FUZZY BASED PID FOR CALCINER TEMPERATURE CONTROL · 2018. 5. 6. · space models use a small number...
Transcript of FUZZY BASED PID FOR CALCINER TEMPERATURE CONTROL · 2018. 5. 6. · space models use a small number...
Abstract— The Calciner Unit plays an important role in the
modern cement industries as it is used for preheating the raw
materials like limestone which are fed into the kiln. The
mathematical model of the calciner unit is designed using
System Identification technique for the real time data obtained
from the plant. A conventional PID controller has been
designed to control the temperature of the calciner unit. The
parameter of PID controller is tuned using Ziegler – Nichols
tuning method. In order to achieve optimum controller
parameter a Self Tuning Fuzzy PID controller is developed. The
performance of the calciner unit has improved significantly
compared to conventional PID controller.
I. INTRODUCTION
Calciner temperature control process is one of the
most important processes in cement manufacturing. It is used
to maintain the raw mix texture, size of the mixture and
perfect blending of the raw material to produce more
valuable clinker. Calciner unit is used to preheat the raw mix
sent into the kiln. The product obtained is “clinker”
(cement). Normal temperature of kiln is to be maintained at
800-960 °C and a normal coal feeding is 10-20 t/hr. There
are four basic processes in cement manufacturing. It starts
with quarry where the raw material is extracted and crushed.
Then it will be sent to raw mill wherein the blending process
takes place (raw mix). The resultant from the above process
was sent to the calciner where the raw mix was preheated
and fed into the kiln. The raw mix and fuel was sent into the
kiln. Clinker and exit gases come out. The clinker was sent
to finish mill, after which the size was reduced to obtain the
final product „cement„. The basic schematic diagram of
cement manufacturing plant is shown in Fig.1.1.
Figure 1.1: Schematic diagram of cement manufacturing
plant
II. IDENTIFICATION OF SYSTEM
A. ANALYZING AND PROCESSING DATA
When preparing data for identifying models, it was
mandatory to specify information such as input-output
channel names, sampling time (10s). The toolbox helps to
attach this information to the data, which facilitates
visualization of data, domain conversion, and various
preprocessing tasks. Measured data often has offsets, slow
drifts, outliers, missing values, and other anomalies. The
toolbox removes such anomalies by performing operations
such as de-trending, filtering, resampling, and reconstruction
of missing data. The toolbox can analyze the suitability of
data for identification and provide diagnostics on the
persistence of excitation, existence of feedback loops, and
presence of nonlinearities. The toolbox estimates the impulse
and frequency responses of the system directly from
measured data. Using these responses, system characteristics,
such as dominant time constants, input delays, and resonant
frequencies can be analyzed. These characteristics can also
be used to configure the parametric models during
estimation.
FUZZY BASED PID FOR CALCINER TEMPERATURE
CONTROL
Mrs.Z.Brijet *1
, M.B.Sri Padmadarshan*2
, S.Vigneshwaran*3
, P.B.Mohankrishna*4
*1 Assistant Professor – III, Department of Electronics and Instrumentation Engineering, Velammal
Engineering College, „Velammal New-Gen Park, Ambattur-Red Hills Road, Chennai – 600066,
India *[email protected]
*2,3,4 4th year Bachelor‟s degree, Department of Electronics and Instrumentation Engineering,
Velammal Engineering College, „Velammal New-Gen Park, Ambattur-Red Hills Road, Chennai –
600066, India *2
International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 14563-14570ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu
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B. ESTIMATING MODEL PARAMETERS
Parametric models, such as transfer functions or state-
space models use a small number of parameters to capture
system dynamics. System Identification Toolbox estimates
model parameters and their uncertainties from time-response
and frequency-response data. These models can be analyzed
using time-response and frequency-response plots, such as
step, impulse, bode plots, and pole-zero maps.
C. VALIDATING RESULTS
System Identification Toolbox helps to validate the
accuracy of identified models using independent sets of
measured data from a real system. For a given set of input
data, the toolbox computes the output of the identified model
and lets to compare that output with the measured output
from a real system. One can also view the prediction error
and produce time-response and frequency-response plots
with confidence bounds to visualize the effect of parameter
uncertainties on model responses.
Figure 2.1: Shows the process of selecting the range for
validation and estimation of data.
D. LINEAR MODEL IDENTIFICATION
System Identification Toolbox lets to estimate
multi-input, multi-output continuous or discrete-time transfer
functions with a specified number of poles and zeros. One
can specify the transport delay or let the toolbox determine it
automatically. In this work, transfer function model was used
for system identification.
