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汽车安全与节能学报

汽车安全与节能学报 ›› 2025, Vol. 16 ›› Issue (6): 832-842.DOI: 10.3969/j.issn.1674-8484.2025.06.002

• 汽车安全 • 上一篇    下一篇

基于鲁棒模型预测的电子机械制动系统夹紧力控制

张荣誉(), 赵轩, 王姝(), 李美莹   

  1. 长安大学 汽车学院，西安 710064，中国
  • 收稿日期:2025-07-03 修回日期:2025-11-27 出版日期:2025-12-31 发布日期:2026-01-12
  • 通讯作者: * 王姝, 高级工程师。E-mail：shuwang@chd.edu.cn。
  • 作者简介:张荣誉（2001—），男（汉），河南，研究生。E-mail：3210588893@qq.com。
  • 基金资助:
    国家自然科学基金项目(52472397);陕西省重点研发计划项目(2024GX-YBXM-260);陕西省科技成果转化计划项目(2024CG-CGZH-19)

Robust model prediction based clamping force control for electro-mechanical braking systems

ZHANG Rongyu(), ZHAO Xuan, WANG Shu(), LI Meiying   

  1. School of Automobile, Chang’an University, Xi’an Shanxi 710064, China
  • Received:2025-07-03 Revised:2025-11-27 Online:2025-12-31 Published:2026-01-12

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摘要： 为了提高电子机械制动（EMB）系统夹紧力控制鲁棒性和跟踪准确性，提出基于自抗扰扩张状态观测器（ESO）的EMB鲁棒模型预测控制策略。首先，分析了EMB系统存在的电气扰动、机械扰动和环境扰动，建立了包括扰动集总项的EMB系统的数学模型；其次建立了基于鲁棒模型预测控制（RMPC）的EMB夹紧力控制策略，引入自抗扰ESO对扰动进行估计和补偿；最后通过建立硬件在环（HIL）实验平台对所提方法进行验证。结果表明：MPC控制算法在负载干扰下，EMB夹紧力出现较大的波动，最大误差为228 N，最大误差率为5.7%；而融合ESO的RMPC作用下的夹紧力最大稳态跟踪误差为38 N，最大误差率为1.52%，说明该文提出的控制策略能够有效抑制扰动影响，具有较高的夹紧力跟踪精度和较强的抗干扰能力。

关键词: 电子机械制动（EMB）系统, 夹紧力控制策略, 鲁棒模型预测控制（RMPC）, 自抗扰扩张状态观测器（ESO）, 硬件在环（HIL）实验

Abstract:

A robust model predictive control (RMPC) strategy based on an active disturbance rejection extend state observer (ESO) was proposed to improve the robustness and tracking accuracy of clamping force control in an electro-mechanical brake (EMB) system. Firstly, electrical disturbances, mechanical disturbances, and environmental disturbances inherent in the EMB system were analyzed, and a mathematical model incorporating a lumped disturbance term was established. Secondly, an EMB clamping force control strategy based on RMPC was formulated, introducing an active disturbance rejection ESO to estimate and compensate for disturbances. Finally, a hardware-in-the-loop (HIL) experimental platform was developed to validate the proposed method. The results show that the EMB clamping force controlled solely by MPC exhibits significant fluctuation under load disturbance, with a maximum error of 228 N and a maximum error rate of 5.7%; In contrast, the clamping force under the combined RMPC with ESO action shows a maximum steady-state tracking error of only 38 N, with a maximum error rate of 1.52%, indicating that the proposed control strategy effectively suppresses disturbance effects, which can achieve high clamping force tracking precision and strong anti-disturbance capability.

Key words: electro-mechanical brake (EMB) system, clamping force control strategy, robust model predictive control (RMPC), active disturbance rejection extend state observer (ESO), hardware-in-the-loop (HIL) experiment

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