Non-linear sliding mode control of the lower extremity exoskeleton based on human–robot cooperation. (11th October 2016)

• Record Type: Journal Article
• Authors: Zhu, Shiqiang; Jin, Xinglai; Yao, Bin; Chen, Qingcheng; Pei, Xiang; Pan, Zhongqiang
• Is Part Of: International journal of advanced robotic systems. Volume 13:Number 5(2016)
• Publication Date: 2016-10-11
• Subjects: Human–robot cooperation -- lower extremity exoskeleton -- inverse Jacobian matrix -- non-linear sliding mode control -- RBF neural network -- saturation function; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881416662788
• Languages: English
• ISSNs: 1729-8806

Abstract

This article presents a human–robot cooperation controller towards the lower extremity exoskeleton which aims to improve the tracking performance of the exoskeleton and reduce the human–robot interaction force. Radial basis function neural network is introduced to model the human–machine interaction which can better approximate the non-linear relationship than the general impedance model. A new method to calculate the inverse Jacobian matrix is presented. Compared to traditional damped least squares method, the novel method is proved to be able to avoid the orientation change of the velocity of the human–robot interaction point by the simulation result. This feature is very important in human–robot system. Then, an improved non-linear robust sliding mode controller is designed to promote the tracking performance considering system uncertainties and model errors, where a new non-linear integral sliding surface is given. The stability analysis of the proposed controller is performed using Lyapunov stability theory. Finally, the novel methods are applied to the swing leg control of the lower extremity exoskeleton, its effectiveness is validated by simulation and comparative experiments.