Brushless DC motor optimum speed control using TSA-PID strategy

Due to the applied load torque variations, the electric Direct Current (DC) motors' speed has many more fluctuations. The main objective of this research is to study the speed control of Brushless DC Motors (BLDCMs) using a Proportional Integral Derivative (PID) controller that regulates the motor voltage. This controller was automatically tuned using the Transit Search Algorithm (TSA). The effects of the proposed controller on BLDCM speed control were studied, analyzed, and compared with those of other PID gain calculation methods like Particle Swarm Optimization (PSO), Adaptive Tabu Search (ATS), Genetic Algorithm (GA), Rime-inspired metaheuristic (RIME) and Whale Optimization Algorithm (WOA). The performance is evaluated by contrasting speed-response characteristics such as Settling Time (ST), Rise Time (RT), and Percentage Overshoot (P.O.%). Different objective functions, such as the Integral Absolute Error (IAE), Integral Squared Error (ISE), Integral Time Absolute Error (ITAE), and Mean Squared Error (MSE), are used with TSA to determine the best PID gain parameters for controlling the BLDCM using each objective function separately. The main novelty of this work lies in the integration of fusion-based execution criteria with the TSA's effective search ability to achieve high-quality, superior, and robust PID gains for the BLDCM speed control process. The performance of the PID-TSA for controlling the speed of the BLDCM surpasses that of other controllers by optimizing its control and confirming the superiority of the proposed system under different applied load conditions. The proposed controller uses a fusion objective function (FOF) to achieve a fast response with zero P.O.%, ST equal to 0.027 s, and a RT reach to 0.01 s using single run, in addition, average with standard deviation values of (RT = 3.96414E-05 ± 2.85417E-05 s and ST = 0.002346233 ± 0.000439398 s) for 30 runs.