Adaptive block compensation trajectory tracking control based on LuGre friction model

Zhimin Wu

doi:10.1177/1729881419873212

Abstract

Aiming at the problems of modeling error and uncertain external disturbance in the multi-joint robot control model, an adaptive block compensation trajectory tracking controller based on LuGre friction model is proposed. Firstly, the algorithm divides the interference term of LuGre friction model into three parts with different physical quantities. Secondly, an adaptive neural network compensator is designed to assess the three parts of the LuGre friction model. Thirdly, a robust sliding mode controller is developed to reduce the influence of these estimation errors of neural network compensator and other uncertain disturbances and ensure that the system converges in a finite time at the same time. Finally, numerical simulations under different input and disturbance signals for the planar multi-joint robot and the inverted pendulum are conducted to validate the effectiveness of the proposed controller, and the performance of the proposed controller is compared with conventional sliding mode controller to illustrate the usefulness and efficiency of the proposed controller.

Highlights

The uncertainty of parameters and complex friction types of multi-joint robot are the main factors that make it difficult to establish the complete and accurate dynamic model of multi-joint robot
In view of the abovementioned problems, this article proposes an adaptive block compensation trajectory tracking controller based on radial basis function neural networks (RBFNNs), where the local approximation characteristics of the neural network are used to compensate for the three friction subfunctions with different physical quantities
An adaptive block compensation trajectory tracking controller based on LuGre friction model is proposed

Summary

Introduction

The uncertainty of parameters and complex friction types of multi-joint robot are the main factors that make it difficult to establish the complete and accurate dynamic model of multi-joint robot. Nguyen et al.[13] proposed a neural network-based adaptive sliding mode control method for tracking of a nonholonomic wheeled mobile robot subject to unknown wheel slips, model uncertainties, and unknown bounded disturbances, where self-recurrent wavelet neural networks are employed to approximate unknown nonlinear functions. Pavol and Yuri[15] established the multi-fingered manipulator model and conducted target recognition, check and attitude tracking based on the deep learning model of convolutional neural network These aforementioned neural network sliding mode controllers have just provided an overall compensation control for structured and unstructured uncertainties. In view of the abovementioned problems, this article proposes an adaptive block compensation trajectory tracking controller based on RBFNN, where the local approximation characteristics of the neural network are used to compensate for the three friction subfunctions with different physical quantities. According to the threepart variable function, the input variables of the neural network are set to zðq_ Þ, nðq_ Þzðq_ Þ, and q_ ðkÞ

Design of controller

Design of sliding mode robust controller

Design of RBFNN controller

Conclusion