Speed Control of Three-Phase Induction Motor Using ANFIS and Flower Pollination Algorithm Optimized PI Controller

This paper presents a novel approach for the speed control of a three-phase induction motor by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a Flower Pollination Algorithm (FPA) optimized Proportional-Integral (PI) controller. The primary objective is to improve the efficiency of induction motor speed control under various load conditions, ensuring robustness and adaptability. The ANFIS is employed to intelligently predict and adjust the motor speed in real-time, capitalizing on its ability to handle nonlinearities and uncertainties inherent in the motor dynamics. To further refine the control strategy, the PI controller's parameters are optimized using the Flower Pollination Algorithm, a nature-inspired optimization technique that mimics the pollination process of flowers. This optimization ensures that the PI controller works in perfect harmony with the FPA model, resulting in improved response times, minimized speed overshoot, and enhanced stability under dynamic load changes. Simulation results exhibit the superiority of the proposed method over traditional control strategies, offering significant improvements in speed control accuracy and system resilience. The FPA-PI outperforms the ANFIS based control and provides effective speed control of the motor.