Adaptive LuGre Friction Compensation for Servo System Based on Backstepping Control and Feedforward Control

Author:

Zhu Qixin12ORCID,Wang Jiaqi1,Zhu Yonghong3

Affiliation:

1. School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China

2. Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, 215009, China

3. School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333001, China

Abstract

Aims: The study aims to improve the position control accuracy of a class of permanent magnet synchronous motors under friction. Background: Permanent magnet synchronous motor and servo system are important parts of modern industry, in which the friction effect is a typical nonlinear factor. To overcome the nonlinear friction effect, it is necessary to design a compound feedforward algorithm to improve the motion control accuracy. Objective: The objective of the study is to design a compound adaptive friction feedforward controller to overcome the nonlinear friction effect in the servo system while ensuring tracking accuracy. Methods: A compound algorithm combining velocity-acceleration double feedforward and adaptive friction feedforward is proposed to ensure the control accuracy, and then the backstepping control is used to ensure strict convergence. Finally, the friction parameter observer is used to estimate the parameters, and the performance of the control system is simulated in the Simulink module Results: Compared to Pure Friction Feedforward Compensation (PFFC) and Adaptive Friction Compensation (AFC), Adaptive Backstepping Feedforward Friction Compensation (ABFFC) has a faster convergence speed, higher steady-state accuracy, and less friction nonlinear effect Conclusion: The servo system with adaptive backstepping and feedforward friction compensation improves the accuracy and convergence of control performance. Moreover, the adaptive permanent magnet synchronous motor control system can effectively overcome the nonlinear friction effect.

Funder

National Nature Science Foundation of China

Taizhou Science and technology support plan project

Publisher

Bentham Science Publishers Ltd.

Subject

Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. RBF Neural Network-Based Output Constrained Control of Servo System with LuGre Friction;2023 6th International Conference on Robotics, Control and Automation Engineering (RCAE);2023-11-03

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