Administrator by China Associction for Science and Technology
Sponsored by China Society of Automotive Engineers
Published by AUTO FAN Magazine Co. Ltd.

Automotive Engineering ›› 2024, Vol. 46 ›› Issue (3): 526-535.doi: 10.19562/j.chinasae.qcgc.2024.03.017

Previous Articles    

Fuzzy PID Control Based on Genetic Algorithm Optimization of a Dual-coil Magnetorheological Brake

Jie Wu1,3(),Hui Zhang2   

  1. 1.Hubei Agricultural Machinery Institute,Hubei University of Technology,Wuhan 430068
    2.School of Mechanical Engineering,Xihua University,Chengdu 610039
    3.Institute of Modern Agricultural Equipment,Xihua University,Chengdu 610039
  • Received:2023-06-29 Revised:2023-10-06 Online:2024-03-25 Published:2024-03-18
  • Contact: Jie Wu E-mail:jiewu323@163.com

Abstract:

For the unstable braking torque output of magnetorheological (MR) brakes, the genetic algorithm (GA) optimized Fuzzy PID control method is used to control the dual-coil MR brake in this article. Based on the Bingham model, a mathematical model for the braking torque of the dual-coil MR brake is established and a dynamic model of the brake is also derived. The torque experiment of the MR brake has been completed. When the coil current is 1.0 A, the maximum braking torque of the MR brake is 4.8 N·m. The least squares structural model is used to identify the transfer function parameters of the dual-coil MR brake. Based on genetic algorithm and Fuzzy PID control, a Fuzzy PID controller optimized by genetic algorithm for the brake is designed. An experimental platform is established to carry out experimental research on the control of the MR brake. The research results indicate that the dual-coil MR brake can achieve better control effect under GA optimized Fuzzy PID control compared to traditional Fuzzy PID control. The rising time of the step response of the braking torque is 0.63 seconds, with an overshoot of 4.17%, and a tracking error of the braking torque smaller than 0.2 N·m. It has a faster response speed, a smaller overshoot, and a smaller torque tracking error.

Key words: magnetorheological (MR) brake, genetic algorithm (GA), Fuzzy PID, braking torque, tracking error