E. ESTIMATING TRANSFER FUNCTION MODEL
Estimate continuous-time and discrete-time transfer
functions and low-order process models. Use the estimate
models for analysis and control design. Polynomial and
state-space models can be identified using estimation
routines provided in the toolbox. These routines include
autoregressive models (ARX, ARMAX), Box-Jenkins
models, Output-Error models, and state-space
parameterizations. Estimation techniques include maximum
likelihood, prediction-error minimization schemes, and
subspace methods based on N4SID, CVA, and MOESP
algorithms. A model of the noise affecting the observed
system can also be estimated. Figure 2.2 depicts the process
of obtaining the transfer function model.
Figure 2.2: Obtaining transfer function model
F. ESTIMATING STATE-SPACE MODEL
A state space model is commonly used for
representing a linear time invariant system. It describes a
system with a set of first order difference equation using
inputs, outputs and state variables. In the absence of the
equation, a model of desired order can be estimated for
measured input, output data. The model was widely used in
modern control application for designing controllers and
analyzing system performance in the time domain and
frequency domain. The models can be applied to nonlinear
system or system with a non-zero initial condition.
Figure 2.3: Obtaining state space model
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III. DESIGN OF PID CONTROLLER FOR
CALCINER
A. PID CONTROLLER:
P-I-D controller has the optimum control dynamics
including steady state error, fast response, less oscillations
and higher stability. The necessity of using a derivative gain
component in addition to the P-I-D controller is to eliminate
the overshoot and the oscillations occurring in the output
response of the system. One of the main advantages of the P-
I-D controller was that it can be used with higher order
processes including more than single energy storage.
From a mathematical viewpoint, the PID control works to
reduce the error e(t) to zero, where e(t) was the difference
between output response and the set point.
The control response u(t) is given by:
u(t)=Kpe(t)+Ki∫e(t)dt+Kd de(t)/dt
where kp, ki, kd are scale factors for the proportional,
integral and differential terms respectively.
B. ZIEGLER – NICHOLS TUNING METHOD:
The basic steps in Z-M method are
1. The value of Kd and Ki were set to zero.
2. The value of Kp was slowly increased such the sustained
oscillation occurs (constant amplitude and periodic).
3. The value of Kp at which sustained oscillation occurs was
ultimate gain Ku and the period of oscillation was ultimate
period Pu.
From the calculated value of Ku and Pu, the parameters of
PID controller were calculated using the formula:
The table 3.1 shows the PID controller parameter tuned
using Ziegler – Nichols method.
Table 3.1: PID controller tuning parameters
Control type Kp Ki Kd
PID 0.6*200=120 2/0.2=10 0.2/8=0.025
IV. DESIGN OF FUZZY CONTROLLER
Figure 4.1: General block diagram of fuzzy logic controller
A. FUZZY INFERENCE SYSTEM
A Fuzzy inference system (FIS) was a system that
uses fuzzy set theory to map inputs to outputs. There are two
types of FIS .They are mamdani and Takagi sugeno FIS. In
this project there are two inputs and three outputs. Therefore,
mamdani type FIS was used in this project.
i. MAMDANI FIS
Mamdani FIS is widely accepted since it
can be applied for both MIMO, MISO systems whereas
sugeno can be implemented only for MISO systems. In
mamdani, the membership functions can be chosen even for
outputs whereas it was not possible in sugeno type. Hence
mamdani FIS was used for our project.
ii. DEVELOPMENT OF MAMDANI TYPE FIS
Calciner temperature in the cement
manufacturing process was developed using mamdani fuzzy
model. It consists of two inputs and three outputs. First input
was error. Second input was rate of change of error. The
three outputs were Kp, Ki and Kd (i.e. controller gains).
Table 4.1:Rule table of fuzzy controller
B. MAMDANI FIS IMPLEMENTATION FOR
CALCINER TEMPERATURE CONTROL
Figure 4.2: Fuzzy logic toolbox
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Figure 4.3: Membership function of inputs
Figure 4.4: Membership function of outputs
Figure 4.5: Rule viewer of mamdani FIS
Figure 4.6: Surface viewer of mamdani FIS
V. IMPLEMENTATION OF FUZZY PID
CONTROLLER
A. STRUCTURE OF FUZZY-PID CONTROLLER
Self tuning fuzzy-PID controller means that the three
parameters Kp, Ki, and Kd of PID controller are tuned by
using fuzzy tuner. The coefficients of the conventional PID
controller are not often properly tuned for the non-linear
plant with unpredictable parameter variations .Hence, it was
necessary to automatically tune the PID parameters.
