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The aim of this article is to design an improved African buffalo optimization-based Takagi–Sugeno–Kang Fuzzy PI Controller (IABO-TSKFPIC) Model for brushless direct current motors to achieve better and more reliable speed control under various load and speed conditions. Due to the nonlinearity properties of BLDC motor drives, such as extended power fluctuation, settling time, fluctuation of steady-state error, and other effects, controllability is poor. To overcome these issues, the proposed model uses an improved Takagi–Sugeno–Kang fuzzy PI controller that incorporates the IABO algorithm in a self-tuned regulator structure to minimize the supplied objective function and meet inequality requirements. The use of Hall sensors in speed control is restricted due to component failure, low dependability, the need for specialized mechanical mounting solutions, and electrical noise issues. The performance of the IABO-TSKFPIC Model is evaluated in terms of rise time, peak overshoot, settling time, and steady-state error. Simulation results show that the suggested IABO-tuned TSK Fuzzy PI controller outperforms traditional and metaheuristic algorithms in terms of time-domain characteristics, control effort, and performance indices. The IABO-TSKFPIC Model improves the performance of the optimized fuzzy PI controller with greater output voltage compared to state-of-the-art studies.

Improved African Buffalo Optimization-Based Takagi–Sugeno–Kang Fuzzy PI Controller for Speed Control in BLDC Motor