Control Engineering Practice 12 (2004) 23–30

Diagnosis of automotive electronic throttle control systems

R. Conatsera, J. Wagnerb,*, S. Gantab, I. Walkerb

a BWX Technologies, Y-12 National Security Complex, Oak Ridge, TN 37830, UKb Departments of Mechanical and Electrical Engineering, Clemson University, Automotive Research Laboratory, 212 Fluor Daniel Engineering Building,

Clemson, SC 29634, USA

Received 26 March 2002; accepted 23 October 2002

Abstract

During the past two decades, the automotive industry has been required to develop on-board health monitoring capabilities to

meet legislated diagnostic requirements for engine management systems. In this paper, real-time diagnostics are presented which

monitor the performance of an electronic throttle control system to detect and identify a suite of anomalies. The ETC system shall

be modeled and a parity diagnostic strategy applied to detect the presence of faults. The specific nature of the fault is isolated using a

parametric estimation methodology. Representative numerical results are presented and discussed to demonstrate the operational

performance of the ETC system and health monitoring algorithms.

r 2003 Elsevier Ltd. All rights reserved.

Keywords: Engine management; Modeling; Diagnostics; Failure detection; Parametric estimation

1. Introduction

Today’s automobile effectively encompasses the spiritof mechatronic systems with its abundant application ofelectronics, sensors, actuators, and microprocessor-based control systems to provide improved perfor-mance, fuel economy, emission levels, and safety. Theevolution of State and Federal legislation establishingstandards for corporate average fuel economy, tailpipeemissions, and on-board health monitoring systemshave helped to promote these mechatronic systems(e.g., CARB, 1997; Gertler et al., 1993). One majorthrust in automotive technology is the drive-by-wiresystem which replaces the direct mechanical linkageswith sensors and actuators under computer control (e.g.,Rossi, Tilli, & Tonielli, 2000; Jordan, 1999; Stanton &Marsden, 1997). These systems allow the traditionaldriver-to-vehicle interfaces to be redesigned and offerthe potential to supplement the driver’s commands. Theelectronic throttle control (ETC) system regulates thethrottle plate angle using a DC servo-motor to adjustthe inlet airflow rate (refer to Fig. 1).

The integration of ETC into existing engine manage-ment systems (e.g., intake manifold, fueling, combustionprocess, and rotational dynamics) is summarized inFig. 2. The driver’s commands and the throttle positionfeedback signal are processed by the ETC controller toregulate the servo-motor voltage (Huber, Lieberoth-Leden, Maisch, & Reppich, 1991). It is important toobserve that the ETC system must work in unison withthe existing engine control unit (ECU). The ECUregulates the spark and fuel delivery processes basedon the commanded throttle position, engine speed, andengine load. To maintain the engine speed at idle, an idleair control (IAC) circuit provides an airflow pathwayaround the closed throttle plate. Thus, the ETC systemoffers the opportunity to integrate IAC and throttleplate regulation into a single unit which increases theneed for trouble-free and dependable operation.

Traditional on-board diagnostic systems continuallymonitor vehicle operation and provide notification tothe driver in the form of warning lights when faultyperformance is detected (Rizzoni, Soliman, & Passino,1993). The introduction of microprocessor-baseddrive-by-wire systems allow for advanced ‘‘limp home’’modes and/or system shutdown to avoid catastrophicfailure (Box et al., 2000). To satisfy federal andstate regulations pertaining to on-board diagnostics,ETC systems must have health monitoring capabilities

ARTICLE IN PRESS

*Corresponding author. Tel.: +1-864-656-7376; fax: +1-864-656-

4435.

E-mail address: jwagner@clemson.edu (J. Wagner).

0967-0661/03/$ - see front matter r 2003 Elsevier Ltd. All rights reserved.

