Speed Control of BLDC Quadrotor Motor Using an ANN Trained with PID Controller Data Optimized by PSO Algorithm

In this study, we propose a brushless direct current (BLDC) motor speed controller for a quadrotor. These motors are commonly used in quadrotors due to their high power-to-weight ratio and precise control capabilities, which allow for independent speed adjustment of each motor to control the quadrotor's rotation direction, track trajectories, and execute necessary flight maneuvers. The main objective of this study is to regulate the speed of the BLDC motor to achieve optimal response performance using artificial neural networks (ANN). First, we tuned the gain values of the classical PID controller using the particle swarm optimization (PSO) algorithm within a closed loop to achieve optimal parameter values and reduce error for stability during the tracking of the desired speed signal. Subsequently, data regarding the input and output values of the PID controller were collected and stored within the MATLAB workspace. Next, we created a new controller trained with this data using an artificial neural network (ANN) and implemented it in the BLDC motor model in SIMULINK-MATLAB. Finally, the results demonstrate that the ANN-trained controller effectively controls the required motor speed.