Enhanced Performance Assessment of Subspace Model‐Based Predictive Controller with Parameters Tuning

Abstract

THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING 537This study focuses on performance assessment of model predictive control. An MPC-achievable benchmark for the unconstrained case is proposed based on closed-loop subspace identifi cation. Two performance measures can be constructed to evaluate the potential benefi t to update the new identifi ed model. Potential benefi t by tuning the parameter can be found from trade-off curves. Effect of constraints imposed on process variables can be evaluated by the installed controller benchmark. The MPC-achievable benchmark for the constrained case can be estimated via closed-loop simulation provided that constraints are known. Simulation of an industrial example was done using the proposed method.Cette étude porte sur l'évaluation de la performance du contrôle prédictif par modèles (MPC). On propose un banc d'essai adapté au MPC pour le cas non contraint en se basant sur l'identifi cation de sous-espaces en boucle fermée. Deux mesures de performance sont élaborées pour évaluer l'avantage potentiel de mettre à jour le nouveau modèle identifi é. L'avantage potentiel par réglage du paramètre peut s'obtenir à partir des courbes de compromis. L'effet des contraintes imposé sur les variables de procédé peut être évalué par le banc d'essai de contrôleur installé. Le banc d'essai adapté au MPC pour le cas contraint peut être estimé par la simulation en boucle fermée dans la mesure où les contraintes sont connues. On a réalisé la simulation d'un exemple industriel à l'aide de la méthode proposée.

Subspace predictive control: stability and performance enhancement

“…Third, possible performance degradation of the controller is detected. The main focus of the existing works on performance monitoring of SPC concentrates on the first and second steps [36,52], and the third step is not addressed adequately.…”

System identification and robust position control for electro-hydraulic servo system using hybrid model predictive control

“…Lee JH and Yu ZH (1994) studied tuning rules about general state-space representation MPC, and provided tuning principle to obtain nominal stability, then to detune robust performance for infinite horizon MPC. Zhang and Li (2007) focused on performance assessment of MPC, and an MPC-achievable benchmark was proposed on subspace identification. They used trade-off curves to find potential benefit by tuning weighting parameters and by a different combination of P and M in MPC-achievable benchmark, then optimal horizon numbers can be selected.…”

Model Predictive Control Tuning Methods: A Review

“…Similarly, the bounds proposed by McIntosh et al , are based on the simulated process delay and rise time. In recent years, advances in subspace identification , and automated testing have allowed practitioners to dramatically reduce the cost of tuning MPC applications . The autocovariance least-squares technology developed by Rawlings’ group , is expected to make Kalman filtering much more accessible by automatically identifying the main tuning parameters.…”