Ijeee v1i4 07

IJEEE, Vol. 1, Issue 4 (August, 2014)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

RESEARCH FOR PRODUCTIVITY CONTROL ACCESSMENT, APPLIED TO THE COMPARATIVE ANALYSIS OF PID & FUZZY CONTROLLERS IN PROCESS INDUSTRY 1

Prince Munjal, 2Sirdeep Singh

1,2

Bhai Gurdas Institute of Engineering and Technology, Punjab, India 1

princearora0091@gmail.com

ABSTRACT- This paper presents a systematic approach for the design and implementation of temperature controller using Intelligent Fuzzy Hybrid PID Controller for Temperature control in Process Industry. The proportional integral derivative (PID) controllers are widely applied in industrial process owing to their simplicity and effectiveness for both linear and nonlinear systems, and the tuning methods still a hot research area to give the optimum result for control behavior. The proposed approach employs PID based intelligent fuzzy-controller for determination of the optimal results than PID controller parameters for a previously identified process plant. Results indicate that the proposed algorithm significantly improves the performance of the process Industries. It is anticipated that designing of PID based fuzzy controller using proposed intelligent techniques would dramatically improves the speed of response of the system, Rise time and settling time would be reduced in magnitude in the intelligent scheme as compared with conventional PID controller. It can get better performance of the dynamic response by using fuzzy PID controller than the traditional PID controller or fuzzy controller through the comparison MATLAB with the traditional PID controller and fuzzy controller. The first part is devoted to the formal framework of the theory of fuzzy sets and fuzzy controllers. The second part of this paper is a description of a simulated system, and a presentation of simulated controllers. In the second part, fuzzy controller is examined in details. A sensitivity of the fuzzy logic controller to design parameters, different shapes and superposition of membership functions, is tested. Moreover, the simulations are done for the different types of fuzzy reasoning and defuzzification methods.

controller is the most widely used in industrial application because of its simple structure. On the other hand conventional PID controllers with fixed gains do not yield reasonable performance over a wide range of operating conditions and systems (time-delayed systems, nonlinear systems, etc.). Control techniques which based on fuzzy logic and modified PID controllers are alternatives to conventional control method. Fuzzy logic control (FLC) technique has found many successful industrial applications and demonstrated significant performance improvements.

However, fuzzy controller design remains a fuzzy process due to the fact that there is insufficient analytical design technique in contrast with the welldeveloped linear control theories. A wide variety of fuzzy PID-like controllers have been developed. In most cases Fuzzy logic can handle imprecise data and can effectively used in controller. intelligent fuzzy control system is as shown in fig 1.

Keywords- Process plants, Steam temperature control, Industrial system, Multi objective control; Optimaltuning; PID control Fuzzy logic control, genetic algorithms, nonlinear control, optimal control, PID control.

Fig 1. Fuzzy Control System

1. INTRODUCTION Well-known proportional-integral-derivative

PID

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This Paper is an attempt to undertake the development of a new analytical approach to the optimal design of www.ijeee-apm.com

fuzzy controllers. We propose a new methodology for the optimal design of fuzzy PID controllers. In the proposed work the main objective of the investigator is to compare the performances of conventional PID controllers and the intelligent fuzzy logic controller. For this comparison, two parameters needs to be evaluated i.e. Overshoot and settling time. This paper suggests a fuzzy logic based controller which acts with the help of artificial intelligence techniques. There are many artificial intelligence techniques and fuzzy logic is one of them.

2. FUZZY LOGIC – A BRIEF HISTORY Fuzzy logic, invented by Lotfi Zadeh in the mid 1960s provides a representation scheme and a calculus for dealing with vague or uncertain concepts. It is a paradigm for an alternative design methodology which can be applied in developing both linear and non- linear systems for embedded control. Zadeh originally devised the technique as a means for solving problems in the soft sciences, particularly those that involved interactions between humans, and/or between humans and machines [6]. Since then there has been rapid developments of the theory and application of Fuzzy logic to control systems. Fuzzy logic controllers are being increasingly applied in areas where system complexities, development time and costs are the major issues[7].

