河北大学学报(自然科学版) ›› 2026, Vol. 46 ›› Issue (3): 264-270.DOI: 10.3969/j.issn.1000-1565.2026.03.004

• • 上一篇    

基于修正Hurst指数的控制系统评价及PID参数优化方法

李士哲1,张天宇1,刘畅1,刘帅2   

  • 收稿日期:2024-07-26 发布日期:2026-05-15
  • 通讯作者: 张天宇(2001—)
  • 作者简介:李士哲(1981—),男,华北电力大学副教授,主要从事控制系统的性能评价及诊断、大数据可视化等方向研究.
    E-mail:lishizhe121@163.com
  • 基金资助:
    国家自然科学基金项目(61973117);华北电力大学中央高校基本科研业务项目(9160323007);河北省科技计划项目(22567643H);深圳市可持续发展科技专项项目(KCXFZ20201221173402007)

Control system evaluation and PID parameter optimization method based on modified Hurst index

LI Shizhe1, ZHANG Tianyu1, LIU Chang1, LIU Shuai2   

  1. 1. Department of Automation, North China Electric Power University, Baoding 071003, China; 2. Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation, Baoding 071003, China
  • Received:2024-07-26 Published:2026-05-15

摘要: 为了提升工业过程中PID控制系统的性能,提出一种基于修正Hurst指数的PID控制器参数优化方法.首先,根据Hurst指数与最小方差指标修正了Hurst指标的计算方式,划分了性能评价等级;其次,针对评价结果未达标的控制系统,分别采用基于模型的遗传算法和基于数据驱动的虚拟参考反馈整定方法进行参数优化整定;最后,通过MATLAB/Simulink仿真验证.仿真结果表明,修正的Hurst指标能够准确评估控制系统的性能,遗传算法和虚拟参考反馈整定方法均能改善PID参数,提高系统控制品质.

关键词: Hurst指数, 性能评价, 遗传算法, 虚拟参考反馈整定

Abstract: To improve the performance of PID control systems in industrial processes, a method for optimizing PID controller parameters based on a modified Hurst exponent is proposed. First, the calculation method of the Hurst exponent is modified according to the Hurst index and the minimum variance index, and performance evaluation levels are assigned. Then, for control systems that fail to meet the evaluation criteria, model-based genetic algorithms and data-driven virtual reference feedback tuning methods are used to optimize the PID controller parameters. Finally, simulations in MATLAB/Simulink- 引用格式:张文恺,杨术明,马永龙,等.基于EDEM的牧场推料机器人参数优化设计与试验[J].河北大学学报(自然科学版),2026,46(3):225-236.引用格式:李士哲,张天宇,刘畅,等.基于修正Hurst指数的控制系统评价及PID参数优化方法[J].河北大学学报(自然科学版),2026,46(3):264-270.DOI:10.3969/j.issn.1000-1565.2026.03.004基于修正Hurst指数的控制系统评价及PID参数优化方法李士哲1,张天宇1,刘畅1,刘帅2(1.华北电力大学 自动化系,河北 保定 071003;2.河北省发电过程仿真与优化控制技术创新中心,河北 保定 071003)摘 要:为了提升工业过程中PID控制系统的性能,提出一种基于修正Hurst指数的PID控制器参数优化方法.首先,根据Hurst指数与最小方差指标修正了Hurst指标的计算方式,划分了性能评价等级;其次,针对评价结果未达标的控制系统,分别采用基于模型的遗传算法和基于数据驱动的虚拟参考反馈整定方法进行参数优化整定;最后,通过MATLAB/Simulink仿真验证.仿真结果表明,修正的Hurst指标能够准确评估控制系统的性能,遗传算法和虚拟参考反馈整定方法均能改善PID参数,提高系统控制品质.关键词:Hurst指数;性能评价;遗传算法;虚拟参考反馈整定中图分类号:TP273 文献标志码:A 文章编号:1000-1565(2026)03-0264-07DOI:10.3969/j.issn.1000-1565.2026.03.004Control system evaluation and PID parameter optimization method based on modified Hurst indexLI Shizhe1, ZHANG Tianyu1, LIU Chang1, LIU Shuai2(1. Department of Automation, North China Electric Power University, Baoding 071003, China; 2. Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation, Baoding 071003, China)Abstract: To improve the performance of PID control systems in industrial processes, a method for optimizing PID controller parameters based on a modified Hurst exponent is proposed. First, the calculation method of the Hurst exponent is modified according to the Hurst index and the minimum variance index, and performance evaluation levels are assigned. Then, for control systems that fail to meet the evaluation criteria, model-based genetic algorithms and data-driven virtual reference feedback tuning methods are used to optimize the PID controller parameters. Finally, simulations in MATLAB/Simulink- 收稿日期:2024-07-26;修回日期:2025-11-04 基金项目:国家自然科学基金项目(61973117);华北电力大学中央高校基本科研业务项目(9160323007);河北省科技计划项目(22567643H);深圳市可持续发展科技专项项目(KCXFZ20201221173402007) 第一作者:李士哲(1981—),男,华北电力大学副教授,主要从事控制系统的性能评价及诊断、大数据可视化等方向研究.E-mail:lishizhe121@163.com 通信作者:张天宇(2001—),男,华北电力大学在读硕士研究生,主要从事控制系统性能评价及优化方向研究.E-mail:1842129705@qq.com 第3期李士哲等:基于修正Hurst指数的控制系统评价及PID参数优化方法河北大学学报(自然科学版) 第46卷validate the results. Simulation results show that the modified Hurst exponent can accurately evaluate the performance of the control system, and both the genetic algorithm and the virtual reference feedback tuning method can improve the PID parameters and enhance the systems control quality.

Key words: Hurst index, performance evaluation, genetic algorithm, virtual reference feedback tuning

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