An Improved Approach for Robust MPC Tuning Based on Machine Learning

He, Ning; Zhang, Mengrui; Li, Ruoxia

doi:10.1155/2021/5518950

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…From the three tests above, they do not show that using the best model will produce the best control performance as well as has been proven by previous research [26]. This is most likely caused by improper tuning of MPC parameters so that better adjustments must be made [27,28,29,30]. In all the tests conducted, MPC-FOPDT1 showed very poor control performance so it was not used as a comparison.…”

Section: Control Performance Evaluationmentioning

confidence: 84%

Model predictive control with exogenous auto-regressive model to improve performance in the CO2 removal

Wahid

Rodiman

Rahma

et al. 2023

Sinergi

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Model predictive control (MPC) is used in the CO2 removal process in the Subang field to improve its control performance. MPC is used to maintain the CO2 concentration at the sweet gas output by controlling the feed gas pressure (PIC-1101), makeup water flow rate (FIC-1102), and amine flow rate (FIC-1103). The empirical model applied to MPC to represent the process model is the auto-regressive exogenous (ARX) model. The ARX model is compared with the first order plus dead time (FOPDT) model based on the root mean square error (RMSE) between the model and the actual process, then MPC parameters are tuned which include sampling time (T), prediction horizon (P) and control horizon (M) to control for the three variables. Improved control performance is measured based on the integral square error (ISE). The results show that the ARX model is the best model for the CO2 removal process with an RMSE value of 35%-91% smaller than the FOPDT model. The optimal control parameters Prediction Horizon (P), Control Horizon (M) and Sampling Time (T) in the CO2 removal process are 75, 25 and 1 on PIC-1101, 25, 10 and 1 on FIC-1102, and 30, 25 and 1 on FIC-1103. The MPC-ARX (MPC using ARX model) can improve the control performance of 33% in the servo control and 6-56% on the regulatory control. However, not all of them showed an increase in control performance improvement from previous studies even though they had used the best model (ARX). This is due to the MPC parameter setting that is not yet appropriate, so it needs to be retuning.

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Section: Control Performance Evaluationmentioning

confidence: 84%

Model predictive control with exogenous auto-regressive model to improve performance in the CO2 removal

Wahid

Rodiman

Rahma

et al. 2023

Sinergi

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“…at is, there is no error if the calculation result is close to 0, but there is an error if the calculation result is close to 1. e sigmoid logarithm, tangent function, and linear function are all common BPNN transfer functions. is model uses an asymmetric sigmoid function, which is a continuously differentiable sigmoid function with certain threshold properties, such as excitation of functional neurons and the gradient descent learning method [10,11]. Type S function is as follows:…”

Section: Bpnn Predictive Controlmentioning

confidence: 99%

“…He et al proposed a prediction model of graphene defect modification based on the combination of particle swarm optimization algorithm and naive Bayes. In the research, aiming at the discretization of entropy, the expected quotient of attributes is used to guide particle swarm optimization to optimize the segmentation points, so as to find the segmentation points intelligently and quickly [10].…”

Section: Introductionmentioning

confidence: 99%

Prediction Method of Graphene Defect Modification Based on Neural Network

Feng

Yao

et al. 2022

Mobile Information Systems

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Because of its excellent thermal, mechanical, and electrical properties, graphene has been used in a variety of functional coatings. Noncovalent bond functionalization and covalent bond functionalization are the most common graphene surface functionalization methods. Polymer modification, for example, can be used to give graphene and its derivatives new structure, morphology, and properties. The basic structure and predictive control principle of neural networks are discussed in this study, and a high thermal resistance porous graphene structure is reversely designed using machine learning. The effect of a graphene defect modification prediction model based on a GA (genetic algorithm) and improved BPNN (BP neural network) algorithm is investigated. The RMSE predicted by submodels 1–4 decreases by 13.26%, 3.86%, 11.71%, and 19.63%, respectively, according to the simulation results. The BPNN graphene defect modification prediction model optimized by GA has a better training and prediction effect than before optimization.

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“…18 Others have also expanded on using ML based MPC strategies for this same purpose. 19,20 A major challenge associated to MPC-based pH adjustment is operating in a low data regime, particularly of interest for highthroughput small-scale pH adjustment, as opposed to continuous pH adjustment. Aside from automated strategies, pH adjustment process is also often conducted manually in a R&D stage which is time consuming, requiring approximately five to seven minutes per sample (Table 1: entries 7, 10, 13).…”

Section: Introductionmentioning

confidence: 99%

Automated pH Adjustment Driven by Robotic Workflows and Active Machine Learning

Pomberger

Jose

Walz

et al. 2022

Preprint

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Buffer solutions have tremendous importance in biological systems and in formulated products. Whilst the pH response upon acid/base addition to a mixture containing a single buffer can be described by the Henderson-Hasselbalch equation, modelling the pH response for multi-buffered poly-protic systems after acid/base addition, a common task in all chemical laboratories and many industrial plants, is a challenge. Combining predictive modelling and experimental pH adjustment, we present an active machine learning (ML)-driven closed-loop optimization strategy for automating small scale batch pH adjustment relevant for complex samples (e.g., formulated products in the chemical industry). Several ML models were compared on a generated dataset of binary-buffered poly-protic systems and it was found that Gaussian processes (GP) served as the best performing models. Moreover, the implementation of transfer learning into the optimization protocol proved to be a successful strategy in making the process even more efficient. Finally, practical usability of the developed algorithm was demonstrated experimentally with a liquid handling robot where the pH of different buffered systems was adjusted, offering a versatile and efficient strategy for a pH adjustment processes.

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An Improved Approach for Robust MPC Tuning Based on Machine Learning

Cited by 7 publications

References 16 publications

Model predictive control with exogenous auto-regressive model to improve performance in the CO2 removal

Model predictive control with exogenous auto-regressive model to improve performance in the CO2 removal

Prediction Method of Graphene Defect Modification Based on Neural Network

Automated pH Adjustment Driven by Robotic Workflows and Active Machine Learning

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