Tuning of FOPID parameters combined with swarm intelligent algorithm

This paper delves into the parameter tuning of fractional-order PID (FOPID) controllers. FOPID controllers, with additional integral and derivative orders compared to traditional PID controllers, possess enhanced capabilities in handling complex systems. However, effective tuning of its five parameters is challenging. To address this, multiple intelligent algorithms are investigated. The improved sparrow search algorithm (ISSA) utilizes Chebyshev chaotic mapping initialization, adaptive t-distribution, and the firefly algorithm to overcome the limitations of the basic algorithm, showing high accuracy, speed, and robustness in multi-modal problems. The grey wolf optimizer (GWO), inspired by the hunting behavior of grey wolves, has procedures for encircling, hunting, and attacking but may encounter local optima, and several improvement methods have been proposed. The genetic algorithm, based on the survival of the fittest principle, involves encoding, decoding, and other operations. Taking vehicle ABS control as an example, the genetic algorithm-based FOPID controller outperforms the traditional PID controller. In conclusion, different algorithms have their own advantages in FOPID parameter tuning, and the selection depends on system characteristics and control requirements. Future research can focus on further algorithm improvement and hybrid methods to achieve better control performance, providing a valuable reference for FOPID applications in industry.