Speed control enhancement of a brushless direct current motor using transit search optimizer-based proportional integral derivatives controller

Brushless Direct Current (BLDC) motors are capable of achieving accurate speed control tailored to the requirements of various applications, making them suitable for devices that need high-precision motion management, such as robots and medical devices. However, most motors perform poorly as a result of commutation problems where the switching of current is controlled by an electronics speed controller (ESC) thereby causing variation in speed. Linear Quadratic Regulator (LQR) and Proportional Integral Derivatives (PID) controllers have been used to control the speed but with little expected result due to maximum overshoot which leads to system instability. This research aimed to control the Speed of a Brushless DC Motor using a Transit Search Optimizer (TSO) based PID controller. The dynamic mathematical model for the DC motor and PID controller was formulated. Then, the TSO-PID model for DC speed motor control was developed. Simulation of the developed model was done using MATLAB R2021a. The performance evaluation and comparison of the developed model with Simulated Annealing (SA) and Nelder Mead-PID (SA-NM-PID) using rise time, settling time, and percentage overshoot as metrics. The outcome of the results showed that the developed TSO-PID outperformed other conventional methods in terms of rise time, settling time, and percentage overshoot. The application of this work can be considered for domestic and industrial control of drives.