Performance Monitoring and Diagnosis of Multivariable Model Predictive Control Using Statistical Analysis  

作　　者：张强 李少远 

机构地区：[1]Institute of Automation, Shanghai Jiao Tong University, Shanghai 200240, China

出　　处：《Chinese Journal of Chemical Engineering》2006年第2期207-215,共9页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China (Nos.60474051, 60534020), the Key Technology and Devel-opment Program of Shanghai Science and Technology Department (No.04DZ11008), and the Program for New Century Ex-cellent Talents in the University of China (NCET).

摘　　要：A statistic-based benchmark was proposed for performance assessment and monitoring of model predic- tive control; the benchmark was straightforward and achievable by recording a set of output data only when the control performance was good according to the user’s selection. Principal component model was built and an auto- regressive moving average filter was identified to monitor the performance; an improved T2 statistic was selected as the performance monitor index. When performance changes were detected, diagnosis was done by model validation using recursive analysis and generalized likelihood ratio (GLR) method. This distinguished the fact that the per- formance change was due to plant model mismatch or due to disturbance term. Simulation was done about a heavy oil fractionator system and good results were obtained. The diagnosis result was helpful for the operator to improve the system performance.A statistic-based benchmark was proposed for performance assessment and monitoring of model predictive control; the benchmark was straightforward and achievable by recording a set of output data only when the control performance was good according to the user＇s selection. Principal component model was built and an autoregressive moving average filter was identified to monitor the performance; an improved T^2 statistic was selected as the performance monitor index. When performance changes were detected, diagnosis was done by model validation using recursive analysis and generalized likelihood ratio （GLR） method. This distinguished the fact that the performance change was due to plant model mismatch or due to disturbance term. Simulation was done about a heavy oil fractionator system and good results were obtained. Thediagnosis result was helpful for the operator to improve the system performance.

关 键 词：predictive control performance monitoring DIAGNOSIS principal component analysis 

分 类 号：TQ01[化学工程]