Intelligent Control and Automation, 2011, 2, 186-195
doi:10.4236/ica.2011.23023 Published Online August 2011 (http://www.SciRP.org/journal/ica)
An Antilock-Braking Systems (ABS) Control:
A Technical Review
Ayman A. Aly1,2, El-Shafei Zeidan1,3, Ah me d Hamed1,3, Farhan Salem1
1Department of Mechanical Engineering, Faculty of Engineering, Taif University, Al-Haweiah, Saudi Arabia
2Department of Mechanical Engineering, Faculty of Engineering, Assiut University, Assiut, Egypt
3Department of Mechanical Power Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
Received April 23, 2011; revised May 18, 2011; accepted May 25, 2011
Abstract
Many different control methods for ABS systems have been developed. These methods differ in their theo-
retical basis and performance under the changes of road conditions. The present review is a part of research
project entitled “Intelligent Antilock Brake System Design for Road-Surfaces of Saudi Arabia”. In the present
paper we review the methods used in the design of ABS systems. We highlight the main difficulties and
summarize the more recent developments in their control techniques. Intelligent control systems like fuzzy
control can be used in ABS control to emulate the qualitative aspects of human knowledge with several ad-
vantages such as robustness, universal approximation theorem and rule-based algorithms.
Keywords: ABS, Intelligent Control, Fuzzy Control
1. Introduction
Since the development of the first motor driven vehicle
in 1769 and the occurrence of first driving accident in
1770, engineers were determined to reduce driving acci-
dents and improve the safety of vehicles [1]. It is obvious
that efficient design of braking systems is to reduce acci-
dents. Vehicle experts have developed this field through
the invention of the first mechanical antilock-braking
system (ABS) system which have been designed and
produced in aerospace industry in 1930 [2,3].
In 1945, the first set of ABS brakes w ere put on a Boeing
B-47 to prevent spin outs and tires from blowing and
later in the 1950s, ABS brakes were commonly installed
in airplanes [4,5]. Soon after, in the 1960s, high end
automobiles were fitted with rear-only ABS, and with the
rapid progress of microcomputers and electronics tech-
nologies, the trend exploded in the 1980s. Today, all-
wheel ABS can be found on the majority of late model
vehicles and even on select motorcycles [6-10].
ABS is recognized as an important contribution to
road safety as it is designed to keep a vehicle steerable
and stable during heavy braking moments by preventing
wheel lock. It is well known that wheels will slip and
lockup during severe braking or when braking on a slip-
pery (wet, icy, etc.) road surface. This usually causes a
long stopping distance and sometimes the vehicle will
lose steering stability [11-13]. Th e objective of ABS is to
manipulate the wheel slip so that a maximum friction is
obtained and the steering stability (also known as the
lateral stability) is maintained. That is, to make th e vehi-
cle stop in the shortest distance possible while maintain-
ing the directional control. The ideal goal for the control
design is to regulate th e wheel v elo city. The techno log ies
of ABS are also applied in traction control system (TCS)
and vehicle dynamic stability control (VDSC) [14].
Typical ABS components include: vehicle’s physical
brakes, wheel speed sensors (up to 4), an electronic con-
trol unit (ECU), brake master cylinder, a hydraulic
modulator unit with pump and valves as shown in Figure
1. Some of the advanced ABS systems include acceler-
ometer to determine the deceleration of the vehicle. This
paper is intended to present a literature review of re-
search works done by many researchers concerning
various aspects of ABS technology in an effort to im-
prove the performance of its applications.
2. Principles of Antilock-Brake System
The reason for the development of antilock brakes is in
essence very simple. Under braking, if one or more of a
vehicle’s wheels lock (begins to skid) then this has a

A. A. ALY ET AL.187
Figure 1. Typical ABS components [4].
number of consequences: a) braking distance increases, b)
steering control is lost, and c) tire wear will be abnormal.
The obvious consequence is that an accident is far more
likely to occur. The application of brakes generates a
force that impedes a vehicles motion by applying a force
in the opposite direction.
During severe braking scenarios, a point is obtained in
which the tangential velocity of the tire surface and the
velocity on road surface are not the same such that an
optimal slip which corresponds to the maximum friction
is obtained. The ABS controller must deal with the brake
dynamics and the wheel dynamics as a whole plant [15].
