Ekinci Serdar, Izci Davut, Gider Veysel, Abualigah Laith, Bajaj Mohit, Zaitsev Ievgen
Department of Computer Engineering, Batman University, 72100, Batman, Turkey.
Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan.
Sci Rep. 2025 Jan 2;15(1):154. doi: 10.1038/s41598-024-84085-w.
Electric furnaces play an important role in many industrial processes where precise temperature control is essential to ensure production efficiency and product quality. Traditional proportional-integral-derivative (PID) controllers and their modified versions are commonly used to maintain temperature stability by reacting quickly to deviations. In this study, the real PID plus second-order derivative (RPIDD) controller is introduced for the first time for industrial temperature control applications, which is a novel alternative that has not yet been investigated in the literature. To ensure optimal performance, the parameters of the RPIDD controller are optimized using metaheuristic algorithms, including the flood optimization algorithm (FLA), reptile search algorithm (RSA), particle swarm optimization (PSO) and differential evolution (DE). A new approach is proposed which combines the quadratic interpolation optimization (QIO) algorithm with the RPIDD controller, taking advantage of the fast convergence, low computational cost and high accuracy of QIO. Comparative analyses between QIO-RPIDD, FLA-RPIDD, RSA-RPIDD, PSO-RPIDD and DE-RPIDD controller are performed by evaluating performance metrics such as transient and frequency response. The results show that QIO-RPIDD achieves superior performance, adapts quickly to different reference temperatures and performs excellently on key performance indicators. These results make the proposed QIO-RPIDD controller a promising solution for industrial temperature control and contribute to more efficient and adaptive optimization techniques.
电炉在许多工业过程中发挥着重要作用，在这些过程中，精确的温度控制对于确保生产效率和产品质量至关重要。传统的比例积分微分（PID）控制器及其改进版本通常用于通过对偏差快速做出反应来维持温度稳定性。在本研究中，首次将实数PID加二阶导数（RPIDD）控制器引入工业温度控制应用，这是一种尚未在文献中研究过的新颖替代方案。为确保最佳性能，使用元启发式算法对RPIDD控制器的参数进行优化，包括洪水优化算法（FLA）、爬行动物搜索算法（RSA）、粒子群优化（PSO）和差分进化（DE）。提出了一种将二次插值优化（QIO）算法与RPIDD控制器相结合的新方法，利用了QIO的快速收敛、低计算成本和高精度。通过评估诸如瞬态和频率响应等性能指标，对QIO-RPIDD、FLA-RPIDD、RSA-RPIDD、PSO-RPIDD和DE-RPIDD控制器进行了比较分析。结果表明，QIO-RPIDD具有卓越的性能，能快速适应不同的参考温度，并在关键性能指标上表现出色。这些结果使所提出的QIO-RPIDD控制器成为工业温度控制的一个有前途的解决方案，并有助于实现更高效和自适应的优化技术。
