Performance assessment of constrained model predictive control systems

Ko, Byung Su; Edgar, Thomas F.

doi:10.1002/aic.690470613

Cited by 57 publications

(28 citation statements)

References 19 publications

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“…So in this case, the MPC-achievable benchmark converges to the LQG benchmark as present in Favoreel et al (1999) and Kadali and Huang (2002a). Furthermore, if the objective function using the set parameter as N 2 = N u = ∞ and the weighting matrix Q = I, R = 0, the MPC-achievable benchmark converges to the minimum variance benchmark (Harris, 1989;Qin, 1998;Ko and Edgar, 2001). Equations (30) and (31) only consider the stochastic case.…”

Section: Calculate the Mpc-achievable Benchmark Based On Subspace Idementioning

confidence: 81%

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

Zhang

2007

Can J Chem Eng

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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.

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Section: Calculate the Mpc-achievable Benchmark Based On Subspace Idementioning

confidence: 81%

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

Zhang

2007

Can J Chem Eng

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“…Zhao et al [117,118] proposed the LQG benchmarking to estimate achievable variability reduction through control system improvement. Ko and Edgar proposed using dynamic DMC performance bounds [119,120] of a constrained Model Predictive Control system. They developed an index based on the constrained minimum variance controller.…”

Section: Model-based Approachesmentioning

confidence: 99%

Performance Assessment of Predictive Control—A Survey

Domański

2020

Algorithms

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Model Predictive Control constitutes an important element of any modern control system. There is growing interest in this technology. More and more advanced predictive structures have been implemented. The first applications were in chemical engineering, and now Model Predictive Control can be found in almost all kinds of applications, from the process industry to embedded control systems or for autonomous objects. Currently, each implementation of a control system requires strict financial justification. Application engineers need tools to measure and quantify the quality of the control and the potential for improvement that may be achieved by retrofitting control systems. Furthermore, a successful implementation of predictive control must conform to prior estimations not only during commissioning, but also during regular daily operations. The system must sustain the quality of control performance. The assessment of Model Predictive Control requires a suitable, often specific, methodology and comparative indicators. These demands establish the rationale of this survey. Therefore, the paper collects and summarizes control performance assessment methods specifically designed for and utilized in predictive control. These observations present the picture of the assessment technology. Further generalization leads to the formulation of a control assessment procedure to support control application engineers.

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“…This feature was detected using ACF (auto-covariance functions) and spectral peak afterwards. Oscillation monitor or performance indicator such as Harris index [2] and its extents [3] or variants [4] usually give alarms in a wide range in a plant but not only a single loop or unit. Therefore, disturbance propagation analysis and fault source location should be carried out after abnormality been detected.…”

Section: Introductionmentioning

confidence: 99%

Application of DTF method in disturbance propagation analysis of complex chemical process

Xie

et al. 2014

Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control

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Oscillations usually propagate to other loops with the delivery of mass and energy, then cause plant-wide oscillation and affect the performance of whole control system in complex chemical process. DTF (Directed Transfer Function) method, which has been widely used to analyze information flow in the brain structures in biomedical area, is applied to the disturbance propagation analysis of complex chemical process in this paper. Based on MVAR (Multivariate Autoregressive) model, DTF can analyze the multivariate causality simultaneously and calculate the causality quantitatively. Based on the DTF value, one can draw the causality graph, get the disturbance propagation path and finally locate fault sources. The results of simulation on TEP (Tennessee Eastman Process) are presented to illustrate the effectiveness of the proposed approach.

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Performance assessment of constrained model predictive control systems

Cited by 57 publications

References 19 publications

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

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

Performance Assessment of Predictive Control—A Survey

Application of DTF method in disturbance propagation analysis of complex chemical process

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