Performance assessment of multivariable feedback controllers

被引:372
作者
Harris, TJ
Boudreau, F
MacGregor, JF
机构
[1] MCMASTER UNIV,DEPT CHEM ENGN,HAMILTON,ON L8S 4L7,CANADA
[2] QUEENS UNIV,DEPT CHEM ENGN,KINGSTON,ON K7L 3N6,CANADA
关键词
performance monitoring; multivariable control; interactor matrices; minimum-variance control;
D O I
10.1016/S0005-1098(96)00108-2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 [计算机科学与技术];
摘要
Procedures are developed for assessing the performance of multi-input multi-output (MIMO) linear feedback control system. The predicted performance of the MIMO minimum variance controller is used as a lower bound against which current performance is assessed. To use the procedures, one needs only a set of representative data on the controlled variables collected under the current feedback control scheme, and an estimate of the process interactor matrix characterizing the dead-time structure of the process. A non-parametric correlation test allows for a quick check on the closeness of the performance of the current feedback control system to the theoretical lower bound. A quantitative analysis then provides an estimate of the theoretical lower bound on the variance-covariance matrix of the controlled variables, and on the quadratic performance objective of the minimum-variance controller. A measure of controller performance based on the ratio of the current value of the quadratic objective to this thoeretical lower limit can then be monitored on-line. The purpose of these methods is to allow engineers to assess the current performance of the existing control systems, to monitor how this performance changes over time, to see which variables are well controlled and which are not, and to help in deciding whether or not there is sufficient incentive to reidentify the process model and/or redesign the controller. Copyright (C) 1996 Elsevier Science Ltd.
引用
收藏
页码:1505 / 1518
页数:14
相关论文
共 27 条
[1]
Astrom K.J.., 1970, INTRO STOCHASTIC CON
[2]
Astrom K.J., 2011, Computer-Controlled Systems: Theory and Design, VThird
[3]
BOX GEP, 1970, TIME SERIES ANAL FOR
[4]
ON POLYNOMIAL MATRIX SPECTRAL FACTORIZATION BY SYMMETRIC EXTRACTION [J].
CALLIER, FM .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1985, 30 (05) :453-464
[5]
PERFORMANCE ASSESSMENT MEASURES FOR UNIVARIATE FEEDFORWARD FEEDBACK-CONTROL [J].
DESBOROUGH, L ;
HARRIS, T .
CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 1993, 71 (04) :605-616
[6]
PERFORMANCE ASSESSMENT MEASURES FOR UNIVARIATE FEEDBACK-CONTROL [J].
DESBOROUGH, L ;
HARRIS, T .
CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 1992, 70 (06) :1186-1197
[7]
THE ROLE OF THE INTERACTOR MATRIX IN MULTIVARIABLE STOCHASTIC ADAPTIVE-CONTROL [J].
DUGARD, L ;
GOODWIN, GC ;
XIE, XY .
AUTOMATICA, 1984, 20 (05) :701-709
[8]
Goodwin G C., 1984, ADAPTIVE FILTERING P
[9]
AN ITERATIVE METHOD FOR MATRIX SPECTRAL FACTORIZATION [J].
HARRIS, TJ ;
DAVIS, JH .
SIAM JOURNAL ON SCIENTIFIC AND STATISTICAL COMPUTING, 1992, 13 (02) :531-540
[10]
DESIGN OF MULTIVARIABLE LINEAR-QUADRATIC CONTROLLERS USING TRANSFER-FUNCTIONS [J].
HARRIS, TJ ;
MACGREGOR, JF .
AICHE JOURNAL, 1987, 33 (09) :1481-1495