Design of a Novel PID Controller Based on Machine Learning Algorithm for a Micro-Thermoelectric Cooler of the Polymerase Chain Reaction Device

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dc.contributor.authorArikusu Y.S.
dc.contributor.authorBayhan N.
dc.date.accessioned2024-07-22T08:01:57Z
dc.date.available2024-07-22T08:01:57Z
dc.date.issued2024
dc.description.abstractThe combined use of Extreme Gradient Boosting (XGBoost) algorithm, one of the machine learning (ML) methods, and a generalization of Hermite-Biehler theorem to obtain a novel PID controller that will ensure robustly stable and optimized operation of a micro thermoelectric cooler (Micro-TEC), which is the main part of a Polymerase chain reaction (PCR) device, is a unique approach of our study compared to previous studies. Therefore, we first established a mathematical model of the micro-TEC by making real-time measurements and then, a new data set was created to find the optimum parameter values of PID controller, and finally, XGBoost Hyperparameters with GridSearchCV was used for the first time to predict PID controller parameters. The XGBoost algorithm achieved 97% training success and 91% test success in estimating the parameters of the PID controller. Moreover, the novel controller developed using the XGBoost algorithm in this study has an impressive speed of 3 seconds. Additionally, our proposed method was compared with various metaheuristic optimization algorithms in terms of error percentage. The error percentages of XGBoost, the equilibrium optimization, the particle swarm optimization and the artificial bee colony optimization algorithms were found to be 0.4%, 1.1%, 3.7% and 11.1%, respectively. It is observed the settling times of micro-TEC with ML-PID controller for all five PCR cycles are 4.86, 44, 83.4, 123 and 162.5 seconds, respectively, and the overshoot values are below 5%. The proposed method gave the smallest settling time, error and overshoot percentages compared to these metaheuristic optimization algorithms. © 2013 IEEE.
dc.identifier.DOI-ID10.1109/ACCESS.2024.3392734
dc.identifier.issn21693536
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11663
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rightsAll Open Access; Gold Open Access
dc.subjectAdaptive boosting
dc.subjectChains
dc.subjectControllers
dc.subjectCooling systems
dc.subjectDNA
dc.subjectElectric control equipment
dc.subjectElectric power system control
dc.subjectErrors
dc.subjectLearning systems
dc.subjectParameter estimation
dc.subjectParticle swarm optimization (PSO)
dc.subjectPolymerase chain reaction
dc.subjectProportional control systems
dc.subjectThree term control systems
dc.subjectTwo term control systems
dc.subjectGridsearchcv
dc.subjectMachine learning algorithms
dc.subjectMachine-learning
dc.subjectPID controllers
dc.subjectPower systems stability
dc.subjectPrediction algorithms
dc.subjectStability analyze
dc.subjectThermoelectric cooler
dc.subjectXgboost algorithm
dc.subjectThermoelectric equipment
dc.titleDesign of a Novel PID Controller Based on Machine Learning Algorithm for a Micro-Thermoelectric Cooler of the Polymerase Chain Reaction Device
dc.typeArticle

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