Impact of model plant mismatch on performance of control systems: An application to paper machine control

Yousefi, Mahdi; Gopaluni, R. Bhushan; Loewen, Philip D.; Forbes, Michael G.; Dumont, Guy A.; Backström, Johan U.

doi:10.1016/j.conengprac.2015.07.005

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…R(u1,0) u1 regression 0 times R(u1,1) u1 regression 1 times R(u1,2) u1 regression 2 times R(u2,0) u2 regression 0 times R(u2,1) u2 regression 1 times R(u2,2) u2 regression 2 times AR(d1, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] d1 autoregression 1-20 times AR(d2, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] d2 autoregression 1-20 times…”

Section: Mathematical Symbols Explanationsunclassified

“…Jie Yu and S.Joe Qin invented left-right diagonal interactor rather than directly using Nilpotent interactor matrix as another auxiliary calculation method [12] for performance computation. However, the emergence and existence of Model Plant Mismatch (MPM) had had an impact on control performance evaluations [13] and even weakened the reliability of evaluating benchmark. In addition, time delay mismatch would affect the Minimum Variance Benchmark (MVB) and lead to the changes in both the system output variances and the Minimum Variance index [14].…”

Section: Introductionmentioning

confidence: 99%

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Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method

Ming

Xie

2021

Processes

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In closed-loop control systems, the model accuracy exerts large influences on the controllability, stability and quality of the whole process. Among all the faults that could affect the system performance, Model Plant Mismatch (MPM) is the one that not only directly threatens the system stability but also deteriorates the controller performance. Meanwhile, MPM has a major influence on the qualities of outputs about industrial products. In this work, a new detection method based on Granger Causality is proposed to detect and locate the MPM in multiple input multiple output systems. Causality can reflect the relations between the mismatch fault and its negative effects on model predictive control(MPC) systems. With the assistance of disturbance transfer function models, the causality method can further be used to locate the mismatch positions and get the correct channels of each kind of mismatches. The proposed method was examined and validated in the Wood-Berry process in contrast to the decussation location method under model predictive controller.

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Section: Mathematical Symbols Explanationsunclassified

Section: Introductionmentioning

confidence: 99%

Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method

Ming

Xie

2021

Processes

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“…Linear models are applied as approximations of typical nonlinear models. Nonlinear behavior is dominant during the start‐up and setpoint changes , . Disturbances of high magnitude also move the nonlinear system away from its linear approximation.…”

Section: Introductionmentioning

confidence: 99%

System Behavior and Predictive Controller Performance Near the Azeotropic Region

et al. 2018

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High‐purity distillation columns present challenges of nonlinearity, interactions, directionality, and ill‐conditioning for the control systems and thus exhibit poor controller performance. Effects of these issues on the performance of model predictive control in operations in near‐azeotropic regions are investigated. A dynamic model of a pilot‐scale distillation column is simulated and controlled by a linear model predictive controller. As the system moves towards the azeotropic region, the nonlinearity, directionality, and interactions of the system increase. A significant decline is observed in the system control performance since the controller incurs overshoots and offsets for negative and positive setpoint changes. The integral time absolute errors are substantially higher near the azeotropic region. Only a small operating range is able to ensure sufficient controllability near the azeotropic region.

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Flooding and offset-free nonlinear model predictive control of a high-purity industrial ethylene splitter using a hybrid model

Jalanko

Sanchez

Mhaskar

et al. 2021

Computers & Chemical Engineering

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Impact of model plant mismatch on performance of control systems: An application to paper machine control

Cited by 9 publications

References 15 publications

Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method

Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method

System Behavior and Predictive Controller Performance Near the Azeotropic Region

Flooding and offset-free nonlinear model predictive control of a high-purity industrial ethylene splitter using a hybrid model

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