DESIGN COMPARISON OF MAMDAMI AND SUGENO TYPE OF

8/3/2019 DESIGN COMPARISON OF MAMDAMI AND SUGENO TYPE OF

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DESIGN COMPARISON OF MAMDAMI ANDSUGENO TYPE OF FUZZY CONTROLLER

FOR SPEED CONTROL OF D.C SHUNT

MOTOR

SUBMITTED BY-:

MUNISHA KAUSHAL

MTECH 2nd year

10912016



Outline

• Introduction to driving system

• Methods of speed control

• Literature review

•Introduction to fuzzy control

• Fuzzy controller architecture

• Introduction to neural network

• Neuro fuzzy control technique

• Problem formulation

• Software development



Conti..

• Simulation and testing

• Results and discussion

• Conclusion

•Future scope

• References



Introduction to driving system

• Most important component of electromechanical system is

driving unit. Driving unit here constitute D.C shunt typeelectric motor. Various motors are used in industries but due to

load variation there is fluctuation in speed which directly

affect the plant performance. To eliminate fluctuation

controller is to be developed with better accuracy and speedregulation characteristics.



METHOD OF SPEED CONTROL

• FIELD FLUX CONTROL

• ARMATURE RESISTANCE CONTROL(RHEOSTATIC

CONTROL)

•

WARD LEONARD (ARMATURE VOLTAGE CONTROL)



FLUX CONTROL METHOD

• So by decreasing the flux, the speed can be increased and vice

versa hence, the name flux or field control method. The flux of

a DC motor can be changed by changing Ish (Shunt current)

with the help of a shunt field rheostat as can be seen

N α 1/ Ф

FLUX CONTROL METHOD FOR SPEED CONTROL OF D.C SHUNT MOTOR



Literature review

• Noghondhari et al (1994) proposed neuro-fuzzy approach for

sensor less speed control of induction motor drive and

evaluated its performance for wide range of operating

condition. They have evaluated the performance of proposed

method and compared the response with PID controller andfound its performance better than PID controller. Kruse et al

(1994) reported that fuzzy systems are currently being

employed in wide field of industrial and scientific application.

They have optimized some learning process in fuzzy systemwhich construct and optimize such systems automatically.

They have reported survey of various approaches with main

emphasis on neuro-fuzzy approach.



Cont…

• Rahman et al(1994) designed a DC motor controller withNeurofuzzy which provides accurate motor control based onNeurofuzzy algorithm ,reduces time, requires minimumhardware ,low power consumption and solution valid undervarying load condition. They have reported that neural

network and fuzzy logic are highly suitable for modelingnonlinear, time-variant system behavior. They have found thatNeuro-fuzzy allows the designer to concentrate on the systemconfiguration and performance hiding all error prone,cumbersome mathematical manipulation. C. K. Lee et al

(1994) proposed a rule-based fuzzy logic control for thecontrol of a brushless dc motor. The result shows fewer ripplesunder variation in system parameters with fast response times.It is effective in controlling the speed of the motor.



Cont..

• Hirota (1996) has introduced fuzzy logic and fuzzy logic

circuits with several hardware implementations. Then he

has provided the most important application in the field of

processes i.e., fuzzy inference circuits that arecharacterized as one of the fuzzy extension of

combinatorial circuits in two valued Boolean logic.

Futher, in the case of human intelligence oriented fuzzy

application such as fuzzy expert system, he suggested

introduction of multi-stage fuzzy inference, for such

purposes the concept of fuzzy flip flop is given.



Cont…

Leslie smith (2000) has slated various applications of neuralnetworks. He has made comparison with other techniques andreported that neural network cannot do anything that cannot bedone using traditional computing techniques, but they can dosomething which would otherwise be very difficult. Wander G.

da Silva et al (2000) proposed GA’s show potential forobtaining optimized tuning of electric drive speed controllersin the presence of major nonlinearities. They have beenapplied here to optimizing PI speed controller parameters in abrushless dc drive subject to nonlinearities. However, GA’s

can be applied online to the optimization of performance forother operating conditions. The technique will be particularlyuseful when tuning controllers, such as fuzzy controllers,where formal design methods have not been established.



