TIMA 2013

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Proceedings of the International Conference on Trends in Industrial Measurements and Automation (TIMA-2013),

Department of Instrumentation Engineering, MIT Campus, Anna University, Chennai-44. Page 137

LEVEL CONTROL OF HORIZONTAL CYLINDRICAL TANK USING

INTELLIGENT CONTROL TECHNIQUES1. M. Ulaganathan M.Tech., Assistant Professor ([email protected]), 2. K. Abinaya, UG student

([email protected]), 3. M.Aarthi, UG student ([email protected])

Department of Electronics and Instrumentation

Easwari Engineering College

ABSTRACT: This paper proposes control techniques forthe nonlinear level process of a horizontal cylindrical

tank. The control techniques used are Neuro controllerand Fuzzy Logic Controller which are implementedusing LabVIEW. Based on the performance criteria, itcould be concluded that Fuzzy Logic Controllereffectively improves the performance of the system.

KEYWORDS: Fuzzy Logic Controller, Horizontal

cylindrical tank, Neuro Controller, Nonlinear Level

Process

I. INTRODUCTIONHorizontal cylindrical tank is unique in its shape,

as its area varies with increase in height. Many pro-

cess industries use horizontal tank for storage of oil orchemical liquid since it has a wide surface area. Levelcontrol of tanks is necessary to maximize productivity

and efficiency and to ensure safety. Poor operationsinclude overfilling of the tanks due to which cata-

strophic reactions may take place.

Traditionally, a nonlinear process has to be

linearized first before an automatic controller can be

effectively applied. This is typically achieved by

adding a reverse nonlinear function to compensate for

the nonlinearity. There was a proposal in 2005 [1]

which showed the level control of a horizontal tank

implemented using Fuzzy-PID Cascade controller.The application of a neural network trained for a

given set of inputs and output was published in 1993[2] which was used for level control in petrochemical

tanks.

In this paper, the first section describes the

process and the identification of transfer function. In

the second section, we will discuss the conventional

control technique and its tuning method. The third

section illustrates the training of a neural network and

its implementation as a controller. Finally the Fuzzy

logic control (FLC) technique is discussed and the

most effective control method is established based on

the comparison of Integral Absolute error (IAE).

II. PROCESS DESCRIPTION

A.SCHEMATIC DIAGRAM

Fig.I Schematic diagram of the process

Diameter 0.3m

Length 0.45m

Height above the ground 1.2m

Inflow 120LPHTable I: Dimensions of horizontal tank process

The process described in fig I shows that the

nonlinear tank level is controlled by final control

element fitted in the inflow side of the setup. The final

control element used in the process is a equal percentage valve. The opening and closing percentage

depends on the current level of the fluid in the tank.

The dimensions of the tank are declared in table I. Thecurrent level is measured by means of a differential

pressure transmitter (DPT). The output of DPT is

given to NIC-DAQ card which is interfaced with a

computer through serial communication. On receiving

the level value the LabVIEW program takes the

necessary control action. Through NIC-DAQ the value

is given to the final control element. To avoid damage

to the pump due to high pressure a by-pass line has

been provided.

B. MATHEMATICAL MODEL

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Fig II Side view of the cylindrical tank

The mathematical model is derived byconsidering the side view of the tank of radius R and

level in the tank (H). The nonlinearity of the level is

calculated by subtracting the area of the triangle from

the area of the sector as shown in fig II. The resultingarea A is differentiated to obtain a term for changing

level in the tank.

A 2) - r(R-H)

= ((2HR-H2)-1/2) [( ) + 6HR-2H2-2R 2] (1)

From (1), the Mass-Balance equation is derived as,

l * = q 1 - q 2

Substituting for from (1)

= (2)

III. STEADY STATE CHARACTERISTICS

The mathematical model is designed usingLabVIEW based on (2) as derived above. We obtained

the steady state characteristics curve by varying the

inflow to the process in steps and plotting the graph

for inflow vs. steady state.

Fig IV Steady State Characteristics for inflow values ranging from

1-32 LPH

The Steady state characteristics graph shown in

fig IV is divided into seven regions. We obtained the

transfer function for every region and tabulated the

result as follows:

Region

Steady

state

(cm)

K

Rise

time

(cm)

Transfer

Function

0-6 1.15 0.25 0.196

6-10 3.19 0.51 0.176

10-14 6.25 0.77 0.138

14-18 10.33 1.01 0.97

18-22 15.43 1.28 0.118

22-26 21.55 1.53 0.185

26-30 28.7 1.79 0.274

Table II : Transfer function by gain scheduling of the seven regions

A. SYSTEM IDENTIFICATIONSystem identification process is carried out using

the system identification function ‘ident ’ in MATLAB.

The input (inflow) and output (level) data from the

real time process in open loop arrangement applied to

the ident tool to obtain the transfer function of the

system. We obtained the transfer function for

maximum inflow as

We verified the real time transfer function to be

same as that of the simulation results. Hence theestimated transfer function is used in the following

sections for designing the controllers.

IV. CONVENTIONAL CONTROLLERWe designed PI controller for each of the

seven regions defined in table II using gain

scheduling. The gain scheduling is achieved by tuning

the PI parameters (i.e. Proportional gain, integral time,

derivative time) using Ziegler Nichols tuning rules.