PII: S 0 9 6 7 - 0 6 6 1 ( 0 2 ) 0 0 2 8 1 - 2

to detect both soft and hard degradations that interferewith proper system operation. McKay, Nichols, andSchreurs (2000) presented a throttle-by-wire system with

integrated model-free diagnostics. Although the major-ity of current on-board automotive diagnostic algo-rithms may be categorized as model-free, opportunities

ARTICLE IN PRESS

Torque-Motor

Throttle Position Sensor

Mass Air Flow Sensor

Air Filter

AcceleratorPosition Sensor

Engine Sensors

Exhaust

Ignition system EGR valve

Canister purge Fuel pump

Engine ControlUnit

MAP

Electronic ThrottleControl

AirFlow

Electronic Throttle Body

Fig. 1. Electronic throttle control (ETC) schematic diagram.

Nomenclature

a plant process parameterAp throttle plate area (m2)b input process parameterbm motor damping constant (Nms/rad)bt throttle damping constantc input process parametere system error, residual vector (Rp�1)ECU engine control unitESC electronic spark controlETC electronic throttle controlea motor voltage (V)ET vector of process parameters#E Modeled vector of process parameters

f frequency (H)Fa force due to air on plate (N)IAC idle air controlia armature current (A)#ia Modeled system armature current (A)Jg throttle moment of inertia (kg m2)Jm motor inertia (kg m2)J equivalent moment of inertia (kg m2)Kb back emf constant (N m s/rad)Ksp throttle spring constant (N m/rad)Kt motor torque constant (N m/A)La armature inductance (H)MAP manifold air pressure (N/m2)N gear ratio, engine speed (rpm)p residual vector value (Rp�1)Patm atmospheric pressure (N/m2)

Pj physical process coefficientsPm manifold pressure (N/m2)Pu input residual (V)Py output residual (rad)Ra armature resistance (O)Raf focal point of airflow on plate (m)Rp throttle plate radius (m)t time (s)TPS throttle position sensorTa torque due to airflow (N m)Tg torque transmitted from gears (N m)TL load torque (N m)Tm torque applied by motor (N m)Tsp torque due to return spring (N m)u input signal (Rm�1)#u Modeled system input (Rm�1)x system state vector (Rn�1)#x Modeled system state vector (Rn�1)y system output vector (Rp�1)#y Modeled system output vector (Rp�1)Dpj change in physical process coefficientsDP pressure drop across plate (N/m2)Dt time step size (s)V loss functiony throttle plate angular position (rad)y0 pre-tension angle of spring (rad)ym armature angular position (rad)yd desired throttle plate angular position (rad)cT vector of the signals and their derivatives

R. Conatser et al. / Control Engineering Practice 12 (2004) 23–3024

exist to apply model-based strategies which incorpo-rate mathematical models for the physical system(Gertler, 1988). The model-based methods continuallycompare measured values of the system inputs/outputsto the respective modeled values to determine whetherthe system is functioning properly. Model-based meth-ods may detect faults in a more reliable manner viamicroprocessor-based algorithms rather than redundantsensors (Isermann, 1981). If a fault is detected, then theanomaly must be isolated to permit responsive mea-sures. In this research, a diagnostic system will bedesigned to detect and isolate failures in an electronicthrottle control system.

The paper is organized as follows. A behavioralmodel is presented for the electronic throttle controlsystem in Section 2. System health monitoring strategiesare introduced and discussed in Section 3 with thedesign of parity detection and parametric isolationmethodologies. In Section 4, representative numericalresults are presented which demonstrate the detectionand isolation of a suite of ETC failures. Finally, thesummary is presented in Section 5.