The wheel slip, S is defined as:
VR
sV


 (1)
where ω, R, and V de note the wh eel angular v elocity, th e
wheel rolling radius, and the vehicle forward velocity,
respectively. In normal driving conditions, V = ωR,
therefore S = 0. In severe braking, it is common to have
ω = 0 while S = 1, which is called wheel lockup. Wheel
lockup is undesirable since it prolongs the stopping dis-
tance and causes the loss of direction control [16,17].
Figure 2 shows the relationship between braking co-
efficient and wheel slip. It is shown that the slide values
for stopping/traction force are proportionately higher
than the slide values for cornering/steering force. A
locked-up wheel provides low road handling force and
minimal steering force. Consequently the main benefit
from ABS operation is to maintain directional control of
the vehicle during heavy braking. In rare circumstances
the stopping distance may be increased however, the
directional control of the vehicle is substantially greater
than if the wheels are locked up.
The main difficulty in the design of ABS control arises
from the strong nonlinearity and unc ertainty of the prob-
lem. It is difficult and in many cases impossible to solve
this problem by using classical linear, frequency domain
methods [17]. ABS systems are designed around system
hydraulics, sensors and control electronics. These sys-
tems are dependent on each other and the different sys-
tem components are interchangeable with minor changes
in the controller software [18].
The wheel sensor feeds the wheel spin velocity to the
electronic control unit, which based on some underlying
control approach would give an output signal to the
brake actuator control unit. The brake actuator control
unit then controls the brake actuator based on the output
from the electronic control unit. The control logic is
based on the objective to keep the wheels from getting
locked up and to maintain the traction between the tire
and road surface at an optimal maximum. The task of
keeping the wheels operating at maximum traction is
complicated given that the friction-slip curve changes
with vehicle, tire and road changes. The block diagram in
Figure 3. shows the block representation of an antilock
brake system. It shows the basic functionality of the
various components in ABS systems and also shows the
data/information flow.
The ABS (shown in Figure 4) consists of a conven-
tional hydraulic brake system plus antilock components.

A. A. ALY ET AL. 188
Figure 2. Illustration of the relationship between braking coefficient and wheel slip [14].
Wheel Velocit y
Sensor
Vehicle Velocity
Sensor
Tire Road
Interaction
Control AlgorithmBrak e Actuat or
Valve Brak e Actuat or
Figure 3. Block representation of an ABS.
The conventional brake system includes a vacuum
booster, master cylinder, front disc brakes, rear drum
brakes, interconnecting hydraulic brake pipes and hoses,
brake fluid level sensor and the brake indicator. The
ABS components include a hydraulic unit, an electronic
brake control module (EBCM), two system fuses, four
wheel speed sensors (one at each wheel), interconnecting
wiring, the ABS indicator, and the rear drum brake.
Most ABS systems employ hydraulic valve control to
regulate the brake pressure during the anti-lo ck operation.
Brake pressure is increased, decreased or held. The
amount of time required to open, close or hold the hy-
draulic valve is the key point affecting the brake effi-
iency and steering con trollability. c

A. A. ALY ET AL.189
Signal Wire
Wd heel Spee
Sensor
Sensor Ring
Brake Cylinder Pistons
Drum
Cable
T y
Br er
o Emergenc
ake LevEmergency
Brake
Mechanism
Adjuster
Mechanism
Brake Shoes
Figure 4. Anti-lock braking system [14].
3. ABS Control
ABS brake controllers pose unique challenges to the de-
signer: a) For optimal performance, the controller must
operate at an unstable equilibrium point, b) Depending
on road conditions, the maximum braking torque may
vary over a wide range, c) The tire slippage measurement
signal, crucial for controller performance, is both highly
uncertain and noisy, d) On rough roads, the tire slip ratio
varies widely and rapidly due to tire bouncing, e) brake
pad coefficient of friction changes, and f) The braking
system contains transportation delays which limit the
control system bandwidth [19].
As stated in the previous section of this paper, the
ABS consists of a conventional hydraulic brake system
plus antilock components which affect the control char-
acteristics of the ABS. ABS control is a h ighly a nonlin-
ear control problem due to the complicated relationship
between friction and slip. Another impediment in this
control problem is that the linear velo city of the wheel is
not directly measurable and it has to be estimated. Fric-
tion between the road and tire is also not readily meas-
urable or might need complicated sensors. Researchers
have employed various control approaches to tackle this
problem. A sampling of the research done for different
control approaches is shown in Figure 5. One of the
technologies that has been applied in the various aspects
of ABS control is soft computing. Brief review of ideas
of soft computing and how they are employed in ABS
control are giv e n bel o w.
