An advanced PID tuning method for temperature control in electric furnaces using the artificial rabbits optimization algorithm

Abstract

In this study, we aim to enhance temperature control in electric furnaces, addressing key challenges in precision and response stability. We propose an integrated approach featuring a proportional–integral–derivative with N filter (PIDN) controller alongside the artificial rabbit’s optimization (ARO) algorithm. The proposed PIDN controller incorporates adaptive tuning techniques designed to improve response accuracy and reduce overshoot, tailored specifically for the dynamic requirements of electric furnace applications. To optimize the PIDN parameters, we employ the ARO algorithm, a recent metaheuristic inspired by rabbit social behaviors, which has been customized for this control application. To evaluate the performance of the proposed framework, we introduce a modified objective function based on the integral of absolute error, emphasizing both transient and steady-state improvements. Comparative assessments with traditional controllers and metaheuristic algorithms, including the electric eel foraging optimization and whale optimization algorithm tuned by PIDN, genetic algorithm tuned by PID, Cohen–Coon algorithm tuned by PID, and direct synthesis algorithm tuned by PID, confirm the superior efficacy of our approach. Extensive tests including statistical analysis, noisy condition, and time and frequency domain evaluations demonstrate that our controller remains robust under nonideal conditions, such as measurement noise, external disturbances, and saturation. Results underscore the adaptability and effectiveness of this approach, marking a significant advancement in temperature control for electric furnaces.