2. Electronic throttle control system model

A prototype servo-motor throttle body, as shownin Fig. 3, was selected to derive the behavioralmodel (Eriksson & Nielsen, 2000) for the systemhealth monitoring strategies. The electro-mechanicaldynamic system model, shown in Fig. 4, describesthe normal ‘‘no failure’’ ETC hardware opera-tion (Conatser & Wagner, 2000). The ETC systemuses a torque motor to rotate the throttle platebetween 0oyop=2 radians (i.e., closed to wide-open-throttle). The servo-motor is controlled bythe applied armature voltage, ea: The governing

differential equation for the armature current, ia;becomes

dia

dt¼

1

La

� ��Raia � Kb

dym

dtþ ea

� �; ð1Þ

where Ra and La represent the armature resistance andinductance, respectively. The back emf due to the motorrotation is Kb dym=dt

� �: The motor and throttle body’s

ARTICLE IN PRESS

Air Flow

FuelInjection

Engine

Control

Unit

Electronic

Throttle

Controller

Intake

Manifold

CombustionProcess

RotationalDynamics

ThrottleBody

DC Motor

ThrottleAir Flow Air Fuel Indicated

Torque

Speed

Position

Control And Diagnostics

MAPEngineSpeed

Engine Position

Inlet Temperature

TPS Port

Fuel

ESC

Motor VoltageCommand

Driver Pedal

Fig. 2. ETC integration into an automotive engine management system.

Fig. 3. Prototype of an electronic throttle control system.

Tf

Tairflow

Tspring

e a i a eb

Tg

R

a

L

a

TMotorTL

θ mθ

Fig. 4. Mechatronic system diagram for throttle-by-wire system.

R. Conatser et al. / Control Engineering Practice 12 (2004) 23–30 25

rotational dynamics may be described as

d2ym

dt2¼

1

Jm

� ��bm

dym

dt

� �� TL þ Tm

� �;

d2ydt2

¼1

Jg

� ��Tsp � bt

dydt

� �� Ta þ Tg

� �; ð2Þ

where the gear ratio is defined as N ¼ ðym=yÞ ¼ ðTg=TLÞ:The parameters bm and bt denote the motor shaft andthrottle viscous damping coefficients, respectively. Themotor torque may be expanded as Tm ¼ Ktia: Eqs. (1)and (2) may be reduced and expressed in terms of thethrottle body’s rotation as

dia

dt¼

1

La

� ��Raia � KbN

dydt

þ ea

� �; ð3Þ

d2ydt2

¼1

N2Jm þ Jg

� �

� � N2bm þ bt

� �dydt

þ NTm � Tsp � Ta

� �: ð4Þ

A stiff rotational spring returns the plate to a closedposition when no applied armature voltage is present.The spring assembly has been initialized to an angle y0;which produces a closing torque. The spring torque, Tsp;may be written as Tsp ¼ Kspðyþ y0Þ: The airflow overthe throttle plate induces a small torque, Ta; whosemagnitude may be expressed as Ta ¼ Raf Fa cos y whereRaf is the distance from the throttle plate center tothe force concentration point. The variable Fa denotesthe air force acting on the plate parallel to the airflow direction so that Fa ¼ DPAp cos y with DP ¼ðPatm � PmÞ and Ap ¼ pR2

p: The manifold pressureis a nonlinear throttle angle dependent function, Pm ¼f ðy;Patm;NÞ; that approaches atmospheric pressure asthe throttle approaches a wide-open state.

The system state and input vectors may be defined asx ¼ ½y;dy=dt; ia�T and u ¼ ½ea� such that the governingdifferential equations in state space become

’x1

’x2

’x3

264

375 ¼

0 1 0

�Ksp

J

�Kf

J

NKt

J

0�NKb

La

�Ra

La

266664

377775

x1

x2

x3

264

375

þ

0

�Kspy0

J�

R2pRaf p

JDP cos2x1

0

26664

37775

þ

0

0

1

La

26664

37775½u�; y ¼ ½x1�; ð5Þ

where J ¼ N2Jm þ Jg and Kf ¼ ðN2bm þ btÞ: The throt-tle plate’s angular position may be measured usinga rotational potentiometer sensor. The final task isthe introduction of a standard proportional-integral-derivative (PID) control algorithm to regulate theservo-motor. The desired throttle plate rotationaldisplacement, yd ; will be computed by the con-troller based on the driver and vehicle operatingconditions.

