IJECT Vol. 6, IssuE 1, spl- 1 Jan - MarCh 2015 ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)

w w w . i j e c t . o r g 160 InternatIonal Journal of electronIcs & communIcatIon technology

Design and Performance of PID and Fuzzy Logic Controller for Thermal Process Using LabVIEW

1Ullash Roy, 2Ransai Basumatary, 3Ganesh Roy 1,2,3Instrumentation Engg. Dept., Central Institute of Technology, Kokrajhar, BTAD, Assam, India

AbstractThe Proportional Integral Derivative (PID) controller is the most widely used control strategy in industry. The popularity of PID controllers can be attributed partly to their robust performance in a wide range of operating conditions and partly to their functional simplicity. This paper presents design of PID controller using LabVIEW software for a thermal process. Also a Fuzzy Logic Controller (FLC) using simple approach & smaller rule set is proposed using LabVIEW. Experimental results are demonstrated to compare the performances of the two controllers.

KeywordsPID Controller, Fuzzy Logic Controller, Thermal Process, LabVIEW.

I. Introduction The Proportional–Integral–Derivative (PID) controller operates the majority of the control system in the world. Now a days in any process industry PID controller is one of the most important components in order to obtain the high quality products at lesser cost along with all the safety aspects of the plant operation. PID controllers [5], [9] provide robust and reliable performance for most systems if the PID parameters are tuned properly.Fuzzy logic control systems [10], which have the capability of transforming linguistic information and expert knowledge into control signals, are currently being used in a wide variety of engineering applications. The simplicity of designing these fuzzy logic systems has been the main advantage of their successful implementation over traditional approaches such as optimal and adaptive control techniques despite the advantages of the conventional Fuzzy Logic Controller (FLC) over traditional approaches, there remain a number of drawbacks in the design stages. Even though rules can be developed for many control applications, they need to be set up through expert observation of the process. The complexity in developing these rules increases with the complexity of the process. FLC’s also consist of a number of parameters that are needed to be selected and configured in prior, such as selection of scaling factors, configuration of the centre and width of the membership functions, and selection of the appropriate fuzzy control rules. In this project Temperature is controlled using Virtual Instrumentation. It is the combination of data acquisition hardware and LabVIEW software .Here the DAQ hardware used which is C Series I/O Modules for NI Compact DAQ and the LabVIEW (2012) software is installed in the PC and the DAQ hardware is connected to the PC through (RJ-45). At first the temperature is measured using 3-wire RTD and it is connected to the 4-Channel NI 9219 universal analog input module. DAQ converts the analog output of RTD into digital form that is compatible with computer. In the computer the PID controller program is designed with the help of LabVIEW. The action perform by the PID controller is feed to the process through NI 9263 analog voltage output module. Temperature is monitored in the front panel of the program and is settle down within the set point provided.

II. Process Plant Layout The thermal process that is used to implement the PID controller and FLC is built around LabVIEW software. This system layout is shown in Fig.1. This type of system is usually known as Virtual Instrumentation system. The combination of data acquisition hardware and LabVIEW software are required to install into the computer. The C Series I/O Module for NI Compact DAQ has been used as DAQ hardware and the LabVIEW (2012) software is installed in the computer and finally the DAQ hardware is connected to the computer through RJ-45. The set point is given in the Front Panel of the controller program of the temperature presses control to compare the temperature with the process value coming from RTD through 4-Channel NI 9219 Universal Analog Input Module. The NI 9219 Module is a module which convert analog signal to digital form and make compatible with computer. If there is any error present in the process the controller takes necessary action. The controller output is feed to the final control element through NI 9263 Analog voltage output module that converts digital signal coming from computer to analog form and make compatible to operate the final control element. Here relay has been used as a final control element. As per action taken by the controller the relay is switched ON or OFF that will ON or OFF the heater and thus the heating process is controlled accordingly. When the set point is reached the desire temperature the process will be stopped.

Fig. 1: Process Plant Layout of Temperature Measurement

III. Designing of PID Algorithm The basic mathematical equation for the PID control algorithm is as follows.

(1)The PID controller is designed by following the equation (1). The algorithm is given in the block diagram Fig. 2(a) and the simulated response is given in the front panel in the Fig. 2(b) [1-2].

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ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)

Fig. 2(a): Block Diagram of PIDVI

Fig. 2(b): Front Panel PIDVI

After the controller has been designed, according to equation (1) it is required to adjust the parameters. This is known as a ‘controller tuning’ problem. There are three general approaches are used for tuning a controller [5], [12]. In the present work the Ziegler-Nichols method [2] has been selected for its cycling method of performance. According to that tuning methodology the values for the constants are KP=0.5, KI=0.6, KD=2.This PID algorithm has been used to control the temperature of a process tank showing in Fig. 1. The necessary block diagram and front panel are provided here in Fig. 3(a) and Fig. 3(b) respectively.