Figure 5.1: Structure of the self tuning fuzzy-PID controller
The error and the derivative of its error are sent to the fuzzy
controller. The PID parameter Kp, Ki and Kd is calculated
according to the rules in the fuzzy controller, at the same
time, Kp was also refined by P controller which was the
immune PID controller, so the Kp, Ki and Kd can be
continuous updated according to error e(t) and its derivative
de/dt.
VI. SIMULATION RESULTS AND DISCUSSION
A. SERVO RESPONSE OF PID AND FUZZY PID
CONTROLLER
Simulation studies are carried out to demonstrate the
tracking capability of tuned PID controller and fuzzy PID
controller. The performance of process for tuned PID and
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fuzzy PID were shown in figures 6.3 and 6.4 respectively.
From the response, it was observed that the calciner
temperature follow the given set points and the servo
response of the PID and fuzzy PID were compared in the
table 8.1.
Fig 6.1: Servo response of the PID controller
Fig 6.2: Servo response of the fuzzy PID controller
Table 6.1: Comparison of performance indices of PID and
FUZZY PID tuned controller for servo response
CALCINER
TEMPERATURE
CONTROL USING
ISE IAE ITAE
PID CONTROLLER 1.559
e^(+05)
416.9 3975
FUZZY CONTROLLER 1.045
e^(+05)
279.3 2138
From the responses, it was observed that the performance
criterion such as ISE, IAE and ITAE of Fuzzy PID controller
was better compared to conventional PID controller. It was
also observed that fuzzy PID controller settles quickly than
PID controller response.
B. SERVO WITH REGULATORY RESPONSE OF PID
AND FUZZY PID CONTROLLER
Fig 6.5: Servo with regulatory response of the PID controller
Fig 6.6: Servo with regulatory response of the fuzzy PID
controller
Table 6.2: Comparison of performance indices of PID and
FUZZY PID controller for servo with regulatory response
CALCINER
TEMPERATURE
CONTROL USING
ISE IAE ITAE
PID CONTROLLER 1.605e^(+05) 622.8 9293
FUZZY CONTROLLER 1.294 e^(+05) 410.9 4294
VII. REAL TIME IMPLEMENTATION –
CEMULATOR
Contrary to most cement process simulators,
ECS/CEMULATOR was developed on a full functional
control systems platform enabling the complete set of
functions and features of a modern control system
environment for the users. Having a skilled team of operators
plays a crucial role in beneficial and safe operation of
industrial plants. Especially in the cement industry, with the
significant high cost of investment, practical knowledge and
experience of plant operation have a direct effect on
production economy. Insufficient insight in process
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dynamics and interactions, high stress factors in real time
operation conditions, and lack of adequate experience in
utilizing the existing control system are typical reasons for
incorrect operator actions. The consequences of this may
result in low production quality, production interrupts, and
equipment damage, in worst case risk on human safety. The
increasing demand on production sustainability in the recent
years has resulted in requirements of which the degree of
fulfillment is effected by the level of skills of plant operators
and engineers.
A. REAL TIME RESPONSE OF THE PID
CONTROLLER
Figure 7.1: Response of PID controller in real time
B. REAL TIME RESPONSE OF FUZZY PID
CONTROLLER
Figure 7.2: Response of Fuzzy PID controller in real time
Comparison of performance indices of PID and FUZZY PID
controller for the real time response is shown in Fig. 7.1 and
7.2.
Table 7.1:
CALCINER TEMPERATURE CONTROL
USING
ISE
PID CONTROLLER
18.4
FUZZY CONTROLLER 16.4
From the table 7.1 it has been observed that Integral Square
Error (ISE) value of fuzzy PID controller is reduced as
compared to PID controller.
VIII. CONCLUSION
The main aim of the project was to control the
calciner temperature and to obtain a good quality clinker.
The transfer function model of calciner for the process has
been derived using system identification tool. The simulink
model of calciner has been developed in MATLAB using
real time steady state values of Turkey power plant. The
open loop response of the process where observed and the
interaction effect has been studied. The parameters for PID
were obtained using Ziegler - Nichols tuning. The fuzzy
rules were written using FAM table and the rules are inserted
in the FIS using mamdani method which is used to tune the
PID. Thus Fuzzy PID controller was implemented and then
optimized values were obtained. It is observed that the
performance criteria namely the ISE, IAE, ITAE, and
settling time in Fuzzy PID controller is better than the PID
controller. Also from the responses, it has been observed that
the proposed method has better tracking and faster settling
time.