In Japan, Terano, inspired by Zadeh’s work introduced the idea to the research community in about 1972. This led to active research and a host of commercial applications, almost entirely in the area of physical system control. In 1990 a research institute namely LIFE (Laboratory for International Fuzzy Engineering) started functioning under the leadership of Terano[6].

The Japanese researchers have been a primary force in advancing the practical implementation of Fuzzy theory and now have more than 2000 patents in the area.

3. METHODOLOGY A new methodology is proposed for the analytical design of a fuzzy PID controller. There are various steps for the design of the Fuzzy PID Controller.

In Step 1, the structure of a fuzzy PID controller is designed and the structural parameters are set for the preliminary design. The tuning parameters are identified Step 2, while in Step 3 an analytical fuzzy calculation is performed, which produces a closed-form relationship between the design parameters and control action for the fuzzy inference.

Step 4, numerical simulation (or control theory) is used to obtain the control performance data.

Step 5, genetic-based optimizations are carried out to produce optimal design parameters. This also provides useful information for the redesign of the original system.

Finally, if necessary, redesign is undertaken using the designer's expertise for further improvement to the control system. Note that the theoretical study in Step 3 makes the fuzzy controller transparent. This step is important since it will establish a close link between fuzzy control design technique and classical/modern www.ijeee-apm.com

control theory.

Simplicity is a key principle of this design methodology.

The reason is obvious if we see that fuzzy logic controllers are systems which simulate human control exercise. For many everyday control tasks, people initially try to apply simple rules. Three rules used in this work are very common in a feedback set-point control problem. If a satisfactory control process can be achieved by applying simple rules, the use of complex rules, which is often associated with a higher cost of computation, becomes unnecessary. Simplicity is the best and direct way to maintain a clearly physical insight into the control laws. It also makes highdimensional fuzzy systems tractable for using simple mathematical expressions for describing functionality between design parameters and nonlinearity.

4. SIMULATION RESULTS Oil Tank Temperature Controller This temperature controller is used to control the temperature of raw oil in oil tank. In this the set temperature is 265oC and PID temperature controller reaches set temperature in five hours and twenty minutes.

Fuzzy model was developed using error, change in error and fuzzy output to improve the settling time.

Table 1. Fuzzy system, for temperature controller

(a) Membership functions of Error input.

Membership function for Error Initial Peak Final Linguistic variable value value value Very Small (VS) -2 20 60 Small (S) 20 50 90 Medium (M) 60 100 140 High (H) 100 140 190 Very High (VH) 160 190 240 (b) Membership functions of Change in Error input.

Membership function for Change in Error Linguistic Initial Peak Final variable value value value Very Small (VS) -12 -6 -3 Small (SM) -6 0 8 Medium (MD) 0 8 16 Large (L) 9 17 28 (c) Membership functions of Fuzzy output.

Membership function for Fuzzy Output Linguistic Initial Peak Final variable value value value Very Small (VS) 29 38 52 Small (S) 38 70 92 Medium (M) 70 110 142 Large (L) 120 165 215 Very Large (VL) 180 220 265 To develop Fuzzy controller, firstly error signal(e) is calculated by subtracting output of PID temperature International Journal of Electrical & Electronics Engineering 25

controller from set temperature then change in error(∆e) was calculated by subtracting previous error from current error. Considering error and change in error as input and fuzzified output as output function membership functions are created for each input and output. Membership functions for these quantities are defined as in above Table 1. The membership functions are shown in schematic form in Fig. 2.

time and a steady state error of 45oC. To improve this fuzzy response the membership functions of all the input and output are increased.

Fig.3. Response curve of PID Vs Fuzzy temperature controller in oil tank temperature controller

(a) Membership functions of Error input.

(b) Membership functions of Change in Error input.

Membership functions of error and change in error inputs have been increased to six and that of fuzzy output has been increased to seven.

The rule base is also revised as shown in Table 3. By using this rule base, oscillations in fuzzy response decreases and steady state error was also reduced than the last fuzzy model.

Table 3 New Improved Rule base

(c) Membership functions of Fuzzy output.