3. System health monitoring strategies

The four goals for a diagnostic system includedetection, isolation, estimation, and reconfigurationfor continued operation (Wagner & Shoureshi, 1992).The first goal, detection, is the ability of the diagnosticsto indicate that a fault/failure has occurred in thesystem. Second, the monitoring system must be able toestablish the exact location of the error; this is calledisolation. Third, estimation is the determination of themagnitude of the discrepancy followed by reconfigura-tion (or failure accommodation) for continued systemoperation. The reader is referred to representativeliterature on diagnostic systems by Willsky (1976),Satin and Gates (1977), Isermann (1984), and Frank(1990).

Initially, physical redundancy (i.e., presence of severalidentical system sensors) was the preferred manner ofobtaining the duplicate signals to compare (Deckert,Desai, Deyst, & Willsky, 1976). The advent of costeffective, high speed microprocessors has allowed theuse of mathematical models to estimate the neededsignals for analytical redundancy (Chow & Willsky,1984). These models are based on the numerical input/output system relationships and/or dynamic behavioralmodels. The measured signals from the operatingsystem are compared to the model-based estimatedsignals to produce a fault decision (Gertler &Singer, 1985). The advantage of analytical redundancyis the reduced hardware costs inherent in physicalredundancy; the drawback is the need for computingpower.

3.1. Failure detection

The nonlinear parity residual generation method usesforward and inverse system models to compare actualand modeled system input/output values to detectsystem faults (Krishnaswami & Rizzoni, 1994). Theforward system model computes estimated system out-puts, #y; from the actual inputs, u: The inverse modelcomputes estimated system inputs, #u; from the actualplant outputs, y: For the ETC system, the inverse modelcan be realized by relating the estimated input voltage tothe corresponding output throttle position so that the

ARTICLE IN PRESSR. Conatser et al. / Control Engineering Practice 12 (2004) 23–3026

estimated ETC armature current becomes

#ia ¼1

NKt

Jd2ydt2

þ Kspyþ ðN2bm þ btÞdydt

�

þKspy0 þ R2pRaf pDP cos2 y

i: ð6Þ

The estimated input voltage may be determined usingthe estimated current as

#u ¼ La

d#ia

dtþ Ra

#ia þ NKb

dydt: ð7Þ

The forward model developed in Section 2 predicts theestimated system outputs. The corresponding residuals,Pu ¼ ½u � #u� and Py ¼ ½y � #y�; are generated by compar-ing the values of the estimated inputs and outputs withthe measured values (Krishnaswami, Luh, & Rizzoni,1995). These residuals will have near-zero values duringnormal ‘‘fault free’’ operation (Gertler & Singer, 1990).A component is considered degraded when its residualvalue surpasses a preset threshold level. After detectingthe fault, the next step is fault isolation. A parametricestimation strategy has been selected since the presenceof a single output precludes diagnosis from parityrelations.

3.2. Failure isolation

The failure detection strategy derives its faultinformation by comparing the inputs and outputs ofthe measured system to those of the modeled system.The results may provide a ‘‘fault’’ or ‘‘no fault’’statement for the overall plant. The parametric estima-tion failure isolation method leverages specific plantparameter magnitude changes (Isermann & Freyermuth,1991). Consider the system description

y þ a1dy

dtþ a2

d2y

dt2þ?þ an

dny

dtn

¼ b1u þ b2du

dtþ b3

d2u

dt2þ?þ bmþ1

dmu

dtm; ð8Þ

where u; du=dt; d2u=dt2;y denotes the system inputand its respective derivatives, and y; dy=dt; d2y=dt2;yrepresents the system output and its derivatives. Theprocess parameters, ET ¼ ½a1; a2;y; anjb1; b2;y; bmþ1�;denote relationships between the process coefficients. Ifthe process coefficients that indicate faults are notdirectly measurable, then their changes can often bedetermined by the changes in the process parameters(Young, 1981).