Fig. 3(a) Block Diagram of Thermal Process Using PID

Fig. 3(b): Front Panel of Thermal Process Using PID

IV. Designing of Fuzzy Logic Algorithm The Fuzzy logic is the method of rule-based decision making used for process control. Fuzzy system contains three main parts as membership functions, rules, and linguistic variables. For a two input fuzzy controller the number of membership functions are generally is used 3,5,7,9 or 11 for each input are mostly used [11]. In this work, only two fuzzy membership functions are used for the two inputs error e and derivative of error as shown in the fig. 4. The fuzzy membership functions for the output parameter are shown in Fig. 5, here N means Negative, Z means Zero and P means Positive.

Fig. 4: Membership Function for Inputs and

The system response can be divided in two phases. Phase 1 - System output is below the set point temperature. Phase 2 - System output is above the set point. Depending upon whether the output temperature is increasing or decreasing, 4 rules were derived for the fuzzy logic controller (Table 1). These four rules are sufficient to cover all probable situations [6].

Table 1: Fuzzy Rules

uN P

eN N ZP Z P

IJECT Vol. 6, IssuE 1, spl- 1 Jan - MarCh 2015 ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)

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Fig. 5: Membership Function for Output

Block diagram model of the fuzzy controller and the plant with unity feedback is shown in Fig. 6(a). The output response for the thermal process is depicted on Fig. 6(b).

Fig. 6(a): Block Diagram of Thermal Process Using FLC

Fig. 6(b): Front panel of Thermal Process Using FLC

V. Comparison between PID and FLCThe time response parameters like maximum overshoot (%Mp), rise time(tr), settling time (ts) and steady state error (ess) for Ziegler Nichols tuned PID and fuzzy logic controller (FLC) for the higher order system are presented in Table 2. The output responses obtained by utilizing PID control technique and Fuzzy logic technique is also provided in fig. 7. From the graph it is easy to find the transient parameters of the Table 2.

Table 2: Time Response ParametersSl. No.

Controller Used

tr (sec)

ts (sec) %Mp Transient

Behaviour1 PID 7 11 10 Oscillatory2 FLC 9 26 0 Smooth

Fig. 7: Output Response of the Thermal Process Using PID and FLC

Fig. 8: Snap of the Complete Process At Working Condition

VI. ConclusionThe paper presented an outline of PID controller, design of PID controller using Z-N technique and design of fuzzy logic controller for thermal process system. The proper selection of a controller is a very crucial issue. After selecting the proper controller the control of these plant variables will become quite effortless. Experimental results using LabVIEW are discussed here for Ziegler Nichols tuned PID controller and the Fuzzy logic controller. The Fuzzy Logic controller gives no overshoot, zero steady state error and smaller settling time than PID controller. The experimental results confirms that the proposed Fuzzy logic controller with simple design approach and smaller rule base can provide better performance comparing with the Ziegler Nichols tuned PID controller.

VII. AcknowledgmentThe authors would like to acknowledge the Instrumentation Engineering department of Central Institute of Technology, Kokrajhar for providing the facility of laboratory.

References[1] Alia M A. K., Zalata M K. A.,“A Closed-Loop Temperature

Control System by Utilizing A LabVIEW Custom-Design PID controller”, [Online] Available: http://www.inele.ufro.

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cl/apuntes/LabView/Manuales/.[2] Chattopadhyay S., Roy G., Panda M.,“Simple Design of PID

Controller and Tuning of Its Parameters Using LabVIEW”, Sensors & Transducers Journal, Vol. 129, Issue 6, June 2011, pp. 69-85.

[3] Kavitha S., Ponmalar S.J.,“Fuzzy Based Control Using Lab View for Temperature Process”, International Journal of Advanced Computer Research, Vol. 2, No. 4, Issue 6, December 2012.

[4] “Fuzzy Logic Toolkit User Manual”, www.ni.com.[5] Ang K.H., Chong G., Li Y.,“PID Control System Analysis,

Design and Technology,” IEEE transaction on Control System Technology, Vol. 13, No. 4, 2005, pp. 559-576.

[6] Vaishnav S.R., Khan Z.J.,“Design of PID & Fuzzy logic controller for higher order system”, International Conference on Control & Automation (ICCA'07), Hongkong, China, 2007, pp. 1469-1472.

[7] Sertaç Sunay A., Koçak O., Kamberli E., Koçum C., “Design and Construction of A Labview Based Temperature Controller With Using Fuzzy Logic”.

[8] Chinthamani B., Rajeena Mol P.T., Kamini K.P., Sughashini K.R.,“Fuzzy Based Control Using Labview for MISO Temperature Process”, IJRET, Vol 1, Issue 2, pp. 108-114, October, 2012.

[9] PID controller, Wikipedia, [Online] Available: http://en.wikipedia.org/wiki/PID_controller.

[10] Lee C. C.,“Fuzzy Logic in control System: Fuzzy Logic Controller-Part I”, IEEE Transaction on Systems, Man and Cybernetics, Vol. 20, No. 2, March/April 1990.

[11] S.Chopra, R.Mitra, V.Kumar,“Fuzzy controller:Choosing an appropriate & smallest rule set”, International Journal of Computational Cognition, Vol. 3, No. 4, 2005, pp. 73-78.

[12] Zhang J., Wang N., Wang S.,“A developed method of tuning PID controllers with fuzzy rules for integrating process”, Proceedings of the American Control Conference, Boston, 2004, pp. 1109-1114.