FUZZY CONTROL

• Basic concept underlying Fuzzy logic is that of a linguistic

variable, that is, a variable whose values are words rather than

numbers.



Fuzzy controller architecture

FUZZY CONTROLLER ARCHITECTURE



Introduction to neural network

• Neural networks are composed of simple elements operating in

parallel. These elements are inspired by biological nervous

systems. As in nature, the network function is determined

largely by the connections between elements. You can train a

neural network to perform a particular function by adjustingthe values of the connections (weights) between elements.



Problem formulation

• Identifying the need

Control problems in the industry are dominated by non-

linear, time varying behaviour, different characteristics of

various sensors, multiple control Loops and interaction among

the control loops. Conventional controllers can control the

process to some extent.

As these are fixed gain feedback controllers, they can

neither compensate the parameter variations in the plant nor

adapt changes in the environment. Mathematical modeling of the plants and parameter tuning of the controller have to be

done before implementing the controller. While the intelligent

controllers like Fuzzy have many advantages.



Cont…

Intelligent controllers like Fuzzy have many advantages

• Can deal with uncertainty or unknown variations in plant

parameter and structures more effectively, improving

robustness of the control system.

• Both techniques don’t need the mathematical model of plant’s.

• Highly suitable for modeling non-linear, time-variant system

behavior.

• Offers a high level of automation in the design process,

significantly reducing design time.

• In many applications they are found to have better accuracy

and control then the conventional techniques.



Objective

• Design and development of fuzzy controller for speed control

of D.C shunt motor with the help of data acquired by the

experimental setup to get data of speed variation when load is

changed and cross check simulation results for this problem by

development of Mamdami and Sugeno type of fuzzycontroller.

• Then comparison of simulation results to find the best

controller among them by calculating the average error and

view of control surface.



RESULTSPractical Readings Practical readings Mamdami type Fuzzy

readings

Error Figure no.

Reference speed

Current

Current

Practical values- Fuzzyreadings

692 0.55 0.534 0.55-0.534=0.016 4.2.3.1

638 0.48 0.47 0.48-0.47=0.01 4.2.4.1

568 0.42 0.39 0.42-0.39=0.03 4.2.5.1

542 0.40 0.39 0.40-0.39=0.01 4.2.6.1

510 0.37 0.39 0.37-0.39=-0.02 4.2.7.1

470 0.35 0.31 0.35-0.31=0.04 4.2.8.1

438 0.33 0.327 0.33-0.327=0.003 4.2.9.1

394 0.31 0.31 0.31-0.31=0 4.2.10.1

348 0.29 0.244 0.29-0.244=0.046 4.2.11.1

270 0.26 0.247 0.26-0.247=0.013 4.2.12.1

168 0.23 0.242 0.23-0.242=-0.012 4.2.13.1



Cont.. Practical Readings Practical readings Sugeno type Fuzzy

readings

Error Figure no.

Reference speed Current Current Practical values- Fuzzy

readings

692 0.55 0.55 0.55-0.55=0 4.2.3.2

638 0.48 0.48 0.48-0.48=0 4.2.4.2

568 0.42 0.427 0.42-0.427=-0.007 4.2.5.2

542 0.40 0.42 0.40-0.42=-0.02 4.2.6.2

510 0.37 0.42 0.37-0.42=-0.05 4.2.7.2

470 0.35 0.35 0.35-0.35=0 4.2.8.2

438 0.33 0.35 0.35-0.33=-0.02 4.2.9.2

394 0.31 0.35 0.35-0.31=-0.04 4.2.10.2

348 0.29 0.30 0.29-0.30=-0.01 4.2.11.2

270 0.26 0.30 0.26-0.30=-0.04 4.2.12.2

168 0.23 0.23 0.23-0.23=0 4.2.13.2



Cont..

