Engineering Applications of Artificial Intelligence 16 (2003) 717–721

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doi:10.1016/S095

The control of highway tunnel ventilation using fuzzy logic

Erc .ument Karaka-s*

Department of Electrical Education, University of Kocaeli, Izmit 41100, Kocaeli, Turkey

Abstract

The purpose of tunnel ventilation control is to provide a safe and comfortable environment for users. The tunnel ventilation is

optimized by controlling jet fans and dust collectors installed inside the tunnel. The jet fans blow polluted air from inside the tunnel

toward air exit ports. The dust collectors remove soot and smoke so that pollutant concentration inside the tunnel can be better

measured by CO (carbon monoxide) meters. Since this is a process involving many elements which are difficult to quantify exactly,

the predictive fuzzy control is introduced to solve the problem. By means of this approach it was made possible to reduce electric

power consumption greatly while keeping the degree of pollution within the allowable limit.

r 2003 Elsevier Ltd. All rights reserved.

Keywords: FLC; Jet fan tunnel; Ventilation; Induction motor

1. Introduction

The necessary and sufficient amount of airflowventilated in different traffic conditions in a tunnel mustbe provided with a minimum electric power consump-tion. In particular, increasing the ventilation efficiencyin tunnels is important to reduce the operating costs.

As stated in Chen et al. (1998), moving vehicles withspeeds of 80 km/h generally take about 8–10min to passsafely along a large road tunnel with about 10 km inlength. Such a long driving duration in those kind oflarge tunnels would require a satisfied environment toavoid foreseeable hazards that of air pollution is mainlyrelated to either vehicle driver or passengers. Therefore,air pollution control in a tunnel ventilation system is ahot topic especially in case of any traffic congestion. Theair in tunnel, usually contaminated by CO, HC anddust, will reduce the visibility and more seriously causetraffic accidents accordingly. The air pollution controlaims (Iokibe et al., 1993):

* To increase the visibility so that the visibility indexVIX40%. Actual control scheme regulates VIX50%for safety reasons.

* To decrease the concentration of carbon monoxidedenoted by CO so that COo100 ppm. Actual controlscheme regulates COo40 ppm.

2-3249910; fax: +90-262-3313909.

ss: karakas@kou.edu.tr (E. Karaka-s).

see front matter r 2003 Elsevier Ltd. All rights reserved.

2-1976(03)00068-X

* To minimize electrical power consumption for costeffectiveness.

An outline of the tunnel ventilation control systemusing artificial intelligence is given in Nagataki et al.(1992), which was remodeled and a thorough ventilationsystem including plant, dynamics, fuzzy logic control,simulation (Tamura and Matsushita, 1991; Funabashiet al., 1991) and evaluation was developed in Chen et al.(1998).

In the following sections, tunnel ventilation systemconfiguration (TVSC) and fuzzy logic control (FLC) areexplained, and then an FLC application in TVSC isintroduced. Modeling and simulation of this newapproach is followed by simulation results. This studyfocuses on the jet fan torque of the ventilation systemof a highway tunnel whose specifications are given inTable 1 using Matlab-Simulink, and the results arecompared with those of obtained using the PIDcontroller.

2. Tunnel ventilation system configuration

A hypothetical highway tunnel with specifications asstated in Table 1 is considered to apply the predictivefuzzy control technique that is explained in the followingsection. The parameters given in the table are used tospecify power consumption, lighting, emergency control

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Table 1

Tunnel specifications

Right carriageway (uphill tube) 3311m

Left carriageway (downhill tube) 3363m

Max. gradient 2%

Number of lanes per tube 3

Max. permissible traffic speed 90 km/h

Rule BaseFuzzification Defuzzification

Control Rules Judgement

Data Base

error and change of error

Syst. Math. Model

U e

u E

de DE

R

E. Karaka-s / Engineering Applications of Artificial Intelligence 16 (2003) 717–721718

and radio control as well as ventilation control of thetunnel.

