Evidence map›Paper›PMID 41086170›Full record

ArticlePloS one2025

A Novel 2-DOF PIDA control strategy with GCRA-based parameter optimization for electric furnace temperature control.

Erdal Eker, Davut Izci, Serdar Ekinci, Fahmi Elsayed, Mohammad Salman

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Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Erdal EkerVocational School of Social Sciences, Muş Alparslan University, Muş, Turkey.ORCID https://orcid.org/0000-0002-5470-8384
Davut IzciDepartment of Electrical and Electronics Engineering, Bursa Uludag University, Bursa, Turkey.ORCID https://orcid.org/0000-0001-8359-0875
Serdar EkinciDepartment of Computer Engineering, Bitlis Eren University, Bitlis, Turkey.
Fahmi ElsayedCollege of Engineering and Technology, American University of the Middle East, Egaila, Kuwait.ORCID https://orcid.org/0000-0003-2261-9907
Mohammad SalmanCollege of Engineering and Technology, American University of the Middle East, Egaila, Kuwait.ORCID https://orcid.org/0000-0002-1769-6652

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate and energy-efficient temperature regulation in electric furnace systems remains a challenging control problem due to nonlinear dynamics, significant thermal inertia, and inevitable time delays. Conventional proportional-integral-derivative (PID) and PID-acceleration (PIDA) controllers, though widely used, often exhibit degraded performance under such conditions, particularly when implemented in a single-degree-of-freedom. To address these limitations, this study proposes, for the first time, a two-degree-of-freedom (2-DOF) PIDA controller tailored for electric furnace temperature control. The controller structure allows independent tuning of set-point tracking and disturbance rejection by introducing separate feedforward paths in the proportional and derivative channels while maintaining integral and acceleration actions on the error signal. To optimize the controller parameters, the recently developed greater cane rat algorithm (GCRA) is employed for the first time in this context. A novel adaptive objective function (combining normalized overshoot, normalized settling time, and cumulative tracking error) guides the tuning process to achieve a balanced improvement in both transient and steady-state performance. The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. Results demonstrate that the proposed method consistently achieves faster settling times, reduced overshoot, and near-zero steady-state error, while maintaining robustness under external disturbances and measurement noise. For instance, in the nominal case, the method yields an overshoot of 1.8382% and a settling time of 3.4542 s, outperforming PFA, HOA, L-SHADE, and PSO. Robustness tests under load disturbances and measurement noise confirm stable operation with minimal performance degradation, achieving less than 2.5% overshoot and under 4 s settling time across all evaluated scenarios. These findings highlight the potential of the GCRA-based 2-DOF PIDA controller as a high-precision and energy-efficient solution for temperature regulation in industrial time-delay systems.

Indexed as

AlgorithmsTemperatureModels, Theoretical

Identifiers

PMID41086170
PMCPMC12520383

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.