A five-step method is presented by Isermann (1984) todetermine these coefficients:

1. Establish the process equation for the measurableinput and output variables: yðtÞ ¼ f ðuðtÞ;EiÞ or

yðtÞ ¼ cTE þ e where

cT tð Þ ¼ �dny

dtn;�

dn�1y

dtn�1;y;�

dy

dtjdmu

dtm;dm�1u

dtm�1;y;

du

dt; u

�

ð9Þ

and e represents the discrepancies between theestimated and actual system behavior.

2. Determine the relationship between the processparameters, Ei; and the physical process coefficients,pj : Ei ¼ gðpjÞ:

3. Estimate the process parameters as a result ofmeasurements of the signals y and u:

4. Calculate the process coefficients and determine theirchanges, Dpj ; as pj ¼ g�1ðEiÞ:

5. Diagnosis of the symptoms based on processcoefficient changes and statistical methods.

The process parameter values may be computed fromthe plant’s input/output relationships using regressionanalysis; process coefficients may be determined fromthese estimated process parameters. By minimizing theloss function, V ¼

PNk¼0 e2 ¼ eTe; and solving for the

vector of process parameters, E; the estimated para-meters become the nonrecursive least squares estimationequation #E ¼ ½cTc��1cTy:

The parametric estimation method requires thesystem differential equations to estimate the variouscoefficients. The input and output for the ETC systemare u ¼ ea and y ¼ y: To achieve a relationship similarto (8), the second row of Eq. (5) may be examined with

’x2 ¼d2y

dt2¼

1

J�Kspx1 � Kf x2 þ NKtx3 � Kspy0

�

�R2pRaf pDP cos2 x1

�: ð10Þ

To simplify the analysis, the motor armature induc-tance, La; will be assumed negligible so that Eq. (10)may be rewritten in terms of y as

y þ a1dy

dtþ a2

d2y

dt2¼ b1u � c1DP cos2 y � y0; ð11Þ

where the process parameters, ET ¼ ½a1; a2jb1; c1; y0�; aredefined as a1 ¼ ðKf =Ksp þ KtN

2Kb=KspRaÞ; a2 ¼ðJ=KspÞ; b1 ¼ NKt=KspRa; and c1 ¼ R2

pRaf p=Ksp: Theinductance was not neglected previously to maintain thedetection sensitivity. The process parameter c1 will notbe considered since it does not contribute significantly tothe fault isolation process. The process coefficients ofspecific interest are bm; bt; Ksp; Kt; Kb; and Ra: Theremaining coefficients (i.e., J; N; Rp; Raf ) are assumedconstant. The process parameters are estimated usingthe input/output relationship in Eq. (11) with a leastsquares method. The process coefficients are notdetermined per step (4) due to the many unknowns(i.e., 6) and few relationships (i.e., 4). Rather, theprocess parameters from step (3) are directly utilized to

ARTICLE IN PRESSR. Conatser et al. / Control Engineering Practice 12 (2004) 23–30 27

isolate failures. Finally, the diagnoses of symptoms instep (5) are inferred from changes in the processparameters.

4. Numerical results

The engine control unit for an internal combustionengine receives input from many engine sensors (e.g.manifold air pressure, manifold temperature, enginespeed, etc.) to control the fuel and spark deliveryprocesses. For the application at hand, the primaryfocus is the ETC dynamics. The maximum supplyvoltage is 12 VDC. A Matlab/Simulink1 simulation hasbeen created and executed for t ¼ 20:0 s using a time-step of Dt ¼ 1:0�e�3 s. White noise has been introducedto assess the diagnostic strategy when noise corrupts theplant measurements. The simulation parameters arelisted in Table 1. The throttle plate follows a sinusoidalposition of amplitude 0pypp=2 and frequencyf ¼ 0:17 H. The simulation of the ‘‘fault free’’ ETCplant yielded the results shown in Fig. 5. The inputresidual ranged between 70.35 V and the outputresidual between 70.035 (radians). The throttle platesuccessfully tracks the desired trajectory with ECUoutput voltage ranging from 1.33 to 8.06 V.