Emissions from cars are dependent not only on theway they are built but also on the way they are drivenin various traffic situations. In a highway tunnelatmosphere, various gases are emitted by combustionengines. They consist largely of nitrogen (N2), carbondioxide (CO2), steam (H2O) and other particles. Inaddition, a number of harmful substances are presentsuch as hydrocarbons (HC), carbon monoxide (CO),lead and sulfur dioxide (SO2). Because of thesedangerous gases, it is necessary to provide fresh air inlonger tunnels. The fresh air which is used to lower theconcentration of CO also serves to improve visibility.

The purpose of ventilation is to reduce the noxiousfumes in a tunnel to a bearable amount by introducingfresh air. Every tunnel has some degree of naturalventilation. But a mechanical ventilation system shouldhave to be also installed in order to improve theventilation to a non-harmful condition. In order tocreate air stream, fans are installed on the ceiling or side-walls of the tunnel. The fans take air in tunnel and blowit out at higher speed along the axis of the tunnel. It isassumed that 16 jet fans in the right carriageway and 6jet fans in the left carriageway are mounted in the tunnelwith 50Hp (Horse-power) each.

It is further assumed that in each tunnel tube of cross-sectional area 144m2, two locations are utilized formeasuring the carbon monoxide, dust particle concen-tration and traffic volume at entrance and exit of thetunnel. From the measuring units all essential dataobtained can be transmitted to the ventilation controlsystem for further processing.

Fig. 1. Performance of a fuzzy controller.

Output DataInference Processing Input Data

Carbon Monoxide

Traffic Volume

Visibility Index PollutionPrediction

Judgment Rule Inference

Control Instructions

Fig. 2. Relationship among control functions in a tunnel ventilation

control system.

3. Fuzzy logic control (FLC)

The design and industrial implementation of anautomatic control system require use of efficienttechniques. In order to solve a control problem it isnecessary to first describe the dynamic behaviour of thesystem to be controlled. Traditionally this is done interms of a mathematical model. However, it is wellknown that mathematical modeling of a plant is alwaysforced with the problem of uncertainty. There is alwaysa discrepancy between the mathematical model of aplant, which can be very accurate but at a price, and the

plant’s actual behaviour. The control engineer has tofind the simplest and the cheapest solution which fulfilsthe performance requirements in the face of the existingmodeling uncertainties of the plant.

The structure of the controller used in this work isshown in Fig. 1. The aim is to maintain a single process-state variable defined Bose (1994) at set point. Thecontroller is a fuzzy logic controller (FLC) with theinputs being the error and change of error, and itsoutput being the required change in the controllervariable.

The control logic structure is shown in Fig. 2. Asdepicted in Fig. 2, the logic structure of FLC systemproposed consists of three sub-systems, namely, inputdata, inference processing and output data. Trafficvolume (TV), visibility index (VI) and carbon monoxide(CO) are the three basic measurable input parameterswhich are fed into the inference processing sub-system todetermine pollution prediction (PP), make judgment (J)and so propose the required rule inference (RI). Finally,control instructions (CI) are extracted as the output dataof the control system. For the present study, 49 if–thenrules are developed and used in judgment process.

Inputs of the fuzzy control system are VI that includesdust and smoke penetration rate, and carbon monoxideratio (CO) in air, and traffic volume that is measured bya traffic counter placed at highway entrance ramps.Output is control instruction for ventilation equipmentthat includes jet fans and dust-removal units. First of allVI and CO values are measured, and the degree ofpollution inside tunnel is predicted. Finally, trafficvolume is measured and the effect of airflow driven bypassing vehicles on degree of pollution is predicted and

ARTICLE IN PRESSE. Karaka-s / Engineering Applications of Artificial Intelligence 16 (2003) 717–721 719

control instruction is sent. Control range that includesinputs and outputs is well covered with seven member-ship functions.

Table 2

Parameters of the induction motor model

Rs (O) 0.0870

Rr (O) 0.2280

L1s (H) 0.0008

L1r (H) 0.0008

Lm (H) 0.0346

P 4

J (kgm2) 1.662

P (Hp) 50

Ten (Nm) 234.395

able 3

uzzy controller rules

(k) De(k)

PB PM PS Z NS NM NB

B NB NB NB NB NM NS Z

M NM NM NM NS NS Z Z

4. Simulation results

The block diagram of the proposed adaptive fuzzycontroller model for an induction jet fan motor drive isshown in Fig. 3 which consists of the induction jet fanmotor driven by an PWM inverter, a dynamic load, andthe fuzzy controller.