3.1. Classical Control Methods Based on PID
Control
Out of all control types, the well known PID has been
Antilock Brake Control
Sy
stems
R
esearch
Classical Control
Intelligent Control
Robust Control
O
p
timal Control
Adaptive Control
Nonlinear Con trol
Figure 5. Sampling of ABS control.

A. A. ALY ET AL. 190
used to improve the performance of the ABS. Song, et al.
[20] presented a mathematical model that is designed to
analyze and improve the dynamic performance of a ve-
hicle. A PID controller for rear wheel steering is de-
signed to enhance the stability, steerability, and drive-
ability of the vehicle during transient maneuvers. The
braking and steering performances of controllers are
evaluated for variou s driving conditions, such as straight
and J-turn maneuvers. The simulation results show that
the proposed full car model is sufficient to predict vehi-
cle responses accurately. The developed ABS reduces
the stopping distance and increases the longitudinal and
lateral stability of both two- and four-wheel steering ve-
hicles. The results also demonstrate that the use of a rear
wheel controller as a yaw motion controller can increase
its lateral stability and reduce the slip angle at high
speeds.
The PID controller is simple in design but there is a
clear limitation of its performance. It does not posses
enough robustness for practical implementation. For
solving this problem, Jiang [21] app lied a new Nonlinear
PID (NPID) control algorithm to a class of truck ABS
problems. The NPID algorithm combines the advantages
of robust control and easy tuning. Simulation results at
various situations using TruckSim show that
NPID controller has shorter stopping distance and
better velocity performance than the conventional PID
controller and a loop-shaping controller.
3.2. Optimal Control Methods Based on
Lyapunov approach
The optimal control of nonlinear system such as ABS is
one of the most challenging and difficult subjects in con-
trol theory. Tanelli et al. [22] proposed a nonlinear out-
put feedback control law for active braking control sys-
tems. T he control law gu arantees bound ed control actio n
and can cope also with input constraints. Moreover, the
closed-loop system properties are such that the control
algorithm allows detecting without the need of a friction
estimator, if the closed-loop system is operating in the
unstable region of the friction curve, thereby allowing
enhancing both braking performance and safety. The
design is performed via Lyapunov-based methods and its
effectiveness is assessed via simulations on a multibody
vehicle simulator. The ch ange in the road conditions im-
plies continuous adap tation in controller parameter.
In order to resolve this issue, an adaptive control-
Lyapunov approach is suggested by R. R. Freeman [23]
and similar ideas are pursued in [24,25]. The use of Son-
tag’s formula is applied in the adap tiv e con tro l Lyapunov
approach in [26], which includes gain scheduling on ve-
hicle speed and experimental testing. Feedback lineariza-
tion in combinatio n with gain scheduling is suggested by
Liu and Sun [27]. PID-type approaches to wheel slip
control are considered in [28-32]. A gain scheduled LQ
control design approach with associated analysis, and,
except [26] and [32], is the only one that contains de-
tailed experimental evaluation using a test vehicle. In
[33], an optimum seeking app roach is taken to determine
the maximum friction, using sliding modes. Sliding
mode control is also considered in [34,35].
Another nonlinear modification was suggested by Ün-
sal and Kachroo [36] for observer-based design to con-
trol a vehicle traction that is important in providing
safety and obtaining desired longitudinal vehicle motion.
The direct state feedback is then replaced with nonlinear
observers to estimate the veh icle velocity from the ou tpu t
of the system (i.e., wheel velocity). The nonlinear model
of the system is shown locally observable. The effects
and drawbacks of the extended Kalman filters and slid-
ing observers are shown via simulations. The sliding
observer is found promising while the extended Kalman
filter is unsatisfactory due to unpredictable changes in
the road conditions.
3.3. Nonlinear Control Based on Backstepping
Control Design
The complex nature of ABS requiring feedback control
to obtain a desired system behavior also gives rise to
dynamical systems. Ting and Lin [37] developed the
anti-lock braking control system integrated with active
suspensions applied to a quarter car model by employing
the nonlinear backstepping design schemes. In emer-
gency, although the braking distance can be reduced by
the control torque from disk/drum brakes, the braking
time and distance can be further improved if the normal
force generated from active suspension systems is con-
sidered simultaneously. Individual controller is designed
for each subsystem and an integrated algorithm is con-
structed to coordinate these two subsystems. As a result,
the integration of an ti-lock braking and active suspen sion
systems indeed enhances the system performance be-
cause of reducti on o f braking time and distance .