A suite of failures, listed in Table 2, are nowintroduced to explore the detection and isolationcapabilities of the diagnostic strategies. The systemfailures were introduced by manipulating the corre-sponding model parameters at t ¼ 10:0 s during thesimulation. Notice that Faults 1, 2, and 5 involve motorfaults, and the remaining faults involve ETC throttlebody hardware. Adjustable thresholds may be estab-lished for the residuals to declare a failure once exceededfor a specified time period. To assess whether thresholdshave been exceeded by the residuals, the absolute valueof the residuals are integrated for fixed intervals of time(or moving ‘‘windows’’) and compared to preset thresh-olds for fault detection. This minimizes false alarmsarising from residuals momentarily surpassing presetthresholds. In the event of a detected fault, relevantplant data is continually collected and processed by theparametric estimation algorithm. The time period forprocess parameter collection and analysis is dependanton the accuracy required.

Fig. 6 shows the results from the simulation of theETC plant when Fault 1 (i.e., motor torque coefficient)of 10% magnitude decrease was introduced. The spikein the ECU output voltage is due to the increaseddemand on the motor (i.e., greater current draw) tocompensate for the reduced motor torque generated.The ECU voltage, required to track the output after theanomaly was introduced, ranged from 1.5 to 8.9 V. The

input residual displays the discrepancy between theactual and ideal ECU voltages required to trackthe position; the output residual shows the differencebetween the actual and ideal plant outputs for the ECUinput plant voltage. The diagnostic software successfullynoted the system fault as evident by the non-zeroresiduals. In this case, the magnitudes of input andoutput residuals ranged from 0.061 to 0.97 V and �0.39to �0.23 (radians), respectively. The system behaviordue to Fault 2 (i.e., motor resistance) was similar to thatof Fault 1 since the ECU voltage magnitude increased tothe plant compensate for the reduction in the armaturecurrent.

The effect of Fault 3 (i.e., throttle damping) for a 25%magnitude increase is shown in Fig. 7. It can be noticedthat the ECU voltage range, 1.1–8.25 V, increases toovercome the resisting throttle damping force. The inputand output residuals ranged from �0.45 to 0.36 V and�0.079 to 0.0841 rad, respectively. Note that the fault’simpact at the system output response is near negligible,but is reflected in the residuals. The simulation results ofFault 4 (i.e., motor damping) were similar to Fault 3since an increase in the resisting damping forcenecessitates a similar system behavior. The introductionof Fault 5 resulted in residual variations similar to the

ARTICLE IN PRESS

Table 1

Summary of electronic throttle control system model parameter values

Parameter Value Units Parameter Value Units

bm 0.03 N m s/rad La 0.003 H

bt 3.397e�3 N m s/rad N 4 —

Jg 0.005 kg m2 Patm 1.01325e5 N/m2

Jm 0.001 kg m2 Ra 1.9 OKb 0.1051 V s/rad Raf 0.002 m

Ksp 0.4316 N m/rad Rp 0.0015 m

Kt 0.1045 N m/A y0 p=2 rad

Fig. 5. Fault free simulation of electronic throttle control plant.

1 Registered trademarks of The MathWorks, Natick, MA, 2002.

R. Conatser et al. / Control Engineering Practice 12 (2004) 23–3028

previous two cases with the exception of the phase angle,due to the fact that the ECU voltage range actuallydecreases to maintain the torque generated at thedesired levels. Finally, the effect of Fault 6 (i.e., return

spring) for a 10% reduction in stiffness can be observedin Fig. 8. The reduced resisting spring stiffness requiresless torque to rotate the throttle plate to the desiredposition. This is reflected by the reduction in the ECUvoltage, which ranged from 0.88 to 7.53 V. The inputand output residuals varied from �0.78 to �0.2 V and0.2–0.3 rad, respectively.

