncertainties. To address this problem, the model parameter identification method based on Neighborhood Field Optimization (NFO) algorithm is proposed to obtain the accurate model parameters of 2-DOF exoskeleton, which guides the model-based controller design. For the 2-DOF lower limb exoskeleton experimental platform, the model is constructed by Lagrange equation. Meanwhile, the excitation trajectory with the setting mechanical constraints is designed by NFO to guarantee the identification accuracy. Meanwhile, the Huber fitness function is adopted to suppress the influence of the disturbance points in sampling dataset with respect to the identification accuracy. Finally, the NFO algorithm with the Huber fitness function is verified by 2-DOF lower limb exoskeleton experimental platform.