same as the ppt i am downloading

1. Control of BLDC Motor Speed using
PID Controller
• Your Name

2. Introduction to Electric Motors
• Electric motors convert electrical to
mechanical energy.
• Used widely in industrial and consumer
applications.
• Types: AC motors and DC motors.
• Green technology prefers efficient motors like
BLDC.

3. What is a BLDC Motor?
• Brushless DC Motor (BLDC) - a type of
synchronous motor.
• No brushes; uses electronic commutation.
• High efficiency, low maintenance.

4. Applications of BLDC Motors
• Electric Vehicles (EVs)
• Drones and UAVs
• Air Conditioners and HVAC Systems
• Robotics and Industrial Automation

5. Need for Speed Control
• Many applications require precise speed
regulation.
• Maintaining constant speed under varying
load is critical.
• Speed controllers help achieve desired
performance.

6. Speed Control Techniques
• Open-loop control: no feedback, less accurate.
• Closed-loop control: uses feedback for
accuracy.
• BLDC typically uses closed-loop with sensors.

7. Introduction to PID Controller
• PID = Proportional + Integral + Derivative
control.
• Equation: U(t) = Kp*e(t) + Ki∫e(t)dt +
Kd*de(t)/dt.
• Widely used in industrial automation.

8. PID for BLDC Motor Control
• PID helps maintain constant speed despite
load changes.
• Provides stable, fast, and accurate response.
• Used in MATLAB/Simulink simulations.

9. BLDC Motor Control Block Diagram
• Includes PID controller, inverter, motor,
sensors.
• External loop for speed control, internal for
current.
• Uses feedback to maintain set speed.

10. Back EMF and Hall Sensors
• Back EMF: voltage generated by motor
rotation.
• Used to determine rotor position.
• Hall sensors provide phase commutation
signals.

11. MATLAB Modeling Setup
• MATLAB/Simulink used to simulate the
system.
• Includes PID, motor model, inverter, sensors.
• Output observed using scopes.

12. PID Parameter Tuning
• Tuning values used: Kp=100, Ki=0.5, Kd=500.
• Fine-tuning improves system stability and
response.
• Balance between speed, overshoot, and
stability.

13. Simulation Result: Speed Response
• Motor reaches 2500 RPM in ~0.018 seconds.
• Minimal overshoot and stable operation after
settling.

14. Simulation Result: Torque
Response
• Torque stabilizes after ~0.030 seconds.
• Smooth transition with negligible
disturbances.

15. Stator Current in 3-Phase
• Current stabilizes along with torque.
• Waveforms show balanced three-phase
operation.

16. 3-Phase Back EMF
• Back EMF stabilizes at ±24V after 0.030 sec.
• Indicates steady motor operation post-startup.

17. Hall Sensor Signal
• Provides rotor position feedback for switching.
• Essential for synchronized motor
commutation.

18. PID Controller Output
• Settles quickly (~0.03 sec) with minimal
undershoot.
• Demonstrates strong transient and steady
performance.

19. Comparison: PI, PID, and Fuzzy
Logic Controllers
• PID: best settling time, lowest
overshoot/undershoot.
• PI: better rise time but more overshoot.
• Fuzzy: good alternative, but PID more
consistent.

20. Conclusion
• PID control offers best performance for BLDC
motors.
• Efficient, stable, and widely applicable
solution.
• Simulation validates real-world feasibility.

21. References
• List of cited works and journals from the
paper.

22. Acknowledgements
• Authors, mentors, and supporting institutions.
• MATLAB tools and simulation environment
used.