• Average error of Mamdami type fuzzy controller is found to be:

= 0.016+0.01+0.03+0.01-0.02+0.04+0.003+0+0.046+0.013-

0.012/11=0.01936

• average error of Sugeno type fuzzy controller is found to be:

• Average error=

0.007+0.02+0.05+0.02+0.04+0.01+0.04+0/11=0.181/11=0.017



COMPARISION OF CONTROL SURFACES

SURFACE VIEWER FOR SUGENO

FUZZY CONTROLLER

SURFACE VIEWER FOR

MAMDAMI FUZZY CONTROLLER



CONCLUSION• The main objective of soft computing methodologies like fuzzy logic and neural

networks is to exploit tolerance of imprecision, uncertainty and partial truth

associated with almost every aspect of real world problems. It is obvious that

humans deal with uncertain and imprecise information everyday and is remarkably

consistent in processing such information. Since many years, the question of

comparison between Mamdani fuzzy inference system and Sugeno inference

system has always baffled the minds of several researchers. A good number of

researches have been done independently on their comparison with respect to afew specific applications. One of the major motivations behind this research is to

ascertain which approach is better in general. In this thesis, a detailed survey of

the comparison of the Mamdani and Sugeno methods of fuzzy inference for speed

control of D.C shunt motor has been made.

• First,mamdami fuzzy controller was developed using 8 rules and 5 membership

function and various values of error with respect to practical values were obtainedcorresponding to each input using both mamdami and sugeno FIS. It has lead to

the conclusion that Sugeno fuzzy inference system is better when compared to

Mamdani fuzzy inference system. After the development of of mamdami and

sugeno fuzzy controller surface view of both controllers is evaluated .From the two

figures we concluded that smoothness is obtained with sugeno fuzzy controller

.Smoother the results move across the control surface the better is the controller



RESEARCH METHODLOGY

•

Experiment to be performed to get data of the speed variation when loadis changed and to cross check simulation result for this problem.

The experimental steps performed are given below:

• DC shunt motor-generator set was taken with motor ratings specified as:

•

9KW, 1450-2170 rpm, 230v with mechanical coupling of 10cm betweenmotor generators. Ammeter taken was of 1A DC rating.

• The motor was run without load and speed was noted using tachometer. Itwas coming out to be 1982 rpm with current intake of 1A. This speed isreferred to as reference speed.

• The heater loads were then increased in steps. As the load increased the

motor speed decreased to 1814 rpm. As the flux control method was usedhere to control the speed so rheostat was varied to change the currentintake and bring the motor speed back to 1982 rpm.

• Step 3 was repeated ten more times and each time the heater load isincreased in steps. Following readings are obtained as shown in Table 1.



Experimental Readings

Reference speed-fluctuated speed(rpm) e speed(rpm) Current(A)

1982-1814 168 0.23

1982-1712 270 0.26

1982-1634 348 0.29

1982-1588 394 0.31

1982-1544 438 0.33

1982-1512 470 0.35

1982-1472 510 0.37

1982-1440 542 0.40

1982-1414 568 0.42

1982-1344 638 0.48

1982-1290 692 0.55



Mamdami fis

• In this type of system the crisp input information is first

transformed by a fuzzifier into a set of linguistic variables in

U; then the fuzzy inference engine using the input variables

and the rules in the fuzzy rule base, derives a set of

conclusions in V; the combined areas in V are, by means of adefuzzifier , converted into a crisp number which corresponds

to the output of the system



DEPICTING MAMDANI’S FUZZY INFERENCE METHOD



SUGENO FIS

• Takagi-sukeno-kang method of fuzzy inference was

first introduced in 1985. It is similar to the Mamdani

method in many respects. In fact the first two arts of

the fuzzy inference process, fuzzifying the inputs andapplying the fuzzy operator, are exactly the same. The

main difference between these two is that the output

membership functions are only linear or constant for

sugeno-type fuzzy inference.



DEPICTING SUGENO TYPE FUZZY INFERENCE SYSTEM



COMPARISON OF MAMDAMI AND

SUGENO

• Mamdani FIS is more widely used, particularly for decision support applications,

mostly because of the intuitive and interpretable nature of the rule base. Since the

consequents of the rules in a Sugeno FIS do not have a direct semantic mean (i.e.

they are not linguistic terms) this interpretability is partially lost. However, since

Sugeno FIS rules’ consequents can have as many parameters per rule as input

values, this translates into more degrees of freedom in its design than a MamdaniFIS,thus providing more flexibility. Although many parameters can be used in the

consequents of the rules of a Sugeno FIS, even a zero order Sugeno FIS can

reasonably approximate a Mamdani FIS. In computational terms, a Sugeno FIS is

more efficient than a Mamdani FIS because it does not involve the computationally

expensive defuzzification process.

