SCI和EI收录∣中国化工学会会刊

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (12): 1997-2004.DOI: 10.1016/j.cjche.2015.08.026

• 第25届中国过程控制会议专栏 • 上一篇    下一篇

Closed-loop identification of systems using hybrid Box-Jenkins structure and its application to PID tuning

Quanshan Li1,2, Dazi Li1, Liulin Cao1   

  1. 1 Institute of Automation, Beijing University of Chemical Technology, Beijing 100029, China;
    2 Beijing Century Robust Technology Co. Ltd., Beijing 100020, China
  • 收稿日期:2015-05-25 修回日期:2015-06-25 出版日期:2015-12-28 发布日期:2016-01-19
  • 通讯作者: Dazi Li
  • 基金资助:

    Supported by the National Natural Science Foundation of China (61573052, 61174128).

Closed-loop identification of systems using hybrid Box-Jenkins structure and its application to PID tuning

Quanshan Li1,2, Dazi Li1, Liulin Cao1   

  1. 1 Institute of Automation, Beijing University of Chemical Technology, Beijing 100029, China;
    2 Beijing Century Robust Technology Co. Ltd., Beijing 100020, China
  • Received:2015-05-25 Revised:2015-06-25 Online:2015-12-28 Published:2016-01-19
  • Contact: Dazi Li
  • Supported by:

    Supported by the National Natural Science Foundation of China (61573052, 61174128).

摘要: The paper describes a closed-loop system identification procedure for hybrid continuous-time Box-Jenkins models and demonstrates howit can be used for IMC based PID controller tuning. An instrumental variable algorithmis used to identify hybrid continuous-time transfer function models of the Box-Jenkins formfromdiscretetime prefiltered data, where the process model is a continuous-time transfer function, while the noise is represented as a discrete-time ARMA process. A novel penalizedmaximum-likelihood approach is used for estimating the discrete-time ARMA process and a circulatory noise elimination identification method is employed to estimate process model. The input-output data of a process are affected by additive circulatory noise in a closedloop. The noise-free input-output data of the process are obtained using the proposed method by removing these circulatory noise components. The process model can be achieved by using instrumental variable estimation method with prefiltered noise-free input-output data. The performance of the proposed hybrid parameter estimation scheme is evaluated by the Monte Carlo simulation analysis. Simulation results illustrate the efficacy of the proposed procedure. The methodology has been successfully applied in tuning of IMCbased flowcontroller and a practical application demonstrates the applicability of the algorithm.

关键词: Hybrid Box-Jenkins models, ARMA models, Instrumental variable, Closed-loop identification, PID tuning

Abstract: The paper describes a closed-loop system identification procedure for hybrid continuous-time Box-Jenkins models and demonstrates howit can be used for IMC based PID controller tuning. An instrumental variable algorithmis used to identify hybrid continuous-time transfer function models of the Box-Jenkins formfromdiscretetime prefiltered data, where the process model is a continuous-time transfer function, while the noise is represented as a discrete-time ARMA process. A novel penalizedmaximum-likelihood approach is used for estimating the discrete-time ARMA process and a circulatory noise elimination identification method is employed to estimate process model. The input-output data of a process are affected by additive circulatory noise in a closedloop. The noise-free input-output data of the process are obtained using the proposed method by removing these circulatory noise components. The process model can be achieved by using instrumental variable estimation method with prefiltered noise-free input-output data. The performance of the proposed hybrid parameter estimation scheme is evaluated by the Monte Carlo simulation analysis. Simulation results illustrate the efficacy of the proposed procedure. The methodology has been successfully applied in tuning of IMCbased flowcontroller and a practical application demonstrates the applicability of the algorithm.

Key words: Hybrid Box-Jenkins models, ARMA models, Instrumental variable, Closed-loop identification, PID tuning