A new control scheme for temperature adjustment of electric furnaces using a novel modified electric eel foraging optimizer

In this study, we present a comprehensive framework for enhancing the temperature control of electric furnaces, integrating three novel components: a proportional-integral-derivative controller with a filter (PID-F), a customized objective function, and a modified electric eel foraging optimization (mEEFO) algorithm. The PID-F controller, introduced for the first time in the literature for temperature control of electric furnaces, leverages a filter coefficient to effectively mitigate the kick effect, improving transient and frequency responses. To further optimize the PID-F controller, we employed the mEEFO, a recently proposed metaheuristic inspired by the social predation behaviors of electric eels, with tailored modifications for electric furnace temperature control. The study also introduces a new objective function, based on the modification of the integral of absolute error (IAE) performance index. The proposed framework was evaluated through extensive comparisons with established metaheuristic algorithms, including statistical analysis, Wilcoxon signed-rank test, and time and frequency domain analyses. Comparative assessments with reported methods, such as genetic algorithms and Ziegler–Nichols-based PID controllers, validated the efficacy of the proposed approach, highlighting its transformative impact on electric furnace temperature regulation. The non-ideal conditions such as measurement noise, external disturbance, and saturation at the output of the controller were also evaluated in order to demonstrate the superior performance of the proposed approach from a wider perspective. Furthermore, the robustness of the proposed approach against variations in system parameters was also demonstrated.