An efficient PID-based optimizer loop and its application in De Jong’s functions minimization and quadratic regression problems

Alagöz, Barış Baykant; Deniz, Furkan Nur; Köseoğlu, Murat

doi:10.1016/j.sysconle.2021.105090

Cited by 7 publications

(16 citation statements)

References 18 publications

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“…Previously, estimates of annual apricot production for Turkey were presented using various time series analyses in some previous studies [12], [13]. In this study, an application of the PID-based optimizer system [10] was demonstrated for the polynomial modelling of the annual apricot production data from Malatya region.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In addition to these studies, an effective algorithm has been presented for solving nonlinear, unconstrained and multiparameter optimization problems using a PID-based optimizer system [10]. The PID-based optimizer system is based on convergence of the error to zero using a slope-sensitive objective function in a closed-loop feedback system [10]. Thus, it has been shown that the consideration of the slope direction of the objective function increases the convergence speed and accuracy of the minimization process [10].…”

Section: Introductionmentioning

confidence: 99%

“…The PID-based optimizer system is based on convergence of the error to zero using a slope-sensitive objective function in a closed-loop feedback system [10]. Thus, it has been shown that the consideration of the slope direction of the objective function increases the convergence speed and accuracy of the minimization process [10].…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1 shows a block diagram of PID-based optimizer system. The PID controller promises to reduce the error signal in the closed-loop control system where r is the reference signal and q is the output signal [10].…”

Section: Brief Introduction Of the Pid-based Optimizer Systemmentioning

confidence: 99%

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PID tabanlı optimize edici döngüsü için bir uygulama: Malatya kayısısının yıllık üretim regresyon modellerinin tahmini

Deniz

2022

DÜMF MD

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In this study, a data analysis application for the PID-based optimizer loop, which was previously proposed in a former study, is carried out. In this application, quadratic and cubic polynomial regression models were obtained for the estimation of annual apricot production by using the yearly total apricot production data of Malatya between 1991 and 2020. In addition, an average of these regression model estimations was calculated to increase estimation reliability. Annual apricot production amount was estimated by using the regression models obtained with the PID-based optimizer system between 2021-2025. The results were compared with the results obtained with the Matlab curve fitting toolbox.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Brief Introduction Of the Pid-based Optimizer Systemmentioning

confidence: 99%

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PID tabanlı optimize edici döngüsü için bir uygulama: Malatya kayısısının yıllık üretim regresyon modellerinin tahmini

Deniz

2022

DÜMF MD

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A hybrid robust model reference adaptive controller and proportional integral controller without reference model for partially modeled systems

Evald

Hollweg

Tambara

et al. 2023

Adaptive Control & Signal

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Summary This work presents the discrete‐time stability analysis of a hybrid robust model reference adaptive controller and proportional‐integral controller (MRAC‐PI) controller using Lyapunov criterion. The control structure merges the classical PI controller with a robust model reference adaptive control, resulting in an adaptive and robust control strategy, where the gains unite both control techniques in a unique structure and it does not require the design of a reference model, being able to track straightforwardly the reference signal. The adaptive gains are updated continuously by a modified gradient algorithm regarding control action, regulation error, and the system output. The stability analysis provides the controller design constraints, while also demonstrate the tendency of regulation error to a residual set, and proves the boundedness of all closed‐loop signals. Furthermore, as the controller is robust to unmodeled dynamics, it is feasible to control partially modeled systems. Simulation results in a real‐world engineering application are presented to corroborate the robustness of this controller and show its fast response in the face of critical exogenous system perturbations.

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Auto-tuning of filtered proportional-integral-derivative controller for industrial processes under routine operating conditions

Gao,

Shen,

Huang

2024

ISA Transactions

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An efficient PID-based optimizer loop and its application in De Jong’s functions minimization and quadratic regression problems

Cited by 7 publications

References 18 publications

PID tabanlı optimize edici döngüsü için bir uygulama: Malatya kayısısının yıllık üretim regresyon modellerinin tahmini

PID tabanlı optimize edici döngüsü için bir uygulama: Malatya kayısısının yıllık üretim regresyon modellerinin tahmini

A hybrid robust model reference adaptive controller and proportional integral controller without reference model for partially modeled systems

Auto-tuning of filtered proportional-integral-derivative controller for industrial processes under routine operating conditions

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