International Journal of Research in Computer and ISSN (Online) 2278- 5841
Communication Technology, Vol 3, Issue 3, March- 2014 ISSN (Print) 2320- 5156
Design and implementation of the fuzzy PID controller using
MATLAB/SIMULINK model
Mr. Tushar Upalanchiwar , Prof.A.V.Sakhare
Computer Science & Engineering Department, G.H Raisoni College Of Engineering.
Email Id :- tushuplanchiwar@gmail.com
ABSTRACT
In many industries, various types of motion
control system used to control various applications.
These motion control systems are nothing but the DC
Motors. DC motors have high efficiency, high torque
and low volume. This paper proposed PID controller
with fuzzy technology. i.e. fuzzy PID controller, here
we are analyze the performance of the conventional
PID controller using the MATLAB/SIMULINK
model.
Keywords: PID, Fuzzy PID controller
1.
INTRODUCTION
Now a day’s DC motors are widely used in
industrial applications, the speed of DC motors can
be controlled by various driver circuits. In which the
drives application mainly involves complex process
such as modeling, control, simulation and parameters
tuning etc.
An expert knowledge is required for tuning
the controller’s parameter to get the optimal
performances. However conventional PID controller
algorithm is simple stable easy adjustment and high
reliability but most of the industrial process with
different degrees of non linearity and parameters
variability and uncertainty of the mathematical model
of the system.
Tuning the parameters of the conventional
PID controller is very difficult, poor robustness, so
therefore it’s difficult to achieve optimal state under
field condition in the actual production.
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For all the problems with the conventional
PID controller, fuzzy is the better way to control
systems. Fuzzy PID controller method is better
method of controlling to the complex and unclear
model systems, it can give simple and effective
control, play fuzzy control robustness, good dynamic
response, rising time, overstrike characteristics.
FLC (fuzzy logic control ) control has
proven effective for complex non linear and
imprecisely defined process for which standard
model based control techniques are impractical.
Fuzzy logic deals with the problems that have
vagueness uncertainty and membership function
between 0 to 1. i.e. if the reliable expert knowledge is
not available or if control system is too complex to
derive the required decision rules, then some efforts
have been made to solve these problems and
simplicity the task of tuning parameters and
developing rules for the controller.
2.
PROBLEM DEFINATION
Unfortunately, most existing conventional PID
controller fails where industrial process having
degrees of non linearity and parameters variability
and uncertainty of the mathematical model of the
system, however conventional PID controller
algorithm is simple stable easy adjustment and high
reliability. Tuning parameters of such systems also
difficult.
3.
OBJECTIVES
Fuzzy PID controller method is better method of
controlling to the complex and unclear model
systems. Fuzzy rules can be evaluated from the
human experience and knowledge about the system.
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International Journal of Research in Computer and ISSN (Online) 2278- 5841
Communication Technology, Vol 3, Issue 3, March- 2014 ISSN (Print) 2320- 5156
The objective is to set fuzzy rules which makes PID
controller reliable for the industrial process having
different degrees of non linearity’s & variation in
parameters.
4. SYSTEM ARCHITECTURE
The typical FIS (fuzzy inference system) inputs
are the signals of error (e(k)) and change of error
(e(k)-e(k-1)). The FIS output is the control action
inferred from the fuzzy rules. Fuzzy Logic
Toolbox™ provides commands and GUI tools to
design a FIS for a desired control surface.
In design of a nonlinear fuzzy PID controller for
a DC motor in Simulink, The plant is a single-input
single-output system in discrete time and our design
goal is simply to achieve good reference tracking
performance. The fuzzy controller in this example is
in the feedback loop and computes PID-like actions
through fuzzy inference. The fuzzy PID controller
uses a parallel structure. It is a combination of fuzzy
PI control and fuzzy PD control.
Fig. implementation of fuzzy PID using matlab
simulink model.
We use the change of measurement -(y(k)-y(k1)), instead of change of error e(k)-e(k-1), as the
second input signal to FIS to prevent the step change
in reference signal from directly triggering the
derivative action. Two gain blocks, GCE and GCU in
the feed forward path from r to u, are used to ensure
that the error signal e is used in proportional action
when the fuzzy PID controller is linear.
5.
