This is based on mid term progress report so there is only Pid controller response analysis.

1. SPEED CONTROL OF DC
MOTOR USING PID FUZZY
CONTROLLER
Presented By:
Anil Acharya (BEL/071/202)
Bikash Kumar Pal (BEL/071/209)
Binod Kafle (BEL/071/211)
Madan Rimal (BEL/071/220)

2. Presentation Overview
 Introduction
 Project Objectives
 PID Controller
 DC motor Parameter and modeling
 Ziegler-Nichols tuning method
 PID Auto Tuning using MATLAB in-built function
 Comparison Between Auto-tuning and Z-N tuning method
 Limitation of PID controller
 Work Completed
 Work remaining
 Conclusion
 References

3. Introduction
 DC motors used in many applications such as rolling mills, electric trains, electric
vehicles, electric cranes and robotic manipulators which require speed controller to
perform their tasks.
 Conventionally speed of DC motor can be controlled by :
a. Field Resistance control method
b. Armature Voltage control method
c. Armature Resistance control method
 For precise and better performance, Proportional Integral Derivative controller
(PID) is widely used in industrial controller due to its control loop feedback
mechanism.
 It has simple structure and stable characteristics.
 PID parameters are calculated by techniques such as self-tuning method, Zeigler-
Nichols tuning method.

4. Project Objectives
 To design a conventional PID controller for speed control of DC motor.
 To design a Fuzzy logic controller as another type of controller that can be used to
control speed of DC motor.
 To analyze the performance comparison between conventional PID and Fuzzy logic
controller in order to control speed of the DC motor based on MATLAB simulation.

5. PID Controller
Figure: PID controller with plant

6. Continued…
 PID controller consists of three parameters:
a. Propotional Gain(Kp): Larger values typically mean faster response. An
excessively large proportional gain will lead to process instability and oscillation.
b. Integral Gain(Ki): Larger values imply steady state errors are eliminated
more quickly.
c. Derivative Gain(Kd): Larger values decrease overshoot but slows down
transient response and may lead to instability due to signal noise amplification
in the differentiation of the error.
 Thus, the design phase of PID controller, there is a crucial and challenging task for
setting Kp, Ki, and Kd for system success.
 PID parameter for plant (DC motor) can be evaluated by either Ziegler-Nichols
empirical formula or using self tuning in MATLAB in-built function.

7. DC motor Parameter and modeling
Parameter Symbol Value
Moment of Inertia of Rotor J 0.1 Kgm2
Damping constant B 0.008 Nm/rad/s
Armature resistance R 0.5 ohm
Armature inductance L 0.02 H
Back emf constant Kb 1.25 V/rad/s
Motor Torque constant KT 1 Nm/A
Table: Parameters of DC motor
Plant TF =
𝑤(𝑠)
𝑣(𝑠)
=
𝐾𝑡
𝑅+𝐿𝑠 𝐽𝑠+𝐵 +𝐾𝑏𝐾𝑡
=
500
𝑠2+25.08𝑠+627

8. Ziegler-Nichols tuning method
 It is an empirical formula based on experimental result which gives better tuned
PID parameters.
 It is based on the trial and error procedure of changing the proportional gain (kp).
kp is increased from small value till the point at which the system goes to unstable.
 Z-N algorithm deals with ultimate time and ultimate gain, so it is necessary to find
the response of the system without any controller.
 Z-N empirical formula for PID tuning:
 where T is a time from delay time to rise time and L is a time from origin to delay
time.
Controller Kp Ki Kd
P T/L Zero Zero
PI 0.9(T/L) L/0.3 zero
PID 1.2(T/L) 2L 0.5L

9. Figure: Response of the system without controller
Kp=1.2T/L=2
Ki=2L=0.042
Kd=0.5L=0.0105
PID(s)=2+0.042/s+0.0105s

10. Figure: Simulation Block Diagram of the system

11. Figure: Output Response of PID controller and
without controller
Parameter Without
Controller
PID
Rise Time 0.0665 sec 0.0525 sec
Setting Time 0.3190 sec 0.257 sec
Peak Time 0.1 sec 0.08 sec
Max. %
Overshoot
28.26% 25.81%
Steady Static
Error
55% 38%
Table: PID controller vs without
controller parameters

12. PID Auto Tuning using MATLAB in-built
function
Figure: MATLAB Simulation for Auto Tuning

13. Figure: PID Auto Tuned Response Figure: Tuned vs Block
Response Parameter

14. Comparison Between Auto-tuning and Z-N
tuning method
Parameter Auto-tuning Z-N tuning
Rise Time 0.0678 sec 0.0525 sec
Peak Time 0.12 sec 0.08 sec
Setting Time 0.348 sec 0.25 sec
Max. Percentage Overshoot 5.75 % 25.81 %
Steady Static error Eliminated 38%
Table: Auto-tuning parameter vs Z-N tuning parameter

15. Limitation of PID controller
 The three parameters kp, ki and kd of a conventional PID controller need to be
constantly adjusted in order to achieve better control performance.
 PID controller in MATLAB Simulink is not a pure PID but it is a PID with derivative
filtering.

16. Work Completed
 PID controller parameters are calculated by Z-N method and auto-tuning method.
 Simulation using conventional PID controller with DC motor in MATLAB.
 Comparison between auto-tuning and Zeigler-Nichols tuning method.

17. Work remaining
 To define membership function and fuzzy rules.
 Implementation of fuzzy rule with PID controller in MATLAB simulation.
 To compare fuzzy PID with conventional PID controller.

18. Conclusion
A mathematical model to control the DC motor is developed and the motor is
controlled using conventional PID controller. The simulation results so obtained show
that the PID controller gives high steady state error and setting time, thus slow
response. Hence alternative method like Fuzzy controller is needed for better
performance.

19. References
 K. J. Astrom, T. Hagglund, Automatic Tuning of PID Controllers, Instrument Society
of America, USA, 1998.
 L. Reznik, Fuzzy Controllers, BH, Victoria University of Technology, Melbourne,
Australia, 1997.
 P. Vas, Artificial-Intelligence-Based Electrical Machines and Drives, Oxford
University Press, New York, 1999.
 R. Palm, D. Driankov, H. Hellendoorn, Model Based Fuzzy Control, Springer, Berlin,
1997.
 C.H. Chen, Fuzzy Logic and Neural Network Handbook, McGraw-Hill, United States,
1996.

20. THANK YOU