Title :
A performance measure for constrained model predictive controllers
Author :
Yongchun Zhang ; Henson, Michael A.
Author_Institution :
Dept. of Chem. Eng., Louisiana State Univ., Baton Rouge, LA, USA
fDate :
Aug. 31 1999-Sept. 3 1999
Abstract :
Model predictive control (MPC) is the dominant control technology for constrained, multivariable chemical processes. Performance assessment of MPC controllers is an important problem that has received little attention. Most multivariable controller assessment techniques are based on comparing the actual performance to that achievable under minimum variance control (MVC). MVC measures not well suited for MPC controllers because inherent control limitations imposed by constraints are neglected and minimum variance performance may be unachievable by any MPC controller even in the absence of constraints. We propose a performance measure for MPC controllers that utilizes the available process model to determine control system performance under constraints. The measure is computed as the ratio of the expected performance to the actual performance over a moving horizon of past information. The MPC performance measure is compared to a MVC measure using a linear distillation column model.
Keywords :
multivariable control systems; predictive control; MPC controllers; MVC measures; constrained model predictive controllers; constrained multivariable chemical processes; linear distillation column model; minimum variance control; multivariable controller assessment techniques; performance measure; Benchmark testing; Computational modeling; Degradation; Distillation equipment; Monitoring; Noise; Process control; constraints; controller performance monitoring; distillation columns; model predictive control;
Conference_Titel :
Control Conference (ECC), 1999 European
Conference_Location :
Karlsruhe
Print_ISBN :
978-3-9524173-5-5