In order to validate the control strategies as discussedabove, simulation studies were carried out. Table 2shows the parameters of the drive system used in thesimulation model, where Rs is the stator resistance, Rr

the rotor resistance, L1s the stator self-inductance, L1r

the rotor self-inductance, Lm the equivalent self-induc-tance due to heat dissipation in motor, p the number ofpoles of stator, J the inertia of rotor, P the power ofinduction motor, and Ten the motor nominal loadtorque. These parameters are used to model theinduction motor and its nominal load torque (Ten)representing all of the ventilation system input variables.

The meanings of the parameters utilized in Fig. 3 areas follows:

Wr is the rotor speed (rad/s), Ias; Ibs; Ics the nominalmotor currents (A), Te the motor torque (Nm).Mathematical model of induction motor shown inFig. 3 further has the following parameters: R1, R2,L1, L2, Lm, Lt, P and J as mentioned above.

The derivation of the fuzzy control rules is based onTable 3 and seven examples of these criteria (total of7� 7 ¼ 49 rules) are as follows:

S NM NB Z Z Z PS PB

Z Z Z Z Z Z Z

S NS NS Z NM NM NM NB

M Z NS NS NS NM NM NM

B NB Z NS NS NS NM NM

* If error (e(k)) is Positive Big (PB) and change of error(De(k)) is PB then fs is NB.

* If error (e(k)) is Positive Medium (PM) and change oferror (De(k)) is PM then fs is NM.

fsfs

+-

Sum

157.0796

wref

VmK-

Vm/fs

Sin.Source

InductionMathematic

Control Block ofFuzzy Logic

Fig. 3. Fuzzy controller model for an

* If error (e(k)) is Positive Small (PS) and change oferror (De(k)) is PS then fs is Z.

* If error (e(k)) is Zero (Z) and change of error (De(k))is Z then fs is Z.

* If error (e(k)) is Negative Small (NS) and change oferror (De(k)) is NS then fs is NM.

* If error (e(k)) is Negative Medium (NM) and changeof error (De(k)) is NM then fs is NM.

* If error (e(k)) is Negative Big (NB) and change oferror (De(k)) is NB then fs is NM.

Here e(k) is the input error coming from the differencebetween angular velocity and reference angular velocity(wref) of the jet fan and fs is the stator frequency. Fig. 4illustrates the membership function of e(k). De(k) is the

T

F

e

P

P

P

Z

N

N

N

fs

w

Mux

Mux1

Motoral Model

TeTe

MechanicalShaft

ww

ibsibs

iasias

icsics

Load

isa

Te_

157.0796wref

induction jet fan motor drive.

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1

0

0.5

-157 -104 -52 0 52 104 157

nb nm ns z ps pm pb

e (k)

Fig. 4. Error in controller inputs eðkÞ:

1

0

0.5

-6 -4 -2 0 2 4 6

nb nm ns z ps pm pb

∆e (k)

Fig. 5. Change of error in controller inputs DeðkÞ:

Table 4

Outcomes obtained comparing Figs. 6–9

Parameters PID FLC

Triangular

mf

Trapezoidal

mf

Bell-shaped

mf

DT (Nm) 246,500 376,000 346,153 342,857

Dty (s) 2740 0394 0304 0308

DTk (Nm) 227,272 232,000 251,908 237,735

ts (s) 0470 0310 0330 0317

tk (s) 3410 0761 0761 0736

DT: change in torque; Dty: change in time to steady state after the load

is applied; DTk: change in torque to steady state after the load is

applied; ts: osscilation time when the motor is started (without load);

tk: time to steady state after the motor is started (without load); mf:

membership function.

∆ty

∆Tk

1 2 3 4 5 6 7

100

200

300

400

500

600

700

800

t (s)

ts

∆tk∆T

Tor

que

(Nm

)

∆T = 246,500 Nm∆Tk = 227,272 Nm∆ty = 2,740 s

Fig. 6. Torque step response of drive system under load obtained with

PID controller developed for the highway tunnel.

