Adaptive Sliding Mode Control for Biped Robots with sEMG Signals

Abstract

The new human-robot interaction technology based on biological signals is an advanced method which can reflect the wearer's motion intentions. This paper presents a hybrid control method for biped lower limb exoskeleton robot with the surface electromyography (sEMG) signals. Compared with physical signals, sEMG has a better performance of identifying the intention of user's movement for the interaction with robots. In this paper, the Fisher Discriminant Analysis (FDA) of motion recognition method is applied to discriminating three-class gait movements of biped robot using sEMG which are collected from human arms. Considering the system's uncertainties and disturbances, the adaptive sliding mode control strategy is designed to approximate the nonlinear dynamic system of biped robot, which can ensure the robustness capacity and high control precision. The simulation results exhibit the affection of the proposed approach.