Wang, et al. [38] compared the design process of
backstepping appro ach ABS via multiple model adap tive
control (MMAC) controllers. The high adhesion fixed
model, medium adhesion fixed model, low adhesion
fixed model and adaptive model were four models used
in MMAC. The switching rules of different model con-
trollers were also presented. Simulation was conducted
for ABS control system using MMAC method basing on
quarter vehicle model. Results show that this method can
control wheel slip ratio more accurately, and has higher
robustness, therefore it improves ABS performance ef-

A. A. ALY ET AL.191
fectively.
Tor Arne Johansen, [39] provided a contribution on
nonlinear adaptive backstepping with estimator resetting
using multiple observers A multiple model based ob-
server/estimator for the estimation of parameters was
used to reset the parameter estimation in a conventional
Lyapunov based nonlinear adaptive controller. Transient
performance can be improved without increasing the
gain of the controller or estimator. This allows perform-
ance to be tuned without compromising robustness and
sensitivity to noise and disturbances. The advantages of
the scheme are demonstrated in an automotive wh eel slip
controller.
3.4. Robust Control Based on Sliding Mode
Control Method
Sliding mode control is an important robust control ap-
proach. For the class of systems to which it applies, slid-
ing mode controller design provides a systematic ap-
proach to the problem of maintaining stability and con-
sistent performance in the face of modeling imprecision.
On the other hand, by allowing the tradeoffs between
modeling and performance to be quantified in a simple
fashion, it can illuminate the whole design process.
Several results have been published coupling the ABS
problem and the VSS design technique [40,41]. In these
works design of sliding-mode controllers under the as-
sumption of knowing the optimal value of the target slip
was introduced. A problem of concern here is the lack of
direct slip measurements. In all previous investigations
the separation approach has been used. The problem was
divided into the problem of optimal slip estimation and
the problem of tracking the estimated o ptimal value. J.K.
Hedrick, et al. [42,43] suggested a modification of the
technique known as sliding mode control. It was chosen
due to its robustness to modeling errors and disturbance
rejection capabilities. Simulation results are presented to
illustrate the capability of a vehicle using this controller
to follow a desired speed trajectory while maintaining
constant spacing between vehicles. Therefore a sliding
mode control algorithm was implemented for this appli-
cation. While Kayacan [44] proposed a grey slid-
ing-mode controller to regulate the wheel slip , depending
on the vehicle forward velocity. The proposed controller
anticipates the upcoming values of wheel slip and takes
the necessary action to keep the wheel slip at the desired
value. The performance of the control algorithm as ap-
plied to a quarter vehicle is evaluated through simula-
tions and experimental studies that include sudden
changes in road conditions. It is observed that the pro-
posed controller is capable of achieving faster conver-
gence and better noise response than the conventional
approaches. It is concluded that the use of grey system
theory, which has certain pred iction cap abilities, can be a
viable alternative approach when the conventional con-
trol methods cannot meet the desired performance speci-
fications. In real systems, a switched controller has im-
perfections which limit switching to a finite frequency.
The oscillation with the neighborhood of the switching
surface cause chattering. Chattering is undesirable, since
it involves extremely high control activity, and further-
more may excite high-frequency dynamics neglected in
the course of modeling. Chattering must be reduced
(eliminated) for the controller to perform properly.
3.5. Adaptive Control Based on Gain Scheduling
Control Method
Ting and Lin [45] presented an approach to incorporate
the wheel slip constraint as a priori into control design so
that the skidding can be avoided. A control structure of
wheel torque and wheel steering is proposed to transfor m
the original problem to that o f state regulation with input
constraint. For the transformed problem, a low-and-high
gain technique is applied to construct the constrained
controller and to enhance the utilization o f the wheel slip
under constraint. Simulation shows that the proposed
control scheme, during tracking on a snow road, is capa-
ble of limiting the wheel slip, and has a satisfactory co-
ordination between wheel torque and wheel steering.
