Purpose
The project investigated how PID tuning affects the performance and energy efficiency of BLDC motors under dynamic load conditions inspired by maritime systems such as winches, cranes and active heave compensation.
Solution
I designed and built a physical motor test bench with a BLDC motor, AC load motor, variable frequency drive, PLC, torque sensor, encoder, power supply, motor controller, CAN bus communication and a mechanical shaft/coupling setup.
The test bench was used to simulate constant load, step load and sinusoidal/dynamic load profiles. Measurements included RPM, torque, voltage, current, power, efficiency, temperature, rise time, settling time, overshoot and steady-state error.
Result
The project compared Ziegler-Nichols, manual tuning and Simulink auto-tuning. In simulation, efficiency rose from about 76% with Ziegler-Nichols to about 94% with auto-tuning. Manual tuning gave the best practical balance between performance and efficiency in the physical setup.
This is not an AI project in itself, but it strengthens my AI profile for industrial clients because it shows experience with physical systems, sensors, control, measurement and real technical constraints.