The controller is designed in such a way thatdepending upon the input set value, the PI values for

the corresponding region is fired. This closed loop

system is implemented in LabVIEW and the responsecurve is obtained.

A. ZIEGLER NICHOLS TUNINGWe implemented the Ziegler Nichols tuning by

obtaining sustained oscillations using a P controller.

The PI parameters were calculated using the Ziegler

Nichols rules.

Region

(cm)

Proportional

gain (Kp)

Integral

gain (Ki)

0-6 58.55 0.038

6-10 25.62 0.028

10-14 13.64 0.031

14-18 7.16 0.02618-22 7 0.033

22-26 9.123 0.032

26-30 11.54 0.028Table III : PI parameters calculated for the seven regions of gainscheduling using Ziegler Nichols tuning rules.

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Fig V: Closed loop response by Ziegler Nichols tuning forconsecutive step inputs of 12cm, 20cm, 15cm

The PI controller designed for all the regions as

defined in table III is used to obtain the closed loopresponse shown in fig V.

IV.NEURAL NETWORKWe observed inaccurate control and large

overshoot in conventional controllers which could

cause instability for certain set values . To overcomethese drawbacks, we opted for Neuro Controller.

To implement Neuro Controller, we obtained real

time data by running the open loop system and trained the neural network with the inputs (set point and error)

and target output (percentage valve opening). We

designed the neural network in MATLAB using

‘nntool’ function and simulation block using ‘Gensim’

function. The Neuro controller designed in MATLAB

is called in LabVIEW using ‘mathscript’ function.

Fig VIII : Closed loop control using Neuro Controller for a set valueof 12cm, 20cm, 15cm

The controller called in LabVIEW is then

interfaced with the process to obtain the the closed

loop response shown in fig VIII.

V. FUZZY LOGIC CONTROLLERWe observed slow response in the neural

controller which we expected to overcome using FLC.

To design a fuzzy logic controller, we first

declared the universe of discourse for the input

variables (error and change in error) and the output

variable (percentage of valve opening). We defined the

membership functions and the rule base using the

Fuzzy toolbox in LabVIEW as shown in fig IX. The

Mamdani-type Fuzzy Inference System (FIS) is then

used to map the inputs and output based on the fuzzylogic.

Fig IX: Membership functions for inputs(error and change in error)and output(percentage valve opening) for FLC defined in LabVIEW

Fig X : Closed loop response curve using FLC for step input setvalues of 12cm, 20cm, 15cm

VI. REALTIME IMPLEMENTATION

Fig XI: Real time Hardware setup of the nonlinear horizontal tank

system

We implemented the Closed loop control using

PID controller and Fuzzy logic controller in real time

by interfacing the hardware of the process withLabVIEW environment in a computer using NI C-

DAQ cards as shown in fig XI. Since the system

transfer function is proved to be equal in simulation

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and real time, we used the same controllers designed

in simulation for the real time control.

Fig XII : Closed loop control of the real time process using PI

controller for a set value of 12cm

Fig XIII : Closed loop control of the real time process using FLC forstep input set values of 12cm, 20cm, 15cm

VII. RESULT:

The response of the closed loop system wasstudied and the drawback of conventional controller

and need for intelligent control is observed. The

performance of the controllers is compared using IAE.

Fig XIV: Comparison of the response of conventional controller,

neural controller and fuzzy logic controller for consecutive setvalues of 12cm, 20cm, 15cm.

The response of the controllers is observed and

compared as shown in fig XIV and the performance is

evaluated using IAE.

Rise

time

Settling

time

IAE

Zeigler

Nichols

240 1450 4458.7

Neural

Network

120 1250 3768.4

Fuzzy Logic

Controller

100 900 3557.8

Table IV : Comparison of the controller action based on the performance criteria – rise time, settling time, IAE

The Conventional controller showed goodresponse time and precision but had large overshoots.

Also, due to linearization of the non-linear process,

several PI controllers were used.

The Neural controller overcomes the above

drawbacks but the response had longer settling times.

The Fuzzy controller rules out linearization. Ithas good time domain characteristics. Theexperimental results show that the response has lesser

oscillations and absence of overshoot.

VIII. CONCLUSION:We concluded that the Fuzzy Logic Controller

outperforms both the conventional PID and Neural

controller, in the level control of a nonlinear horizontalcylindrical tank system.

REFERENCE [1] Satean Tunyasrirut, and Santi Wangnipparnt “Level

Control in Horizontal Tank by Fuzzy-PID Cascade

Controller” - World Academy of Science, Engineering

and Technology 25 -2007.[2] Tani. T, Umano. M, Sato. T, Tanaka. K,

"Application of neural network to tank level control of

petrochemical plants," Industrial Electronics, Control,

and Instrumentation, 1993. pp.321,326 vol.1, 15-19

Nov 1993

[3] J. G. Ziegler and N. B. Nichols, “Optimum settings

for automatic controller,” ASME Trans. Vol64, 1942,

pp.759-768 And “Automatic setting for AutomaticController”

[4] W. K. Ho, C. C. Hang, and J. H. Zhou,

“Performance and gain and phase margins of well-known PI Tuning formulas,” accepted for publication

in IEEE Trans. Contr. Syst. Technol., 1995.[5] Stephanopoulos G (1990) Chemical Process

Control, Prentice Hall, Englewood Cliffs, New Jersey,

chapter 16

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TIMA 2013