Non-linear sliding mode control of the lower extremity exoskeleton based on human–robot cooperation

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.

Optimal control research on a manipulator’s combined feedback device by the variational method genetic algorithm radial basis function method

“…While each DOF has only one angle, each DOF in the optimal trajectory needs three coefficients, which leads to a mapping problem from low-dimensional input to high-dimensional output, where the trained neural network can solve the complex computation and find a nonunique solution. The RBF network is a neural network [23][24][25] that regards the RBF as the "base" of the hidden units forming the hidden layer space so that the input vector can map directly to the hidden space without weight connection. According to Cover's theorem, the hidden layer of the RBF can map the input of low-dimensional space to high-dimensional space via a nonlinear function.…”

Design and Isokinetic Training Control Method of Leg Press Training Device

“…Define s = e as the sliding surface function, then s = e. For the linear and the nonlinear part of the system dynamics equation, the sliding mode control quantity is divided into two parts [24,25]; one is the equivalent control quantity of the first approximation system, and the other is the robust control quantity to deal with the nonlinear term [26,27].…”

Extended State Observer-Based Nonlinear Terminal Sliding Mode Control With Feedforward Compensation for Lower Extremity Exoskeleton

“…However, those mentioned control approaches can only ensure asymptotic convergence when dealing with uncertainties and disturbances. To achieve consistent high dynamic tracking control and better convergence performance, the finite-time control strategy, such as sliding mode control (SMC), has been studied for the robotic exoskeleton control [9,10]. SMC has two specific features of disturbance rejection and insensibility to uncertainties by designing the sliding mode surface [11].…”