• In addition, a Sugeno FIS always generates continuous surfaces. The continuity of the output surface is important since the existence of discontinuities could result in

similar inputs originating substantially different outputs; a situation which is

undesirable from the control/ monitoring perspective. Thanks to its continuous

structure of output functions, a Sugeno FIS is also more adequate for functional

analysis than a Mamdani FIS.



Fuzzy rules

1.3.2.6 Linguistic rule

Fuzzy logic expresses the human knowledge in the form of

linguistic if – then rules. Each rule consists of two parts:

antecedent part called premise part(if part)consequent part called conclusion part(then part)

Rule is always of the general form:-

If (a set of condition is satisfied) then (the set of consequent

can be inferred)

e.g. IF x is A ,then y is B



Rule base in matrix form

current de/dt

Espeed Very low(VL) Low(L) Medium(M) High(H) Very high(VH)

Very low(VL) VL VL VL VL VL

Low(L) VL L L L VH

Medium(M) M L M H L

High(H) H H H VL H

Very high(VH) VH VH VH L L



Design and development of Fuzzy controller

• For the development of mamdami and sugeno type fuzzy controller followingpractical approach steps were taken:

• Define the control objectives and criteria

• Determine the input and output relationships and choose a minimum number of variables for input to Fuzzy Logic engine for both the control schemes.

• Create Fuzzy Logic membership functions that define the meaning of input/output

terms used in the rules.• Using rule based structure of Fuzzy Logic, break the control problem into a series

of IF X AND Y THEN Z rules that defines the desired system output response forgiven system input conditions.

• Test the system and evaluate results,

• Tune the rule and membership functions and retest until satisfactory results areobtained.

• Even though there are various inference methods for the implementation of fuzzylogic but for the present control problem Mamdani and Sugeno inference systemsare discussed which are employed for design of the fuzzy controller.



Parameters and range to be selected

• PARAMETERS

• INPUT:2

• E(t) rpm:165 695

• De/dt:-0.6 to 0.6

• Output:1

• Current:0 to 1 amp

• No of membership function :5

• Verylow ,low,medium,high,veryhigh

• No. of rules:25



MAMDAMI FIS EDITOR

INPUT 1 eSPEED MEMBERSHIP FUNCTION FORMAMDAMI



OUTPUT CURRENT MEMBERSIP FUNCTION

FOR SUGENO TYPE FIS

INPUT 2 de/dt MEMBERSHIP FUNCTION



FIG 4.2.13.1 MAMDAMI FUZZY OUTPUT

CORRESPONDING ESPEED168



FIG 4.2.13.2 SUGENO FUZZY OUTPUT

CORRESPONDING ESPEED 168













































































FIG 4 2 3 1 SUGENO FUZZY OUTPUT







REFERENCES

1. Ajith Abraham, Cerebral quotient of neuro fuzzytechniques SCIT Monash university , Australia,2001

2. Ahmad M. Ibrahim , “Introduction to Applied FuzzyElectronics” , Prentice Hall India ,1997

3. Bart kosko, “Neural networks and Fuzzy systems”, Prentice

Hall India,1992.4. B.R Gupta , Vandana Singhal, “Fundamentals of electric

Machines”, New age International Pvt Ltd, 1996

5. Cihan Karakuzu and Sitki Ozturk, “A Comparison of Fuzzy,Neuro and classical control Techniques based on an

Experimental Application", Journal of Qafqaz University,Fall 2000

6. I.J Nagrath and M Gopal, “Automatic Control System”, New Age International Publishers , 2000

SCOPE FOR FUTURE WORK



The controller developed above is based on the FIS and software simulation. Inour thesis we have developed the Mamdami and Sugeno fuzzy controller usingfive membership function based on above design strategies and can be appliedfor the speed control of various motors, the various controller fuzzy andconventional controller are compared so as to find out the best among them. Inour thesis work we have designed the above controller with the help of MATLAB SIMULATION TOOLBOX. The same controllers can be designedby other commercial software packages such as Brain Maker ,SAS EnterpriseMiner Software ,Neural Works ,MATLAB Neural Network Toolbox ,Propagator

