A projection-based algorithm for model-order reduction with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="mml25" display="inline" overflow="scroll" altimg="si25.gif"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> performance: A convex-optimization setting

Ibrir, Salim

doi:10.1016/j.automatica.2018.03.052

Cited by 13 publications

(9 citation statements)

References 29 publications

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“…Model reduction techniques require the existence of a model of the considered system (typically a first‐principle model). [ 19,20 ] Many model reduction methods in this category are projection‐based methods, in which the original high‐order system is projected onto a lower order reduced space. [ 20 ] Among them, the POD is one of the most widely used methods, which is, for example, applied together with the discrete empirical interpolation method to solve large‐scale model predictive control (MPC) problems for continuous chemical processing.…”

Section: Introductionmentioning

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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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: Introductionmentioning

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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“…Model reduction is carried out based on existing models and aims to simplify it. One of the widely used approaches is to project the high dimension original state space onto a lower dimension subspace [12,13], of which a representative method is the proper orthogonal decomposition (POD) [14]. However, the physical meanings of the original states are often not preserved by the projection approach, making it difficult to handle state constraints.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control of agro-hydrological systems based on a two-layer neural network modeling framework

Huang¹,

Liu²,

Huang³

2022

Preprint

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Water scarcity is an urgent issue to be resolved and improving irrigation water-use efficiency through closed-loop control is essential. The complex agro-hydrological system dynamics, however, often pose challenges in closed-loop control applications. In this work, we propose a twolayer neural network (NN) framework to approximate the dynamics of the agro-hydrological system. To minimize the prediction error, a linear bias correction is added to the proposed model. The model is employed by a model predictive controller with zone tracking (ZMPC), which aims to keep the root zone soil moisture in the target zone while minimizing the total amount of irrigation. The performance of the proposed approximation model framework is shown to be better compared to a benchmark long-short-term-memory (LSTM) model for both open-loop and closed-loop applications. Significant computational cost reduction of the ZMPC is achieved with the proposed framework. To handle the tracking offset caused by the plantmodel-mismatch of the proposed NN framework, a shrinking target zone is proposed for the ZMPC. Different hyper-parameters of the shrinking zone in the presence of noise and weather disturbances are investigated, of which the control performance is compared to a ZMPC with a time-invariant target zone.

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“…Model reduction techniques require the existence of a (typically first-principle) model of the considered system [10,11]. Many model reduction methods in this category are projection-based methods, in which the original high-order system is projected onto a lower-order reduced space [11].…”

Section: Introductionmentioning

confidence: 99%

A comparative study of model approximation methods applied to economic MPC

Huang¹,

Liu²,

Liu³

et al. 2021

Preprint

View full text Add to dashboard Cite

Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for the next generation smart manufacturing. It can lead to improving economic performance but at the same time increases the computational complexity significantly. 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 (WWTP). 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. * Both Zhiyinan Huang and Qinyao Liu are co-first authors and have contributed equally to the work. Qinyao Liu focused more on traditional model approximation and identification while Zhiyinan Huang focused more on explicitly-expressed neural network and optimization.

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A projection-based algorithm for model-order reduction with H2 performance: A convex-optimization setting

Cited by 13 publications

References 29 publications

A comparative study of model approximation methods applied to economicMPC

A comparative study of model approximation methods applied to economicMPC

Model predictive control of agro-hydrological systems based on a two-layer neural network modeling framework

A comparative study of model approximation methods applied to economic MPC

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