Enhancing Field-Controlled DC Motors with Artificial Intelligence-Infused Fuzzy Logic Controller

Abstract

Servomotors play a pivotal role in a wide array of everyday and industrial applications. Field-controlled DC motors particularly stand out for positioning tasks owing to their advantageous speed-torque characteristics. An optical encoder, integrated with the rotor, provides feedback to a PID controller, which in turn generates corrective signals for precise motor positioning. To enhance response speed and minimize hunting, the PID controller incorporates fuzzy logic programming. This paper introduces a novel optimization design approach utilizing a Performance-Oriented Rule-Based Controller (PDFCS) in conjunction with various PID fuzzy controller design methods to attain specific performance goals. Given the criticality of constructing membership functions in fuzzy controllers, a self-optimized membership functions algorithm is proposed. Accuracy analysis demonstrates that the proposed design method achieves a 2.9-second reduction in rise time, a 2.0-second decrease in settling time, and a 1.9% reduction in overshoot compared to conventional design methods. Furthermore, robustness analysis reveals a 4.0-second improvement in rise time, a 1.7-second enhancement in settling time, and a 0.79% decrease in overshoot. These findings underscore the superior accuracy and robustness of employing the proposed performance model alongside various PID fuzzy controller design methods, compared to relying solely on conventional design approaches.