3.6. Intelligent Control Based on Fuzzy Logic
FC has been proposed to tackle the problem of ABS for
the unknown environmental parameters [46-50]. How-
ever, the large amount of the fuzzy rules makes the
analysis complex. Some researchers have proposed fuzzy
control design methods based on the sliding-mode con-
trol (SMC) scheme. These approaches are referred to as
fuzzy sliding-mode control (FSMC) design methods
[51,52]. Since only one variable is defined as the fuzzy
input variable, the main advantage of the FSMC is that it
requires fewer fuzzy rules than FC does. Moreover, the
FSMC system has more robustness against parameter
variation [52]. Although FC and FSMC are both effec-
tive methods, their major drawback is that the fuzzy rules
should be previously tuned by time-consuming trial-and-
error procedures. To tackle this problem, adaptive fuzzy
control (AFC) based on the Lyapunov synthesis ap-
proach has been extensively studied [52-55]. With this
approach, the fuzzy rules can be automatically adjusted
to achieve satisfactory system response by an adaptive
law.
Kumar et al. [56] investigated the in tegrated contro l of
ABS System and collision avoidance system (CAS) in

A. A. ALY ET AL. 192
electric vehicle. Fuzzy logic techniques are applied for
integral control of two subsystems. Control algorithm is
implemented and tested in a prototype electric vehicle in
laboratory environment using free scale HCS12 micro-
controller. A high level network protocol CAN is applied
to integrate all sensors, ABS and CAS. The results show
that integrated control of ABS and CAS maintains the
safe distance from obstacle without sacrificing the per-
formance of either system. Different researcher [57-59]
developed an adaptive PID-type fuzzy controller for the
ABS system. A platform is built to accomplish a series of
experiments to control the ABS. A commercial ABS
module controlled by a controller is installed and tested
on the platform. The vehicle and tire models are deduced
and simulated by a personal computer for real time con-
trol. Road surface conditions, vehicle weight and control
schemes are varied in the experiments to study braking
properties.
Lin and Hsu [60] proposed a self-learning fuzzy slid-
ing-mode control (SLFSMC) design method for ABS. In
the proposed SLFSMC system, a fuzzy controller is the
main tracking controller, which is used to mimic an ideal
controller; and a robust controller is derived to compen-
sate for the difference between the ideal controller and
the fuzzy controller. The SLFSMC has the advantages
that it can automatically adjust th e fuzzy rules, similar to
the AFC, and can reduce the fuzzy rules, similar to the
FSMC. Moreover, an error estimation mechanism is in-
vestigated to observe the bound of approximation error.
All parameters in SLFSMC are tuned in the Lyapunov
sense, thus, the stab ility of the system can be guaranteed.
Finally, two simulation scenarios are examined and a
comparison between a SMC, an FSMC, and the proposed
SLFSMC is made.
The ABS system performance is examined on a quar-
ter vehicle model with nonlinear elastic suspension. The
parallelism of the fuzzy logic evaluation process ensures
rapid computation of the controller output signal, requir-
ing less time and fewer computation steps than control-
lers with adaptive identification. The robustness of the
braking system is investigated on rough roads and in the
presence of large measurement noise. The simulation
results present the system performance on various road
types and under rapidly changing road conditions. While
conventional control approaches and even direct fuzzy/
knowledge based approaches [61-67] have been suc-
cessfully implemented, their performance will still de-
grade when adverse road conditions are encountered.
The basic reason for this performance degradation is that
the control algorithms have limited ability to learn how
to compensate for the wide variety of road conditions
that exist.
Laynet et al. [68] and Laynet and Passino [69] intro-
duced the idea of using the fuzzy model reference learn-
ing control (FMRLC) technique for maintaining ade-
quate performance even under adverse road conditions.
This controller utilizes a learning mechanism which ob-
serves the plant outputs and adjusts the rules in a direct
fuzzy controller so that the overall system be haves like a
“reference model” which characterizes the desired be-
havior. The performance of the FMRLC-based ABS is
demonstrated by simulation for various road conditions
(wet asphalt, icy) and “split road conditions” (the condi-
tion where, e.g., emergency braking occurs and the road
switches from wet to icy or vice versa). Precup et al. [70]
developed a Takagi-Sugeno fuzzy controller and an in-
terpolative fuzzy controller for tire slip control in ABS
systems. By employing local linearized models of the
controlled plant, the local controllers are developed in
the frequency domain. Development methods for the two
fuzzy controllers are also offered. Simulation results
show that the control system performance enhancement
ensured by the fuzzy controllers in comparison with the
conventional PI ones.