Tor

que

(Nm

)

∆T

∆Tk∆ty

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.00

200

400

600

800

1000

1200

t (s)

ts

tk

∆T = 376 Nm

∆Tk = 232 Nm∆ty = 0,394 s

ig. 7. Torque step response of drive system under load obtained by

LC developed for the highway tunnel (when the membership function

triangular).

E. Karaka-s / Engineering Applications of Artificial Intelligence 16 (2003) 717–721720

change in difference between the consecutive samplings(Fig. 5):

eðkÞ ¼ wref ðkÞ � wðkÞ; ð1Þ

DeðkÞ ¼ eðkÞ � eðk � 1Þ; ð2Þ

wðkÞ ¼ 2�fs; ð3Þ

P ¼ TenwðkÞ: ð4Þ

According to the above criteria and after controllingseveral times by conventional controllers, fuzzy algo-rithm is derived and expressed in Table 3 which gives theinferred linguistic values of ‘‘De(k)’’ and ‘‘e(k)’’. Eachuniverse of discourse is divided into seven fuzzy subsets:PB, PM, PS, Z, NS, NM, and NB.

Fig. 7 shows the simulation results of torquevariations against time for the drive system run withFLC where the membership function is triangular.Results under the same working conditions for PIDcontroller are presented in Fig. 6. By comparing thebehaviors depicted in Figs. 6 and 7 the robustness of theFLC system can be recognized. From the closeinspection of these figures several outcomes can beextracted as shown in Table 4 which also includes resultswhen the membership function of FLC is trapezoidaland bell-shaped. As can be easily seen, ts value of FLCwith triangular membership function is about 66% lessthan that obtained by PID. When moment reaches zeroit implies that rotor angular velocity attains either zeroor constant value, the so-called synchronous velocity. In

F

F

is

other words, steady-state starting time can be acceptedas a measure of electrical energy consumption of thecontrol system due to the fact that energy and time aredirectly proportional quantities.

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orqu

e (

Nm

)

0

200

400

600

800

1000

1200

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

t (s)

∆Tk∆ty

ts

tk ∆T

∆T = 346,153 Nm∆Tk = 251,908 Nm∆ty = 0,304 s

Fig. 8. Torque step response of drive system under load obtained by

FLC developed for the highway tunnel (when the membership function

is trapezoidal).

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

t (s)

Tor

que

(N

m)

0

200

100

400

300

600

500

800

700

900

∆Tk∆ty

ts

tk

∆T

∆T = 342,857 Nm∆Tk = 237,735 Nm∆ty = 0,308 s

Fig. 9. Torque step response of drive system under load obtained by

FLC developed for the highway tunnel (when the membership function

is bell-shaped).

E. Karaka-s / Engineering Applications of Artificial Intelligence 16 (2003) 717–721 721

5. Conclusion

Ventilation control is based on sensor information,namely TV, VI and CO measurements. According to theamounts of pollutants in exhaust gas, airflow driven bythe vehicles and degree of pollution inside the tunnel,optimized operation commands are given to the jet fansand dust collectors. ‘‘Optimum’’ means that pollutantconcentration is kept within the allowable limit (for CO,100 ppm or less), and at the same time electric powerconsumption is minimized as shown in Figs. 7–9 (i.e.,where an FLC is used) comparing with Fig. 6 (i.e., wherea PID is used) in terms of steady-state starting time. Ascan be concluded from Figs. 7 and 6 and Table 4, tk forthe model where FLC is applied is 4,48 times less thanthat of the PID approach. Therefore, the powerconsumption required in the former is much lesscompared to the latter. In the past, this control wasperformed using a quantitative numerical model; but themodel as opposed to our approach failed to accountaccurately for a number of phenomena includingturbulence inside the tunnel and emission of pollutants

from vehicles, making it difficult to obtain optimumoperation in which the electrical power consumption isminimized.

References

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applications in power electronics and motion control. Proceedings

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Chen, P.H., Lai, J.H., Lin, C.T., 1998. Application of Fuzzy Control

to a Road Tunnel Ventilation System, Fuzzy Sets and Systems.

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