METHODOLOGY
Designing a fuzzy PID controller involves
configuring the fuzzy inference system and setting
the four scaling factors: GE, GCE, GCU and GU.
In this example we followed the following steps,
1.
Fig- implementation of discrete PID controller.
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Design a conventional linear PID
controller
The conventional PID controller is a discrete
time PID controller with Backward Euler numerical
integration method used in both the integral and
derivative actions. The controller gains are Kp, Ki
and Kd. The controller is implemented in Simulink as
below,
PID controller gains can be tuned either
manually or using tuning formulas. In this example,
Page 370
International Journal of Research in Computer and ISSN (Online) 2278- 5841
Communication Technology, Vol 3, Issue 3, March- 2014 ISSN (Print) 2320- 5156
we use the pidtune command from Control System
Toolbox to obtain an initial PID design
Ts*z
z-1
C= Kp + Ki * ------ + Kd * -----z-1
Ts*z
with Kp = 30, Ki = 28.6, Kd = 6.9, Ts = 0.1
2. Design an equivalent linear fuzzy PID
controller
By configuring the FIS and selecting four
scaling factors, we obtain a linear fuzzy PID control
that reproduces the exact control performance as the
conventional PID controller does.
First, configure the fuzzy inference system
so that it produces a linear control surface from
inputs E and CE to output u.
The FIS settings summarized below are
based on design choices described
in:
 Use Mamdani style fuzzy inference system.
 Use algebraic product for AND connective.
 The ranges of both inputs are normalized to
[-10 10].
 The input sets are triangular and cross
neighbor sets at membership value of 0.5.
 The output range is [-20 20].
 Use singletons as output, determined by the
sum of the peak positions of the input sets.
 Use the center of gravity method (COG) for
defuzzification.
By using this equations, we can implement fuzzy
PID controller by comparison to the conventional
PID controller.
6.
RESULTS AND DISCUSSION
Fig- simulation results of conventional PID controller
for speed controlling of dc motor
Next, we determine scaling factors GE, GCE,
GCU and GU from the Kp, Ki, Kd gains used by the
conventional PID controller. By comparing the
expressions of the traditional PID and the linear
fuzzy PID, the variables are related as:
 Kp = GCU * GCE + GU * GE
 Ki = GCU * GE
 Kd = GU * GCE
Assume the maximum reference step is 1,
whereby the maximum error e is 1. Since the
input range of E is [-10 10], we first fix GE at
10. GCE, GCU and GCU are then solved from
the above equations.
 GE = 10;
 GCE = GE*(Kp-sqrt(Kp^2-4*Ki*Kd))/2/Ki;
 GCU = Ki/GE;
 GU = Kd/GCE;
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Fig- simulation results of fuzzy PID controller
for speed controlling of DC motor
The comparative assessment based on the speed
controlling of the DC motor between conventional
PID controller and fuzzy PID controller shows that,
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International Journal of Research in Computer and ISSN (Online) 2278- 5841
Communication Technology, Vol 3, Issue 3, March- 2014 ISSN (Print) 2320- 5156
the performance of fuzzy PID controller is better than
conventional PID controller in controlling speed, and
time response of specifications.
7.
CONCLUSION
In spite of the fact that PID controllers designed
by the conventional method
give a good
performance, they create poor robustness and high
exceeding. It is obvious that in case of few parameter
changes of the plant led to decline of the performance
of the conventional PID controller drastically.
Thus, it is not enough to control process dynamics
swimmingly although it is a good start to tune PID
parameters. Therefore, in
this paper to overcome problems of conventional
tuning methods, fuzzy base PID approach were
searched.
8.
[5] Venugopal P, “design of tuning methods of pid
controller using fuzzy logic” International Journal of
Emerging trends in Engineering and Development
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[6] C.C.Lee, “Fuzzy logic in Control Systems: Fuzzy
Logic controller –Part I”,IEEE Transactions on
systems,Man,and Cybernetics ,Vol.20,No.2,pp.404418,1990
[7]weinjing huang, yuan zhang, xiowei gou and lei
sun “ simulation and realization of servo rocket
system based on interval fuzzy PID control.” 2012
international conference on electrical and computer
engineering advances in biomedical engineering ,
vol.11
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