Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

Son, Seung Hyun; Choi, Hyun‐Kyu; Kwon, Joseph

doi:10.1002/aic.17301

Cited by 47 publications

(8 citation statements)

References 60 publications

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“…The overall performance of the NN model is better than models with three linearization points but worse than models with five linearization points and the subspace LTI model.Remark Comparing Figures 6 and 8, it is observed that for EMPC, multiple approximation models have responses with significant offsets compared to the original model. With various works done on offset‐free MPC, [ 46–48 ] it is natural to consider if the same approaches can be adopted for EMPC applications. The answer is no, in general.…”

Section: Resultsmentioning

confidence: 99%

“…Comparing Figures 6 and 8, it is observed that for EMPC, multiple approximation models have responses with significant offsets compared to the original model. With various works done on offset-free MPC, [46][47][48] it is natural to consider if the same approaches can be adopted for EMPC applications. The answer is no, in general.…”

Section: Economic Model Predictive Controlmentioning

confidence: 99%

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A comparative study of model approximation methods applied to economicMPC

Huang¹,

Liu²,

Liu

et al. 2022

Can J Chem Eng

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Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for next‐generation smart manufacturing. It has the potential to not only improve economic performance but also significantly increase computational complexity. Model approximation has been a standard approach for reducing computational complexity in process control. In this work, we perform a study on three types of representative model approximation methods applied to EMPC, including model reduction based on available first‐principle models (e.g., proper orthogonal decomposition), system identification based on input–output data (e.g., subspace identification) that results in an explicitly expressed mathematical model, and neural networks based on input–output data. A representative algorithm from each model approximation method is considered. Two processes that are very different in dynamic nature and complexity were selected as benchmark processes for computational complexity and economic performance comparison, namely, an alkylation process and a wastewater treatment plant. The strengths and drawbacks of each method are summarized according to the simulation results, with future research direction regarding control‐oriented model approximation proposed at the end.

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Section: Resultsmentioning

confidence: 99%

Section: Economic Model Predictive Controlmentioning

confidence: 99%

A comparative study of model approximation methods applied to economicMPC

Huang¹,

Liu²,

Liu

et al. 2022

Can J Chem Eng

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show abstract

“…( 15). Several works employed a linear Koopman approach in identification and control studies and observed a higher performance over conventional linear identification methods [27][28][29][30][31][32][33][34]. However, when significant nonlinearity is present, Eq.…”

Section: Koopman Theory For Controlled Systemsmentioning

confidence: 99%

“…Specifically, we employ Koopman theory to identify low-order latent dynamics describing the nonlinear system evolution. While the existing literature on Koopman modeling for control has focused on linear [27][28][29][30][31][32][33][34] and bilinear [35][36][37][38] forms, we consider a MIMO Wiener structure. As we show, when combined with Koopman theory, the Wiener structure provides strong model reduction capabilities beyond those of linear and bilinear models.…”

Section: Introductionmentioning

confidence: 99%

Identification of MIMO Wiener-type Koopman Models for Data-Driven Model Reduction using Deep Learning

Schulze,

Doncevic,

Mitsos

2022

Preprint

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We use Koopman theory to develop a data-driven nonlinear model reduction and identification strategy for multiple-input multiple-output (MIMO) input-affine dynamical systems. While the present literature has focused on linear and bilinear Koopman models, we derive and use a Wiener-type Koopman formulation. We discuss that the Wiener structure is particularly suitable for model reduction, and can be naturally derived from Koopman theory. Moreover, the Wiener block-structure unifies the mathematical simplicity of linear dynamical blocks and the accuracy of bilinear dynamics. We present a Koopman deep-learning strategy combining autoencoders and linear dynamics that generates low-order surrogate models of MIMO Wiener type. In three case studies, we apply our framework for identification and reduction of a system with input multiplicity, a chemical reactor and a high-purity distillation column. We compare the prediction performance of the identified Wiener models to linear and bilinear Koopman models. We observe the highest accuracy and strongest model reduction capabilities of low-order Wiener-type Koopman models, making them promising for control.

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“…An ILC controller distills information from the tracking error in the past to better tune the control input of the present trial (termed “batch” thereafter), etc., until achieving the perfect tracking of the given set-point profile. Notably, over the past decades, a multitude of achievements for better ILC have been witnessed both theoretically − and practically. − Encouraging endeavors of applying ILC in practice include injection molding, bioreactors, and batch chemical reactors, whereas considerable theoretical efforts are devoted to answering the longstanding questionhow to synthesize an ILC controller against various uncertainties. Such efforts include the introduction of feedback, multipoint compensation, adaptive tuning, , and optimal design for linear systems and nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Learning of Iterative Learning Control for Flexible Manufacturing of Batch Processes

Zhong

et al. 2022

ACS Omega

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Flexible manufacturing as an essential component of smart manufacturing implements the customized production mode, thereby requesting fast controller adaptation for producing different goods but still with high precision. This problem becomes even more acute for batch processes. Here we present a solution called learning of iterative learning control (ILC) based on neural networks. It is able to recommend control parameters for ILC controllers accordingly, so as to yield fast tracking error convergence and smaller steady-state error for disparate set-point profiles, which is deemed an abstraction of different production needs. The method substantially outperforms a benchmark ILC on a variety of systems and cases, thereby showing its potential for deployment in the industrial Internet of Things.

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Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

Cited by 47 publications

References 60 publications

A comparative study of model approximation methods applied to economicMPC

A comparative study of model approximation methods applied to economicMPC

Identification of MIMO Wiener-type Koopman Models for Data-Driven Model Reduction using Deep Learning

Learning of Iterative Learning Control for Flexible Manufacturing of Batch Processes

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