Greater cane rat algorithm-based real PIDD 2 controller design strategy for temperature regulation of electric furnaces

Abstract

Maintaining precise temperature control in electric furnaces with inherent time delays remains a significant challenge due to the limitations of conventional controllers in handling nonlinear dynamics, delayed thermal responses, and external disturbances. Traditional PID-based methods often suffer from excessive overshoot, slow convergence, and inadequate robustness in delay-sensitive environments. To address these shortcomings, this paper introduces, for the first time in the literature, an innovative hybrid control strategy combining a real proportional–integral–double-derivative (PIDD²) controller with the recently proposed greater cane rat algorithm (GCRA). The proposed approach leverages low-pass filtered second-order derivative feedback for improved control fidelity and GCRA’s adaptive search capabilities for precise parameter tuning. Extensive simulations validate the effectiveness of the GCRA-based PIDD² controller, achieving a significantly reduced overshoot of 0.8443%, a fast settling time of 3.1201 s, and a low integral absolute error (IAE) of 2.0101. Compared to contemporary controllers optimized by catch fish optimization algorithm, artificial hummingbird algorithm, RIME, aquila optimizer, and others, the proposed method consistently outperforms across transient and steady-state metrics. Moreover, it demonstrates robust tracking performance and resilience under variable set-points and noisy environments. These results affirm the potential of the proposed controller as a high-performance solution for temperature regulation in industrial furnaces with time-delay characteristics.