,NeuroForecaster, Products of NESTOR, Inc. Ward Systems Group(NeuroShell, etc.) ,Neuralyst ,Cortex-Pro ,Partek ,NeuroSolutions ,Qnet ForWindows Version 2.0 ,NeuroLab, A Neural Network Library ,IBM NeuralNetwork Utility ,NeuroGenetic Optimizer (NGO) Version 2.0 ,WAND, TheDendronic Learning Engine, TDL v. 1.1 (Trans-Dimensional Learning),NeurOn-Line ,Neuframe.

Error reduction of the FIS can further be done with the help of ANFIS,OPTIMIZATION. For optimization we can use the Genetic OptimizationToolbox, Optimization USING Ant Colony of the MATLAB 7.5.



Cont..

7. Kevin M. Passino and Yurkovich.S.(1998), “Fuzzy Control” , Addison

Wesley Longman Inclusive ,8. Simon Haykin, “Neural Network” , Pearson Education Asia , 2001

9. Ross, “Fuzzy logic Engineering applications”, Mc graw Hill,1995.

10. Denai A.M and Attia S.A, “Fuzzy and Neuro Control of an Inductionmotor” . Int J.Appl. Math Comput. sci., 2002, vol.12.,No.2,221-233

11. Parvathi .C.S and Bhaskar.P (2004)“Effect of Sampling Rate on theperformance of Fuzzy Logic Controller For the Speed Control of DCMotor” , IETE Technical review,Vol 21,No. 4,pp 291 — 298

12. Pedrycz.W, Heterogeous(2004) “Fuzzy logic networks fundamentals anddevelopment studies”. IEEE transactions, VOL 15 , Issue 6, Nov. ,pp1466-1481.



CONT..

13. Rudolf Kruse and Andreas Numberger ,bearning “Methods for Fuzzy Systems”

1994.

14. Rahman.S, Ullah .Zand W. Shields Neely,(1994) National Semiconductors

Corporations Application Note 958: DC Controller Design With NeuFuz ,

15. Kaehler .D. S. (1994), Fuzzy Logic an Introduction Part 1,2 and 3

16. Thamaraiselvi.S,selvathi.D, Salivahanan.S, Indumathi.G, Kumar.V.(2003) “Fuzzylogic based intelligent control for Irrigation system”, IETE Technical Preview, Vol

20, No. 3, June -2003, pp 199-203

17. Noghondari.M and Rashidi.M.(1994) “General Regression Neural Network Based

Fuzzy Approach for sensorless speed control of Induction Motor”.

18. C. K. Lee and W. H. Pang(1994) “A Brushless DC Motor Speed Control SystemUsing Fuzzy Rules” Power Electronics and variable Drives, Conference

Publication No 399. IEEE



CONT..

19. Wander G. da Silva, Paul P. Acarnley, and John W.(2000) “Find Applicationof Genetic Algorithms to the Online Tuning of Electric Drive SpeedControllers” IEEE transactions on industrial electronics, vol. 47, no. 1,

20. Sankaran.R, Chandramohan .P.S.(2001) "Adaptive Neuro-fuzzy controllerfor improved performance of a permanent magnet brushless DC motor", The10th IEEE International Conference on Fuzzy Systems, pp. 493-496.

21.Shujaec., Srilirakiish Sarathy , Roan Nicholson.(2002) “Neuro -FuzzyController and Convention Controller: A Comparison” Proceedings of the5th Biannual World Automation Congress, pp. 207-214.

22. Cheng.K.Y (2002) “Fuzzy Optimization Techniques Applied to the Designof a Digital BLDC Servo Drive” Power Electronics Specialists Conference,

Vol. 7, pp. 23- 27.23. Lin C.-L. and Jan H.-Y.(2002) “Multiobjective PID control for a linear

brushless DC motor: an evolutionary approach” IEE Proc.-Electr. PowerAppl.. Vol. 149, No 6.



