Intelligent Control of BLDC Motors Using Adaptive PID and ANN Techniques

Brushless Direct Current (BLDC) motors are extensively used in modern applications such as electric vehicles, robotics, and industrial automation due to their high efficiency, precision control, and low maintenance. However, achieving consistent and accurate speed regulation under varying operating conditions remains a challenge with traditional fixed-gain PID controllers. This study proposes an intelligent control framework that integrates Adaptive PID (APID) and Artificial Neural Network (ANN) based control techniques for BLDC motor speed regulation. The APID controller employs a hybrid strategy combining Model Reference Adaptive Control (MRAC) and Self-Tuning Control (STC) to dynamically tune the controller gains based on real-time system parameters and reference tracking. Additionally, an ANN-based controller is implemented to learn and predict optimal control actions based on motor dynamics. A comparative simulation study using MATLAB/Simulink evaluates the performance of four control strategies: traditional PID, adaptive PID, ANN-based control, and a baseline without PID. Key performance metrics were analyzed. The results demonstrate that the APID controller significantly outperforms the traditional PID, achieving the fastest settling time and improved system stability under dynamic load variations. The ANN controller also delivers superior performance compared to conventional PID, but slightly lags behind APID in responsiveness. These findings suggest that adaptive control and intelligent learning-based strategies offer robust, efficient, and scalable solutions for advanced BLDC motor control systems.