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

• Published in: Chinese journal of chemical engineering Vol. 23; no. 12; pp. 1997 - 2004
• Main Authors: Li, Quanshan, Li, Dazi, Cao, Liulin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2015
• Subjects: ARMA models; ARMA过程; Closed-loop identification; Hybrid Box–Jenkins models; Instrumental variable; PID tuning; PID控制器; 传递函数模型; 应用; 最大似然估计; 混合箱; 系统辨识; 闭环辨识; Instrumental variable; ARMA models; Closed-loop identification; Hybrid Box–Jenkins models; PID tuning
• ISSN: 1004-9541; 2210-321X
• DOI: 10.1016/j.cjche.2015.08.026

The paper describes a closed-loop system identification procedure for hybrid continuous-time Box–Jenkins models and demonstrates how it can be used for IMC based PID controller tuning. An instrumental variable algorithm is used to identify hybrid continuous-time transfer function models of the Box–Jenkins form from discretetime 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 penalized maximum-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 IMC based flow controller and a practical application demonstrates the applicability of the algorithm.