Adaptive motion intent understanding–based control of human–exoskeleton system

Abstract

Although human–exoskeleton systems have tremendous potentials for rehabilitation training, transparent human–exoskeleton interaction has not been achieved for late-phase rehabilitation. To cope with this challenge, an adaptive pilot intent prediction–based control algorithm is proposed to achieve compliant motion coordination. A novel surface electromyography signal processing method providing definite physical meaning and high computational efficiency is derived. The state space model of human musculoskeletal system is developed with the input of the processed surface electromyography. Adaptive parameter estimation is employed to cope with the emerging dynamics. It integrates the advantages of surface electromyography signals which imply muscle contraction in advance and force signals with high stability. Based on the coupled human–exoskeleton system dynamics, transparent control of a human–exoskeleton system is realized. Experimental results show that the computational efficiency of the proposed surface electromyography processing method is 129% higher than the energy kernel method, and the maximum interactive force is reduced about 15 N compared with impedance control.