PID controller design optimized using genetic algorithm for speed control of a BLDC motor (#2154)

Authors

Toribio Cerna, Cristhian Jeferson

Yura Donaires, Nelson

Sanchez Penadillo, Edward Russel

Mendoza Acosta, Alert

Abstract

Brushless DC (BLDC) motors have become essential in industries like automotive, robotics, and consumer electronics due to their efficiency and reliability. However, controlling key parameters such as angular velocity and torque remains a significant challenge, particularly in high-precision applications. This study proposes optimizing PID controller parameters using genetic algorithms to enhance the angular velocity control of BLDC motors. Following the VDI 2206 methodology, the development process was divided into five stages: data collection, controller design, algorithm implementation, system integration, and validation. Experimental results demonstrate a 56.25% reduction in steady-state error and improved dynamic response compared to the Ziegler-Nichols method. These findings highlight the potential of combining genetic algorithms with control systems to improve motor performance in real-world scenarios, laying the groundwork for future advancements in industrial motor control.