Fig.2. Fuzzy system, for oil tank temperature controller

A rule base was developed for the fuzzy model using simple IF-THEN rules.The rule base is summarized as in Table 2 Table 2 Rule base

Fuzzy output (Fz) Error(e) VS S M H VH

uzzy output (Fz) Error (e) VS S M H VH EH

Change in Error (∆e) N EL S VS

NS EL VL L ML M VS

SM EL VL L L M VS

SM EL VL VL L ML M

L ML M

VL M

The MATLAB simulation results are plotted along with the actual temperature and set temperature obtained from the process, are plotted in Fig.4.

Change in Error (∆e) VS VL L S VS

SM VL L L M VS

MD VL VL L M S

L VL L VS

On the basis of this rule base a fuzzified output is calculated. This Fuzzy model is simulated in MATLAB fuzzy logic toolbox GUI, and results are obtained. Then results are plotted along with the actual temperature and set temperature obtained from the process, are plotted in Fig.3.

Red graph shows the fuzzy output of fuzzy model of oil tank temperature controller, black line represent the output of PID temperature controller and blue line represent the set temperature enter in PID temperature controller. Fuzzy output has some oscillations in rising International Journal of Electrical & Electronics Engineering 26

Fig.4 Improved Response curve of PID temperature controller Vs Fuzzy temperature controller.

Here the steady state error is decreased to 7oC, and settling time also improved. Further improvements in Fuzzy output and rule base have been made. To achieve this requirement, the range of last two membership www.ijeee-apm.com

function of fuzzy output has been changed. The new range is VL – 200-220-265 and EL – 250-255-270 and rule base is shown in Table 4.

revised. Membership functions of error input and fuzzy output are increased.

Table 4 Improved Rule base

Fuzzy output (Fz) Error (e) VS S M H VH EH

Change in Error (∆e) N EL VS

NS EL EL VL ML M VS

SM EL EL VL L M S

M VL VL VL L ML M

L VL M

VL VS

The MATLAB simulation results are plotted along with the actual temperature and set temperature obtained from the process, are plotted in Fig.5.

Fig.6. New Improved response curve of PID temperature controller Vs Fuzzy temperature controller

Membership functions of error input have been increased to seven and that of fuzzy output has been increased to nine. Improved error input, fuzzy output and rule base are shown in Table 6.

Table 6. Improved Rule base Table

Fig.5. Improved response curve of PID Vs Fuzzy temperature controller.

Here the steady state error and settling time both have been improved. Steady state error is decreased to zero and settling time is reduced by 2 hours and 20 minutes.

For Further improvements, Fuzzy output and rule base have been revised. Revised Fuzzy output and rule base are shown in Table 5.

Fuzzy output (Fz) Error (e) VS S ML MH H VH EH

Change in Error (∆e) N EL EL EL VS

NS EL EL EL VL ML HS VS

SM EL EL EL VL ML HS LS

M EL EL EL L LM -

L L HM -

VL L HM LS

The MATLAB simulation results are plotted along with the actual temperature and set temperature obtained from the process, are plotted in Fig.7.

Table 5. New Improved Rule base

Fuzzy output (Fz) Error (e) VS S M H VH EH

Change in Error (∆e) N EL VS

NS EL EL VL L ML S

SM EL EL EL VL ML VS

M EL EL VL VL L VS

L L M

VL -

The MATLAB simulation results are plotted along with the actual temperature and set temperature obtained from the process, are plotted in Fig.6.

Here fuzzy output response contains lesser oscillations.

Settling time also reduce by 50 minutes from last fuzzy model. Total time reduced is 2 hours and 65 minutes.

To further improve fuzzy response, fuzzy system is www.ijeee-apm.com

Fig.7. Improved response curve of PID temperature controller Vs Fuzzy temperature controller

Here fuzzy output response contains lesser oscillations than the last fuzzy output response. Settling time is also reduced by 24 minutes, i.e. settling time is 2 hours and 25 minutes. But one spick is produced during the rise time that can be removed by changing the rule base as in Table 7.

