Fractional Order PID Controller Design for an AVR System Using the Artificial Hummingbird Optimizer Algorithm

Optimizing the fractional-order PID (FOPID) controller using metaheuristic algorithms has gained significant popularity across various engineering domains. This paper introduces a novel approach by employing the artificial hummingbird algorithm (AHA), an innovative optimization technique inspired by the unique flight and foraging behaviors of hummingbirds, to fine-tune the FOPID controller for the automatic voltage regulator (AVR) system in synchronous generators, a critical component in maintaining voltage stability. The proposed method is rigorously tested using MATLAB/Simulink simulations under challenging conditions, including nonsmoothed higher-order dynamics of the control plant, parameter variations, time delays, and nonlinearities. The effectiveness of the AHA-based FOPID control strategy on the AVR system is comprehensively evaluated through extensive tests and analyses, focusing on aspects such as transient response, robustness, stability, and trajectory tracking. Moreover, a comparative assessment against established optimization algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), gray wolf optimizer (GWO), and artificial bee colony (ABC) is conducted. The results demonstrate the superiority of the proposed AHA-based FOPID control strategy, which significantly increases convergence speed. This is evidenced by a 25% faster rise time and a 45.74% shorter settling time compared to the GA-FOPID controller, the closest in performance for these metrics. Additionally, the AHA-based FOPID controller achieves a 92% reduction in steady-state oscillations compared to the ABC-FOPID controller, the nearest competitor in this aspect. These improvements highlight the AHA-based FOPID controller's superior efficiency and rapid response in achieving optimal performance. Hence, the proposed method shows remarkable success in enhancing stability and robustness, making it highly suitable for the design of practical high-performance applications.