The process parameters are defined in terms ofprocess coefficients; thus, the coefficient value changeshave a direct effect on the process parameter values.However, the process coefficients cannot be uniquelyestimated since the number of unknown coefficients ismore than the number of available relationships (i.e.,process parameters). Hence, the estimated processparameters enable only a qualitative decision makingprocess. Specific ‘‘signature’’ magnitude variationsbetween the ideal and the estimated process parametersmay be evaluated since they are indicative of specificanomalies present in the system (refer to Table 2).Such empirical observations are made based on therelationships between process parameters and processcoefficients. For example, when Fault 1 was intro-duced into ETC system, the magnitudes of a1 andb1 decreased while a2 remained unaltered. This is

ARTICLE IN PRESS

Table 2

ETC system faults, parameter changes, and fault decision table

Parameter values

No. Description Parameter Magnitude (%) Da1 Da2 Db1 Fault condition

1 Motor torque coeff. Kt 10 — — if: Da1o0; Db1o0

2 Motor resistance Ra 10 — — if: Da1o0; Db1o0

3 Throttle damping bt 25 + if: Da1 > 0

4 Motor damping bm 25 + if: Da1 > 0

5 Back EMF Kb 30 — if: Da1o0

6 Return spring Ksp 10 + + + if: Dai > 0; b1 > 0

Fig. 6. ETC plant with Fault 1 of 10% magnitude change introduced

at t ¼ 10:0 s.

Fig. 7. ETC plant with Fault 3 of 25% magnitude change introduced

at t ¼ 10:0 s.

Fig. 8. ETC plant with Fault 6 of 10% magnitude change introduced

at t ¼ 10:0 s.

R. Conatser et al. / Control Engineering Practice 12 (2004) 23–30 29

because process parameters a1 and b1 are directlyproportional to the torque constant, whereas a2 isunrelated to it. Table 2 summarizes the qualitativevariations of the process parameters for each fault. Asshown four fault signatures may be isolated. Specifically,a reduction in the magnitude of process parameters a1

and b1; with a2 staying unaltered, would be indicativeof either Fault 1 or 2. Similarly, an increase inmagnitude for a1 with a2 and b1 remaining unchangedwould indicate the presence of either Fault 3 or 4. Areduction in the magnitude of only a1 would beindicative of Fault 5. Finally, an increase in themagnitudes of all the process parameters would indi-cate the presence of Fault 6.

5. Summary

Diagnostic systems detect, identify, and estimate themagnitude of anomalies such that corrective measurescan be applied to permit continued operation. In thispaper, a real-time health monitoring strategy has beendesigned for an automotive electronic throttle controlsystem. This is an important topic since drive-by-wiretechnology requires the existence of on-board diagnos-tics to satisfy legislated requirements and ensure propersubsystem functionality. A mathematical behavioralmodel was presented with a parity failure detectionand isolation strategy. Representative numerical resultswere presented and discussed which demonstrated theperformance of the health monitoring algorithms indiagnosing a suite of system failures.

References

Box, G., Graves, S., Bisgaard, S., Van Golder, J., James, J., Marko,

K., Seifer, M., Poublon, M., & Fodale, F. (2000). Detecting

Malfunctions in Dynamic Systems. SAE paper no. 2000-01-0363.

CARB: State of California Air Resources Board. (1997). Malfunction

and Diagnostic System Requirements—1994 and Subsequent

Model-Year Passenger Cars, Light-Duty Trucks, and Medium-

Duty Vehicles and Engines. OBD-II, with modifications effective as

of September 25, 1997, Sec. 1986.1 of Title 13, California Code of

Regulations, Sacramento, CA.

Chow, E. Y., & Willsky, A. S. (1984). Analytical redundancy and the

design of robust failure detection systems. Automatica, AC-29(7),

603–614.

Conatser, R., Wagner, J. (2000). Health monitoring for automotive

electronic throttle control systems. Proceedings of the seventh

mechatronics forum international conference, Atlanta, GA.

Deckert, J. C., Desai, M. N., Deyst, J. J., & Willsky, A. S. (1976). F-8

sensor failure identification using analytic redundancy. Transac-

tions on Automatic Control, AC-22(5), 795–803.

