This presentation discusses the designing and simulation of "Integrated fuzzylogic controller for a Brushless DC Servomotor system" using Matlab simulink

1. Integrated fuzzy
logic controller for a Brushless
DC Servomotor system
Ehab Al Hamayel Abdullah Abo Daqqah
Supervised by
Dr. Mohammad Al Janaideh

2. Outlines
• Abstract
• Problem description.
• The Mathematical Model Of the system.
• The control system.
• Conclusion
Keywords: BLDC: Brushless DC servomotor
FLC: Fuzzy logic controller
IFLC: Integrated fuzzy logic controller

3. Abstract
• The proposed controller systems consist of two -input fuzzy
integrated fuzzy logic controller (IFLC) for rotation speed
control of brushless dc servomotor drive.
• The input for the controller are error e(t), and change in error
(first derivative of error ce(t)) with a single-output.
• The IFLC is designed using FLC and proportional derivation
integral (PID) controllers.

4. • The brushless dc motors (BLDC) are used in various applications
such as defense, industries, robotics, etc. In these applications,
the motor should be precisely controlled to give the desired
performance.
• BLDC motors are relatively easy to control, and considered to
be high performance motor that is capable of providing large
amounts of torque over a vast speed range.
• The Torque vs. speed curve is shown in figure.1
Problem description

5. BLDC Motor Torque-Speed characteristics curve

6. The physical system structure

8. The mathematical model
The voltage equation of motor can be described by
The electromagnetic torque is linearly proportional to the
armature current and the equation is given by:
The load torque
And the motion equation can be expressed as:

10. The transfer function parameters

11. The Block Diagram and the unity feedback
response

13. PID controller design
S-C: Kp=2.124, Ki=29.104 C-C: Kp=0.567, Ki=2845.865

14. PID Time response

15. Increasing Friction and inertia

16. FLC Controller
Cd k= 1*10^(-14), Cd k1= 1000, Sat=[-6000, 6000] (optimal response)

17. FLC Inputs fuzzy sets and Rules using Sugeno-
type reasoning and Matlab Anfis tool box

19. FLC Controller response

21. IFLC Controller
Cd k= 1*10^(-14), Sat=[-1000, 1000], C-S: Kp= 30, KI= 88.107, Kd=1.00
C-C: Kp=1.00, Ki= 350.941 (optimal response)

22. IFLC Time response

25. Conclusion
• The design and development of two-input FLC and IFLC for the
speed control of brushless DC servomotor is being developed.
• Simulation results indicate that the IFLC provides the best
performance in comparison with PID and conventional FLC.
• By several further tuning attempts, we got a better responses
for both CFLC and IFLC as shown below:

26. CFLC Response after several tuning attempts

28. IFLC Response after several tuning attempts

30. • The present system can further be improved by adding one more
input variable (change of change of error cce). With three-input
IFLC will get superior, more robust, faster, flexible, cost-effective,
insensitive to the parameter variations control system , but it will
require more computational time, so it’s necessary to use more
powerful processing unit.

31. THANK YOU