Design of a Fuzzy Logic Based Control System for BLDC Motor Speed and Cooling Fan Using Error and Error Change

Abstract

This study presents the design and simulation-based evaluation of a fuzzy logic controller for a 48 V, 10 kW BLDC motor system integrating speed control and thermal management through a cooling fan. The controller employs four input variables, namely RPM error, change in RPM error, temperature error, and change in temperature error, within an 81-rule fuzzy inference system using triangular membership functions. Minimum-based rule evaluation, maximum aggregation, and weighted-average defuzzification are applied to generate PWM control signals. An interactive GUI-based simulation platform incorporating visualization, data logging, and automated testing was developed to systematically evaluate the complete fuzzy rule base. Simulation results from all 81 fuzzy-rule combinations show that the motor PWM adapts to variations in speed error, while the fan PWM is prioritized under elevated temperature conditions to enhance thermal safety. Overlapping membership functions produce smooth intermediate PWM outputs, whereas the discrete PWM-to-RPM mapping results in stepwise speed variations. Overall, the proposed controller demonstrates stable and adaptive speed and thermal control behavior in simulation and provides a practical foundation for future real-time hardware implementation and quantitative experimental validation.