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Table 7. New Improved Rule base Table

Fuzzy output (Fz) Error (e) VS S ML MH H VH EH

Change in Error (∆e) N EL EL EL VS

NS EL EL EL VL ML HM VS

SM EL EL EL EL ML HM LS

M EL EL EL L HM -

L L HM -

VL L HM VS

The MATLAB simulation results are plotted along with the actual temperature and set temperature obtained from the process, are plotted in Fig.8.

thousands, of rules running on dedicated computer systems, a unique FLC using a small number of rules and straightforward implementation is proposed to solve a class of temperature control problems with unknown dynamics or variable time delays commonly found in industry. Additionally, the FLC can be easily programmed into many currently available industrial process controllers. The FLC was first simulated on a tank temperature control problem with promising results. Then, it was applied to an entirely different industrial temperature apparatus. The results show significant improvement in maintaining performance and stability over a wide range of operating conditions.

The FLC also exhibits robust performance for plants with significant variation in dynamics. The stability characteristics were investigated and a stability safeguard was derived. The available field application shows Fuzzy-PID hybrid controller can not only restrain the large fluctuation to temperature effectively, but also has excellent static performance. PID controller can not be applied with the systems which have a fast change of parameters, because it would require the change of PID constants in the time. It is necessary to further study the possible combination of PID and fuzzy controller. It means that the system can be well controlled by PID which is supervised by a fuzzy system. The fuzzy based controller gives the best performance, but the control engineer faces different kind of challenges to design such a controller.

REFERENCES

Fig.8. Improved response curve of PID temperature controller Vs Fuzzy temperature controller

Here settling time reduced by 12 minutes from last fuzzy model. Finally fuzzy model give fuzzy output response with lesser oscillations. This fuzzy model reduces the settling time by 3 hours and 17 minutes.

Comparing first fuzzy model in Fig. 3 and last fuzzy model in Fig 8 developed for oil tank temperature controller, an analysis is made that by increasing the number of membership functions from 5 to 7 for error input. and from 4 to 6 membership functions for change in error input, from 5 to 9 membership functions of fuzzy output, a response curve has been obtained that has a settling time of 1hour 58 minutes, and oscillations in response curve are all most removed. This fuzzy model reduces the settling time by 3 hours and 25 minutes.

CONCLUSION Fuzzy controllers have the advantage that can deal with nonlinear systems and use the human operator knowledge. In all the applications discussed, FLC is an extremely successful means of controlling systems with non-linearities and various complexities. Incorporation of Fuzzy logic in such systems is feasible, versatile and has many advantages. Aiming at characteristic of agro plants and control requirement, a Fuzzy-PID hybrid controller with advantages of both fuzzy controller and PID controller integrated is presented in this paper.

Unlike some fuzzy controllers with hundreds, or even International Journal of Electrical & Electronics Engineering 28

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[5]. Is in Erenoglu, Ibrahim Eksin, Engin Yesil and Mujde Guzelkaya, “An intelligent hybrid fuzzy PID controller,” Proceedings of 20th European Conference on Modeling and Simulation, 2006.

[6]. Leehter Yao and Chin-Chin Lin, “Design of Gain Scheduled Fuzzy PID Controller,” World Academy of Science, Engineering and Technology, pp.152-1561, 2005.

[7]. Seema Chopra, R. Mitra, Vijay Kumar, “Auto tuning of fuzzy PI type controller using fuzzy logic,” in the Proceedings of IJCC, Vol. 6, no.1, pp. 12-18, March, 2008.

[8] .Zhen-Yu Zhao, Masayoshi Tomizuka, Satoru Isaka, “Fuzzy gain scheduling of PID controllers,” IEEE Transactions on Systems, man and cybernetics, Vol. 23, no. 5, September/October 1993, pp. 1392-1398.

AUTHOR Prince Munjal was born in India, on Oct 21, 1989. He graduated in Electrical Engineering from Baba Hira Singh. Bhattal Institute of www.ijeee-apm.com

Engineering and Technology, Lehragaga, Sangrur in 2011. He is pursuing M.Tech from BGIET Sangrur. He is working with KCT Engineering College, Fatehgarh,

www.ijeee-apm.com

Sangrur as Assistant Professor in the Deptt. of Electrical Engineering.

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