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Yayın A novel gudermannian function-driven controller architecture optimized by starfish optimizer for superior transient performance of automatic voltage regulation(Multidisciplinary Digital Publishing Institute (MDPI), 2026) İzci, Davut; Ekinci, Serdar; Jabari, Mostafa; Kocaman, Behçet; Bektaş Güneş, Burcu; Adas, Enver; Ahmad, Mohd AshrafThis paper proposes a Gudermannian function-based proportional-integral-derivative (G-PID) controller to enhance the transient performance of automatic voltage regulator (AVR) systems operating under highly dynamic conditions. By embedding the smooth and bounded nonlinear mapping of the Gudermannian function into the classical PID structure, the proposed controller improves adaptability to large signal variations while effectively suppressing overshoot. The controller parameters are optimally tuned using the starfish optimization algorithm (SFOA), which provides a robust balance between exploration and exploitation in nonlinear search spaces. Simulation results demonstrate that the SFOA-optimized G-PID controller achieves superior transient performance, with a rise time of 0.0551 s, zero overshoot, and a settling time of 0.0830 s. Comparative evaluations confirm that the proposed approach outperforms widely used optimization algorithms (particle swarm optimization, grey wolf optimizer, success history-based adaptive differential evolution with linear population size, and Kirchhoff's law algorithm) and advanced AVR control schemes, including fractional-order and higher-order PID-based designs. These results indicate that the proposed SFOA optimized G-PID controller offers a computationally efficient and structurally simple solution for high-performance voltage regulation in modern power systems.Yayın Enhanced temperature control of continuous stirred tank reactors using QIO-based 2-DoF PID controller(Universitas Muhammadiyah Yogyakarta, 2025) Ekinci, Serdar; İzci, Davut; Jabari, Mostafa; Ma'arif, AlfianAccurate temperature control of continuous stirred tank reactors (CSTRs) remains a major challenge due to the nonlinear dynamics and inherent time delay of the system. Conventional proportional-integral-derivative (PID) controllers often struggle to maintain optimal performance under such complexities, highlighting the need for more advanced control strategies. In this study, a two-degree-of-freedom (2-DOF) PID controller is designed and optimized using the quadratic interpolation optimization (QIO) to enhance temperature regulation in CSTRs. The proposed approach aims to minimize steady-state error, settling time, and overshoot. To implement this method, the nonlinear model of the CSTR is linearized around a stable operating point, and the controller parameters are tuned by minimizing a composite cost function consisting of normalized overshoot and instantaneous error. Simulation results demonstrate that the QIO-based 2-DOF PID controller significantly outperforms other metaheuristic approaches such as differential evolution, particle swarm optimization, slime mould algorithm, and greater cane rat algorithm. Furthermore, comparisons with recent works reveal substantial improvements in rise time, settling time, and steady-state accuracy.Yayın Nonlinear control of engine speed regulation using grey wolf optimizer for enhanced system stability and performance(Universitas Muhammadiyah Yogyakarta, 2025) Ekinci, Serdar; İzci, Davut; Jabari, Mostafa; Ma'arif, AlfianAccurate control of internal combustion engine speed, especially under variable load conditions, has always been a significant challenge in the automotive industry. Classical PID controllers often fail to effectively compensate for nonlinearities and environmental disturbances in spark ignition (SI) engines. To address this issue, we propose a method based on tuning PIDF controller parameters using the grey wolf optimizer (GWO) to enhance system stability and performance. This approach aims to reduce steady-state error, settling time, and overshoot. A mathematical model of the engine speed control system is developed, and the GWO algorithm is applied to optimize the PIDF gains. The performance of the GWO-based controller is then compared to other metaheuristic methods such as particle swarm optimization (PSO), differential evolution (DE), and cuckoo search (CS) algorithms through simulation. Simulation results demonstrate that the proposed GWO-based approach outperforms the alternatives by achieving better error reduction, improved stability, enhanced disturbance rejection, and faster response times.Yayın Reliable parameter estimation of nonlinear chaotic systems and PMSMs with the stellar oscillation optimizer(Nature Research, 2026) Ekinci, Serdar; İzci, Davut; Jabari, Mostafa; Elsayed, Fahmi; Salman, Mohammad; Bektaş Güneş, BurcuAccurate parameter identification is a critical prerequisite for reliable modeling, analysis, and control of nonlinear dynamical systems. This study introduces the stellar oscillation optimizer (SOO), a recently proposed metaheuristic inspired by the oscillatory behavior of stars, and investigates its effectiveness in estimating system parameters through a unified optimization framework. The identification problem is formulated as the minimization of a trajectory-mismatch cost function, where candidate solutions are iteratively refined by the oscillatory dynamics of SOO. To comprehensively evaluate its performance, four benchmark systems were considered: three canonical chaotic models (Lorenz, Chen, and R & ouml;ssler) and a practical engineering case represented by a permanent-magnet synchronous motor (PMSM). The outcomes were benchmarked against several state-of-the-art algorithms, including Kirchhoff's law algorithm (KLA), Tianji's horse racing optimization (THRO), puma optimizer (PO), and hiking optimization algorithm (HOA), under a standardized protocol. The results show that SOO consistently achieves numerically convergent solutions with machine-precision-level residuals under deterministic and noise-free simulation settings, while maintaining strong robustness across independent runs. In chaotic benchmarks, the reported residuals approach floating-point limits, which indicates stable numerical convergence rather than guaranteed physical identifiability under real measurement conditions. On the PMSM model, SOO demonstrates accurate and repeatable parameter estimation within the adopted simulation framework.Yayın Spider wasp optimizer-based PID control approach for temperature management in continuous stirred tank reactors(Institute of Electrical and Electronics Engineers Inc., 2025) İzci, Davut; Jabari, Mostafa; Ekinci, Serdar; Ghandour, Raymond; Salman, MohammadTemperature control in continuous stirred tank reactors (CSTRs) poses a significant challenge due to the inherent nonlinearity of the process and the presence of time delays. These complexities often result in unstable and inefficient performance of traditional control systems. In this paper, a novel approach based on optimizing a PID-F controller using the spider wasp optimizer (SWO) is proposed to address temperature management in CSTRs. This method leverages SWO's computational capabilities to finely tune the controller parameters, effectively overcoming the challenges of nonlinearity and time delays. To implement this solution, the CSTR's mathematical model was linearized and approximated as a stable first-order plus time delay (SFOPTD) model. The performance of SWO in optimizing the PID-F controller was then compared with other established algorithms (CFOA, FLA, and MPA) through simulation studies. Simulation results demonstrate that the proposed SWO-based approach achieves a minimum rise time (0.0126 s), the lowest overshoot (0.1249%), and a fast settling time (0.0645 s). These outcomes highlight SWO's superior performance over competing algorithms, underscoring its potential for industrial applications in CSTR control.
