An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

Chee, Ewan; Wong, Weng Fai; Wang, Xiaonan

doi:10.1007/s11705-021-2058-6

Cited by 16 publications

(5 citation statements)

References 23 publications

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“…As an advantage, MPC can optimize performance of a system while considering constraints [129]. Here, ML is particularly useful for complex non-linear systems or systems for which little process understanding exists [130].…”

Section: Many Ml-based Approaches For Soft Sensors Rely On Anns [Box ...mentioning

confidence: 99%

Machine learning in bioprocess development: from promise to practice

Helleckes

Hemmerich

Wiechert

et al. 2023

Trends in Biotechnology

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Section: Many Ml-based Approaches For Soft Sensors Rely On Anns [Box ...mentioning

confidence: 99%

Machine learning in bioprocess development: from promise to practice

Helleckes

Hemmerich

Wiechert

et al. 2023

Trends in Biotechnology

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“…The Output-Error (OE) estimator has the advantage of being more readily calculable than the predictionerror estimator. Using SI techniques such as the Hammerstein Weiner, Auto Regressive with Exogenous Input (ARX), Auto Regressive Moving Average with Exogenous Input (ARMX), Box-Jenkins (BJ) and OE models, a mathematical model was designed for a laboratory-based heating system [11][12][13][14][15][16][17]. The BJ model provides the greatest Final Prediction Error (FPE), correlation analysis, percentage of fitness, and loss function according to the simulated results [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Identification of heat exchanger process through Hammerstein and polynomial models

L D

2024

Phys. Scr.

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Process industries extensively use heat exchangers in their operations, playing a crucial role in various sectors by facilitating efficient heat transfer, conserving energy, and reducing operational costs. This paper focuses on identifying and validating the system model, with the primary task of designing the controller involving the determination of the mathematical representation of the system. The First Order Plus Dead Time (FODT) model, derived from basic principles, is used to represent the system. To analyze the system's behavior and construct a suitable controller, model development is essential, achieved by constructing principles models using energy balance equations to identify the heat exchanger process. Data collected from the model are utilized in the identification process, with the temperature at the outlet of the hot air blower being the controlled variable in this investigation. The research aims to determine the mathematical model based on time versus temperature data acquired from the Heat Exchanger. Various system identification methods, such as Hammerstein Wiener (HW), Auto Regressive with Exogenous Input (ARX), Box-Jenkins (BJ), Output-Error (OE), and Auto Regressive Moving Average with Exogenous Input (ARMAX) models, are implemented for the heat exchanger. The models obtained undergo validation, and the best-fit model closest to the physical system is considered for controller design. After conducting the analysis, it was found that the Output-Error (OE) model outperforms other models in terms of achieving the best fit.

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“…It is a highly flexible and powerful control scheme that permits the inclusion of practical constraints (e.g., temperature limits of a heating jacket) on the manipulated variables, thereby facilitating the search of feasible solutions to optimization-based control problems by respecting any physical limits imposed. Collectively, the convergence of next-generation information technologies and the pervasive nature of data in modern manufacturing complexes culminates in machine learning-based MPC (ML-MPC), which has attracted an increased level of attention in recent years, and is gaining traction in control of highly nonlinear processes. − In ref an ML-MPC is developed using an autoencoder-based recurrent neural network (RNN) to control the batch crystallization process, where the crystal size and yield are optimized. Additionally, the ML-MPC scheme is equipped with an error-triggered online update mechanism to mitigate issues pertaining to plant-model mismatch and to improve the overall control performance .…”

Section: Introductionmentioning

confidence: 99%

Physics-Informed Online Machine Learning and Predictive Control of Nonlinear Processes with Parameter Uncertainty

Zheng

2023

Ind. Eng. Chem. Res.

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In this work, we present a physics-informed recurrent neural network (PIRNN)-based modeling approach for nonlinear dynamic systems with parameter uncertainty. Physics-informed modeling approaches can improve the generalization performance of machine learning models by embedding the knowledge of physical laws in the learning process. Based on the standard PIRNN modeling approach for the nominal system without model uncertainties, we develop a novel PIRNN-enhanced modeling method that integrates online estimation of uncertain process parameters into the training process using the latest process data. The PIRNN-enhanced modeling approach is incorporated into the design of model predictive control (MPC), where an error-triggered mechanism is employed to trigger the quantification of modeling uncertainties and update of PIRNN models accordingly. Finally, a chemical process example is used to demonstrate the superiority of the proposed PIRNN-enhanced online learning mechanism in comparison to the conventional purely data-driven online update strategy for nonlinear systems subject to process parameter uncertainty.

show abstract

An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

Cited by 16 publications

References 23 publications

Machine learning in bioprocess development: from promise to practice

Machine learning in bioprocess development: from promise to practice

Identification of heat exchanger process through Hammerstein and polynomial models

Physics-Informed Online Machine Learning and Predictive Control of Nonlinear Processes with Parameter Uncertainty

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