吉首大学学报(自然科学版) ›› 2023, Vol. 44 ›› Issue (5): 48-56.DOI: 10.13438/j.cnki.jdzk.2023.05.007

• 交通运输 • 上一篇    下一篇

一种基于μ-S模型的最佳滑移率辨识估计器设计

王波,丁芳,刘明岩,田苗法   

  1. (安徽机电职业技术学院汽车与轨道学院,安徽 芜湖 241002)
  • 出版日期:2023-09-25 发布日期:2023-11-21
  • 作者简介:王波(1980—),女,辽宁东港人,安徽机电职业技术学院汽车与轨道学院副教授,硕士,主要从事汽车电子控制技术与新能源汽车关键技术研究.
  • 基金资助:
    安徽省高校科学研究项目(KJ2020A1101,KJ2020A1116,2022AH052354);安徽省质量工程项目(2021JXTD064)

Optimal Slip Ratio Identification Estimator Design Based on μ-S Model

WANG Bo,DING Fang,LIU Mingyan,TIAN Miaofa   

  1. (College of Automotive and Rail,Anhui Technical College of Mechanical and Electrical Engineering,Wuhu 241002,Anhui China)
  • Online:2023-09-25 Published:2023-11-21

摘要:为了使基于滑移率识别的汽车防抱死控制器实现最优控制,在Kiencke μ-S模型的基础上,利用改进遗忘因子递推最小二乘算法设计了最佳滑移率辨识估计器,并将辨识估计器的最佳滑移率和峰值附着系数估算结果与Burckhardt μ-S模型下的结果进行了对比.将辨识估计器应用于汽车防抱死制动系统的模糊滑模控制器中,在单一路面和跃变路面条件下进行仿真实验.实验结果表明,辨识估计器的误差小、延迟小,基于最佳滑移率识别的防抱死控制器能实现最佳滑移率的在线辨识和快速追踪,有效提升制动效能.

关键词: 最佳滑移率, 辨识估计器, Kiencke μ-S模型, 递推最小二乘法, 遗忘因子

Abstract: In order to achieve optimal control of the automotive anti-lock controller based on slip ratio identification,an optimal slip ratio identification estimator is designed based on the Kiencke μ-S model using an improved forgetting factor recursive least squares algorithm,and the estimation results of the identification estimator regarding the optimal slip ratio and peak adhesion coefficient are compared with those under the Burckhardt μ-S model.The discriminative estimator is applied to the fuzzy sliding mode controller of the automotive anti-lock braking system,and simulation experiments are conducted under single pavement and variational pavement conditions.The experimental results show that the discriminative estimator has small errors and delays,and the anti-lock controller based on the optimal slip ratio identification can achieve online identification and fast tracking of the optimal slip ratio and effectively improve the braking efficiency.

Key words: optimal slip ratio, identification estimator, Kiencke μ-S model, recursive least square method, forgetting factor

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