CONT..

24.Halvaei Niasar. (2002) “Speed Control of a Brushless DCMotor Drive via Adaptive Neuro-Fuzzy Controller Based on

Emotional Learning Algorithm” A Proceedings of the 5th

Biannual World Automation Congress, pp. 207-214.

25.Changliang .X, Peijian Guo, Tingna Shi and Mingchao Wang

(2004)“Speed Control of Brushless DC Motor Using Genetic

Algorithim Based Fuzzy Controller” Proceedings of the 2004

International Conference on Intelligent Mechatronics and

Automation Chengdu, China August .

26.Baojiang .Z and Shiyong .L(2006) “Design of a Fuzzy Logic

Controller by Ant Colony Algorithm with Application to an

Inverted Pendulum System” IEEE International Conference

on Systems, Man, and Cybernetics Taipei, Taiwan



Cont..

27. ZHAO Baojiang (2010) “Optimal Design of Neuro-FuzzyController Based on Ant Colony Algorithm” Proceedings of the 29th Chinese Control Conference , Beijing, China.

28.Yogesh Piolet (1996)“Comparison of Mamdani and Sugeno

Fuzzy Inference Systems” “Soft ComputingApplications.29.Kaoru Hirota (1996) “Theoretical advances and applications

of fuzzy logic and soft computing”.

30.Leslie smith(2000) “Neural networks and their application to

finance” Applied Technologies Centre London, UK.



Cont….

• Abraham (2001) has presented some basic theoretical aspects

of ANN and FIS and some of the popular Neuro-fuzzymodeling techniques. Some application areas are discussed

which are already implemented by the author and has

advocated that neuro fuzzy systems are more efficient in terms

of better performance time and lower error rates as comparedto pure neural networks systems. Khalil Shujaec. et al (2002)

proposed paper differentiating PID controller ,adaptive

controller with neurofuzzy controller and found The NEFCON

to be superior in comparison to the conventional control

techniques such as PID ,adaptive controller. But some of the

problems associated with NEFCON are introduced such as it

can be slow to converge, and only one controller can be

trained at a time.



Cont….

• Kuang-Yao Cheng, et al (2002) presents a novel fuzzy

optimization strategy for designing BLDC servo drives and

analyzed how to find the optimal value for each servo control

parameters. Besides, by using the fuzzy-logic linguistic

description, the expert knowledge for optimizing these control

parameters can be converted into a fuzzy stepwise tuner tospeed up the overall optimization process. Changliang Xia, et

al (2004) proposed the GA based fuzzy controller as the speed

controller of the BLDCM servo system. By comparison with

PID controller, it testifies that this method is not only robust,

but also can improve dynamic performance of the system. The

off-line adjust optimize the fuzzy rules, and the on-line tuning

of the parameters of the fuzzy controller make the controller

good dynamic control system

Cont



Cont….

• Baojiang Zhao et al (2006) proposed adaptive ant colony algorithm

having novel search mechanism that dynamically adjusts the strategy

of selection of the paths and the strategy of the trail information

updating. Results of function optimization show that AACA has nice

performance and can be used to design a fuzzy Logic controller for

real-time control of an inverted pendulum system.

• ZHAO Baojiang (2010) proposed adaptive ant colony algorithm basedon dynamically adjusting the strategy of the trail information

updating. The algorithm can keep good balance between accelerating

convergence and averting precocity and stagnation. The results of

function optimization show that the algorithm has good searchingability and high convergence speed. The algorithm is employed to

design a neuro-fuzzy controller for real-time control of an inverted

pendulum



Cont….

• Meena Tushir et al (2010) proposed a novel interval type-

2 fuzzy controller for speed control of DC motors (series

as well as shunt). Performance of the proposed IT2FLC

was also compared with corresponding conventional

FLC’s with respect to several indices such as peak

overshoot (%OS), settling time, rise time, Integral of

absolute error (IAE) and integral-of-time-multiplied

absolute error (ITAE) and from the simulation, it shows

that the proposed controller can track the reference speedsatisfactorily even under load torque disturbances.

Documents

DESIGN COMPARISON OF MAMDAMI AND SUGENO TYPE OF