Stan, et al. [71] performed a critical analysis of five
fuzzy control solutions dedicated to ABS systems. The
detailed mathematical model of controlled plant is de-
rived and simplified for control design with focus on tire
slip control. A new fuzzy control solution based on a
class of Takagi-Sugeno fuzzy controllers is proposed.
This class of fuzzy controllers combines separately de-
signed PI and PID controllers corresponding to a set of
simplified models of controlled plant linearized in the
vicinity of importan t operating points. Simulation resu lts
validate the suggested fuzzy control solution in control-
ling the relative slip of a single wheel.
R. Keshmiri and A.M. Shahri [72] designed an in telli-
gent fuzzy ABS controller to adjust slipping performance
for variety of roads. There are two major features in the
proposed control system: the first is a fuzzy logic con-
troller providing optimal brake torque for both front and
rear wheels; and the second is also a FLC provides re-
quired amount of slip and torque references properties
for different kinds of roads. Simulation results show
more reliable and better performance compared with
other brake systems. While Karakose and Akin [73]
proposed a different fuzzy control algorithm, which used
dynamical fuzzy logic system and block based neural
network, for dynamical control problems. The effective-
ness of the proposed method is illustrated by simulation
results for dc motor position control problem. In the
same direction Ayman A. Aly [74] designed an intelli-
gent fuzzy ABS controller to adjust slipping performance
for variety of roads. The fuzzy optimizer finds immedi-
ately the optimal wheel slips for the new surface and
forces the actual wheel slips to track the optimal refer-

A. A. ALY ET AL.193
ence wheel slips. The simulation results show that the
proposed ABS algorithm ensures avoiding of wheel’s
blockage, even in different road conditions. Moreover, as
a free model strategy, the obtained fuzzy control is ad-
vantageous from viewpoint of reducing design complex-
ity and, also, antisatur ating, antich attering an d robu stness
properties of the controlled system.
4. Conclusions
ABS control is highly nonlinear control problem due to
the complicated relationship between its components and
parameters. The research that has been carried out in
ABS control systems covers a broad range of issues and
challenges. Many different control methods for ABS
have been developed and research on improved control
methods is continuing. Most of these approaches require
system models, and some of them cannot achieve satis-
factory performance under the changes of various road
conditions. While soft computing methods like Fuzzy
control doesn’t need a precise model. A brief idea of how
soft computing is employed in ABS control is given.
5. Acknowledgement
This study is supported by Taif University under a con-
tract No. 1-432-1168. The financial support of Taif Uni-
versity is highly appreciated.
6. References
[1] P. M. Hart, “Review of Heavy Vehicle Braking Systems
Requirements (PBS Requirements),” Draft Report, 24
April 2003.
[2] M. Maier and K. Muller “The New and Compact ABS
Unit for Passenger Cars,” SAE Paper No.950757, 1996.
[3] P. E. Wellstead and N. B. O. L. Pettit, “Analysis and
Redesign of an Antilock Brake System Controller,” IEE
Proceedings Control Theory Applications, Vol. 144, No.
5, 1997, pp. 413-426. doi:10.1049/ip-cta:19971441
[4] A. G. Ulsoy and H. Peng, “Vehicle Control Systems,”
Lecture Notes, ME 568, 1997.
[5] P. E. Wellstead, “Analysis and Redesign of an Antilock
Brake System Controller,” IEEE Proceedings Control
Theory Applications, Vol. 144, No. 5, September 1997,
pp. 413-426. doi: 10.1049/ip-cta:19971441
[6] R. Fling and R. Fenton, “A Describing-Function Ap-
proach to Antiskid Design,” IEEE Transactions on Ve-
hicular Technology, Vol. VT-30, No. 3, 1981, pp. 134-
144. doi:10.1109/T-VT.1981.23895
[7] S. Yoneda, Y. Naitoh and H. Kigoshi, “Rear Brake
Lock-Up Control System of Mitsubishi Starion,” SAE
Paper, Washington, 1983.
[8] T. Tabo, N. Ohka, H. Kuraoka and M. Ohba, “Automo-
tive Antiskid System Using Modern Control Theory,”
IEEE Proceedings, San Francisco, 1985, pp. 390-395.
[9] H. Takahashi and Y. Ishikawa, “Anti-Skid Braking Con-
trol System Based on Fuzzy Inference,” U.S. Patent No.
4842342, 1989.
