Multiple Model Predictive Control Strategy for Disturbance Rejection

Kuure-Kinsey, Matthew; Bequette, B. Wayne

doi:10.1021/ie100093c

Cited by 31 publications

(12 citation statements)

References 16 publications

(22 reference statements)

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“…This allows the RPSA device to not only produce a wide range of O 2 purities, but also a range of product flow rates. Approaches to switching models based on the disturbance have been reported in References 14,32. While our model switching does not explicitly depend on the disturbance, it does share similarities to these previous formulations in being able to switch the model in real‐time in presence of system transitions.…”

Section: Controller Evaluationmentioning

confidence: 91%

Piecewise linear model predictive control of a rapid pressure swing adsorption system

2020

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Rapid pressure swing adsorption (RPSA) is a gas separation technology used in the small-scale oxygen concentrator devices. These devices are commonly used to produce high purity (~90%) oxygen from air for oxygen rehabilitation therapy, but can also produce a much wider range of oxygen purities for other applications. RPSA is a complex, cyclic, nonlinear switched logic process resulting from the coupling of gas adsorption, heat transfer, flow reversal effects, and process logic switches. For RPSA devices to operate satisfactorily, feedback control is critical but challenging due to their inherent complexity. In this article, we present a piecewise linear model predictive control framework for operation and control of a single-bed RPSA system. A set of coupled, nonlinear partial differential equations with flow switching conditions is used as a plant model for the RPSA process. Subspace system identification with pseudo-random binary sequence signals applied to this plant model at multiple operating points is used to generate a family of piecewise linear models for use in the model predictive controller algorithm. Detailed descriptions of the RPSA plant model, the multiple linear model identification procedure, the controller formulation and model switching logic are presented. The closed-loop system is evaluated in simulation using several realistic set point tracking and disturbance rejection cases.

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Section: Controller Evaluationmentioning

confidence: 91%

Piecewise linear model predictive control of a rapid pressure swing adsorption system

2020

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“…This prevents models from becoming inactive, since the recursive nature of the probability calculation means that if a model probability decreases to zero, then it cannot be nonzero in future time steps. 17 If innovation covariance information S i, k is not available due to the choice of estimator (e.g., MHE), an alternate option for calculating model probabilities is to use the following equation for model likelihood: 17…”

Section: Augmented Model Creationmentioning

confidence: 99%

“…It is possible to use augmented models with MM methods to obtain updated parameter estimates, which provide diagnostic information . MM methods offer the advantage of low computational cost while still providing diagnostic information; however, there are only limited examples of their use in chemical systems. , Furthermore, these works focus on unbiased output estimation and control, which does not necessarily guarantee unbiased state estimation. The design of an appropriate model set to use with MM methods is also challenging, because there needs to be enough separation between models based on output residuals and enough models to capture the range of system dynamics; however, using too many models will decrease performance …”

Section: Introductionmentioning

confidence: 99%

Handling Model Plant Mismatch in State Estimation Using a Multiple-Model-Based Approach

Arulmaran

Liu

2017

Ind. Eng. Chem. Res.

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Accurate state estimates are important for the success of model predictive control (MPC). State estimates are obtained using a model, but, in real plants, there will always be model plant mismatch (MPM), which affects these estimates. In this work, we present a multiple-model (MM)-based approach to obtain unbiased state estimates in the presence of MPM. Necessary assumptions on the source of mismatch and models used are presented. It is shown that unbiased output estimates do not guarantee unbiased state estimates. Our approach is shown to provide unbiased state estimates when all the assumptions are met using a froth flotation system. A model-identification-based control approach using our multiple model estimation approach with a conventional MPC was tested on the froth flotation system and was found to successfully provide offset-free reference tracking when all the necessary assumptions for unbiased state estimation were met. A nonlinear offset-free MPC was also tested on the froth flotation system but was not able to provide offset-free reference tracking, because some necessary conditions were not met.

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“…Gap metric was introduced, which is the distance between the linear approximation of the nonlinear system and another linear system. − Applications of this metric for nonlinearity estimation and multimodel control have been studied , via weight-based control, the H∞ loop-shaping technique, stability margin , and integration with the neighborhood estimation algorithm . However, linearization may lose an important part of the nonlinearity which is detrimental for the appropriate nonlinearity measurement.…”

Section: Introductionmentioning

confidence: 99%

Consistent and Effective Nonlinearity Index and its Application on Model Predictive Controller Performance Deterioration

Uddin

Tufa

Maulud

2018

Ind. Eng. Chem. Res.

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Many control-relevant systems present the challenges of nonlinearity, directionality and ill-conditioning for the control systems, and exhibit poor controller performance. This study proposes a Nonlinearity Index to quantify the extent of nonlinearity of such systems. A dynamic nonlinear model of a pilot-scale distillation column operating near the azeotropic region was simulated using Aspen Plus Dynamics. A comparison of the results is made with the Nonlinearity Measure proposed by Du et al. Results show that a significant increase is exhibited in the proposed Nonlinearity Index values of the system as the system moves toward the azeotropic region. Prediction errors of the linear models are also shown to be correlated to the proposed index. Therefore, the proposed Nonlinearity Index is consistent with the existing indicators of nonlinearity, and thus its measurements of system nonlinearity are reliable. Controller performance for the system at higher values of the proposed index further presents its efficacy.

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Multiple Model Predictive Control Strategy for Disturbance Rejection

Cited by 31 publications

References 16 publications

Piecewise linear model predictive control of a rapid pressure swing adsorption system

Piecewise linear model predictive control of a rapid pressure swing adsorption system

Handling Model Plant Mismatch in State Estimation Using a Multiple-Model-Based Approach

Consistent and Effective Nonlinearity Index and its Application on Model Predictive Controller Performance Deterioration

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