Abstract
Multivariate controller performance assessment (MVPA) has been developed over the last several years, but its application in advanced model predictive control (MPC) has been very limited mainly due to issues associated with comparability of variance control objective and that of MPC. MPC has been proven as one of the most effective advanced process control (APC) strategies to deal with multivariable constrained control problems with an ultimate objective towards economic optimization. Any attempt to evaluate MPC performance should therefore consider constraints and economic performance. This work is to establish a link between variance control and MPC in terms of economic performance. We show that the variance based performance assessment may be transfered to economic assessment of MPC. Algorithms for economic performance assessment and tuning are developed through linear matrix inequalities using routine operating process data. The proposed algorithms are illustrated via an industrial MPC application example.
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