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.
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).