IX. APPENDIX
DATA FROM REAL TIME CALCINER UNIT
S.N
O
CALCINER
TEMPERATU
RE
CALCINE
R COAL
FEED
KILN
TOTAL
FEED
1 894.7916 9.6501 588.4775
2 894.7916 9.6401 589.4781
3 896.5278 9.6359 585.4742
4 898.9583 9.6276 588.4867
5 901.3889 9.6184 594.3333
6 904.1666 9.6096 590.6599
7 902.7778 9.6029 588.5881
8 900.6944 9.6033 590.9871
9 899.3055 9.6079 591.7212
10 901.3889 9.6074 589.3926
11 903.1249 9.6 585.8295
12 901.7361 9.5952 584.7019
13 900.6944 9.5972 586.1656
14 901.0416 9.5997 590.9084
15 903.1249 9.5979 590.3184
16 906.2499 9.5892 591.2415
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17 904.8611 9.5817 590.2633
18 903.1249 9.5822 591.3748
19 902.7778 9.5847 591.8418
20 906.9444 9.5828 585.3685
REFERENCES
[1] Ashwani Kharola “A PID BASED ANFIS AND FUZZY
CONTROL OF INVERTED PENDULUM ON INCLINED
PLANE”, International journal on smart sensing and
intelligent systems.
[2] Ahmed M.Salch and Sohair F.Rezeta “FUZZY LOGIC
CONTROL OF AIR CONDITIONING SYSTEM IN
RESIDENTIAL BUILDINGS”.
[3] Elvira Lakovic and Damir Lotinac “AIRCRAFT LANDING
CONTROL USING FUZZY LOGIC AND NEURAL
NETWORKS”
[4] Gaddam Mallesham and Akula rajani “AUTOMATIC
TUNING OF PID CONTROLLER USING FUZZY
LOGIC”, VIIIth international conference on development
and application system.
[5] Jan Jantzen “TUNING OF FUZZY PID CONTROLLER”
,IEEE journal 2009
[6] P.T.Rajeena Mol , B.Chinthamani, K.P.Kamini and
K.R.Sugashini “INTELLIGENT HOME HEATING
SYSTEM” ,International Journal of Research in
Engineering and Technology
[7] Salim Jyothi ohri “FUZZY BASED PID CONTROLLER
FOR SPEED CONTROL OF DC MOTOR” ,National
institute of Technology.
[8] Sankata B. Prusty, Umesh C. Pati and Kamalakanta
Mahapatra “IMPLEMENTATION OF FUZZY-PID
CONTROLLER TO LIQUID LEVEL SYSTEM USING
LABVIEW”.
[9] Somayeh Abdolzadeh and Seyed Mohammad Ali
Mohammadi "IMPLEMENTATION OF ADAPTIVE
FUZZYCONTROLLER ON THE VARIABLE SPEED
WIND TURBINES IN COMPARISON WITH
CONVENTIONAL METHODS”
[10] S.Subramanian , N. Raghothaman and B. Chellappa
“FUZZY LOGIC CONTROL IN HIGH TEMPERATURE
FURNACE” ,Recent Research in Science and Technology
,2010.
[11] Sudha Hatagar and S.V.Halse “THREE INPUT-ONE
OUTPUT FUZZY LOGIC CONTROL OF WASHING
MACHINE”, International journal of scientific research
and Technology
[12] Tushar Upalanchiwar and A.V.Sakhare “DESIGN AND
IMPLEMENTATION OF FUZZY-PID CONTROLLER
USING MATLAB”,International journal of research in
computer and communication technologies.
[13] ZHANG Shengyi “STUDY OF FUZZY PID
CONTROLLER IN MATLAB FOR TWO PHASE
HYBRID STEPPING”, IInd international conference on
system engineering and modeling.
[14] Zulfatman and M. F. Rahmat “APPLICATION OF SELF-
TUNING FUZZY PID CONTROLLER ON INDUSTRIAL
HYDRAULIC ACTUATOR USING SYSTEM
IDENTIFICATION APPROACH” ,International journal on
smart sensing and intelligent systems, vol. 2, no. 2, june
2009.
[15] Zuo Yusheng and Xu Qun “COMPARED WITH PID,
FUZZY AND FUZZY PID CONTROLLER”,IEEE journal.
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