Eriksson, L., Nielsen, L. (2000). Non-Linear Model-Based Throttle

Control. SAE paper no. 2000-01-0261.

Frank, P.M. (1990). Fault diagnosis in dynamic systems using

analytical and knowledge-based redundancy—a survey and some

new results. IFAC symposium on advanced information processing in

automatic control, Nancy, France, pp. 457–459.

Gertler, J. J. (1988). Survey of model-based failure detection and

isolation in complex plants. IEEE Control Systems Magazine, 8(9),

3–10.

Gertler, J. J., Costin, M., Fang, X., Hira, R., Kowalczuk, Z., & Luo,

Q. (1993). Model-based on-board fault detection and diagnosis for

automotive engines. Control Engineering Practice, 1(1), 3–17.

Gertler, J.J., Singer, D. (1985). Augmented models for statistical fault

isolation in complex dynamic systems. Proceeding of the American

control conference, Boston, MA, pp. 317–322.

Gertler, J. J., & Singer, D. (1990). A new structural framework for

parity equation-based failure detection and isolation. Automatica,

26(2), 381–388.

Huber, W., Lieberoth-Leden, B., Maisch, W., & Reppich, A. (1991).

Electronic throttle control. Automotive Engineering, 99(6), 15–18.

Isermann, R. (1981). Fault Detection Methods for the Supervision of

Technical Processes. Process Automation, 1, 36–44.

Isermann, R. (1984). Process fault detection based on modeling and

estimation methods—A survey. Automatica, 20(4), 387–404.

Isermann, R., & Freyermuth, B. (1991). Process fault diagnosis based

on process model knowledge—Part I: Case study experiments.

ASME Journal of Dynamic Systems, Measurement and Control,

113(4), 620–626.

Jordan, M. (1999). Drive-by-wire will end the era of the handbrake

turn. Electronic Engineering (London), 71(875), 28–30.

Krishnaswami, V., Luh, G. C., & Rizzoni, G. (1995). Nonlinear parity

equation based residual generation for diagnosis of automotive

engine faults. Control Engineering Practice, 3(10), 1385–1392.

Krishnaswami, V., Rizzoni, G. (1994). Robust residual generation for

nonlinear system fault detection and isolation. Proceedings of the

IFAC/IMACS symposium on fault detection, supervision and safety

for technical processes—SAFEPROCESS ‘94, pp. 317–322.

McKay, D., Nichols, G., Schreurs, B. (2000). Delphi Electronic

Throttle Control Systems for Model Year 2000; Driver Features,

System Security, and OEM Benefits. ETC for the Mass Market,

SAE paper no. 2000-01-0556.

Rizzoni, G., Soliman, A., Passino, K. (1993). A Survey of Automotive

Diagnostic Equipment and Procedures. SAE paper no. 930769.

Rossi, C., Tilli, A., & Tonielli, A. (2000). Robust control of a throttle

body for drive by wire operation of automotive engines. IEEE

Transactions on Control Systems Technology, 8(6), 993–1002.

Satin, A. L., & Gates, R. L. (1977). Evaluation of parity equations for

Gyro failure detection and isolation. Journal of Guidance and

Control, 1(1), 14–20.

Stanton, N., & Marsden, P. (1997). Drive-by-wire systems: Some

reflections on the trend to automate the driver role. Proceedings of

the institution of mechanical engineers. Part D: Journal of

Automobile Engineering, 211(D4), 267–276.

Wagner, J., & Shoureshi, R. (1992). Failure detection diagnostics for

thermofluid systems. Journal of Dynamic Systems, Measurement

and Control, 114(4), 699–706.

Willsky, A. S. (1976). A survey of design methods for failure detection

in dynamic systems. Automatica, 12(6), 601–611.

Young, P. (1981). Parameter estimation for continuous-time models—

a survey. Automatica, 17(1), 23–39.

ARTICLE IN PRESSR. Conatser et al. / Control Engineering Practice 12 (2004) 23–3030