[10] R. Guntur and H. Ouwerkerk, “Adaptive Brake Control
System,” Proceedings of the Institution of Mechanical
Engineers, Vol. 186, No. 68. 1972, pp. 855-880.
doi:10.1243/PIME_PROC_1972_186_102_02
[11] G. F. Mauer, “A Fuzzy Logic Controller for an ABS
Braking System,” IEEE Transactions on Fuzzy Systems,
Vol. 3, No. 4, 1995, pp. 381-388.
doi:10.1109/91.481947
[12] W. K. Lennon and K. M. Passino, “Intelligent Control for
Brake Systems,” IEEE Transactions on Control Systems
Technology, Vol. 7, No. 2, 1999, pp. 188-202.
[13] B. Lojko and P. Fuchs, “The Control of ASR System in a
Car Based on the TMS320F243 DSP,” Diploma Thesis,
Dept. of Radio & Electronics, Bratislava, 2002.
[14] P. Hart, “ABS Braking Requirements,” Hartwood Con-
sulting Pty Ltd , Victoria, June 2003.
[15] Q. Ming, “Sliding Mode Controller Design for ABS Sys-
tem,” MSc Thesis, Virginia Polytechnic Institute and
State University, 1997.
[16] M. Stan, R.-E. Precup and A. S. Paul, “Analysis of Fuzzy
Control Solutions for Anti-Lock Braking Systems,” Jour-
nal of Control Engineering and Applied Informatics, Vol.
9, No. 2, 2007, pp. 11-22.
[17] S. Drakunov, U. Ozgiiner and P. Dix, “ABS Control Us-
ing Optimum Search via Sliding Modes,” IEEE Transac-
tion on Control Systems Technology, Vo1. 3 No. 1,
March 1995, pp. 79-85.
[18] National Semiconductor Inc., “Adaptive Braking Systems
(ABS),” US Patent No. 3825305, 1974.
[19] G. F. Mauer, “A Fuzzy Logic Controller for an ABS
Braking System,” IEEE Transactions on Fuzzy Systems,
Vol. 3, No. 4, 1995, pp. 381-388.
doi: 10.1109/91.481947
[20] J. Song, H. Kim and K. Boo, “A study on an Anti-Lock
Braking System Controller and Rear-Wheel Controller to
Enhance Vehicle Lateral Stability,” Proceedings of the
Institution of Mechanical Engineers, Part D: Journal of
Automobile Engineering, Vol. 221 No. 7, 2007, pp. 777-
787. doi:10.1243/09544070JAUTO225
[21] F. Jiang, “An Application of Nonlinear PID Control to a
Class of Truck ABS Problems,” Proceedings of the 40th
IEEE Conference on Decision and Control, Orlando,
2000, pp. 516-521.
[22] M. Tanellia, A. Astolfi and S. M. Savaresi, “Robust
Nonlinear Output Feedback Control for Brake by Wire
Control Systems,” Automatica, Vol. 44, No. 4, 2008, pp.
1078-1087. doi:10.1016/j.automatica.2007.08.020
[23] R. Freeman, “Robust Slip Control for a Single Wheel,”
University of California, Santa Barbara, 1995.
[24] J. S. Yu, “A Robust Adaptive Wheel-Slip Controller for
Antilock Brake System,” Proceedings of 36th IEEE Con-

A. A. ALY ET AL. 194
ferrence on Decision Control, San Diego, 1997, pp.
2545-2546.
[25] J. Yi, L. Alvarez, R. Horowitz and C. C. DeWit, “Adap-
tive Emergency Braking Control Using a Dynamical
Tire/Road Friction Model,” Proceedings of 39th IEEE
Conference on Decision Control, Sydney, 2000, pp. 456-
461.
[26] J. Lüdemann, “Heterogeneous and Hybrid Control with
Application in Automotive Systems,” Ph.D. dissertation,
Glasgow University, 2002.
[27] Y. Liu and J. Sun, “Target Slip Tracking Using
Gain-Scheduling for Braking Systems,” Proceedings of
the 1995 American Control Conference, Seattle, 1995, pp.
1178-1182.
[28] S. Taheri and E. H. Law, “Slip Control Braking of an
Automobile during Combined Braking and Steering Ma-
noeuvres,” American Society of Magazine Editors, Vol.
40, No. 1, 1991, pp. 209-227.
[29] C. Jun, “The Study of ABS Control System with Differ-
ent Control Methods,” Proceedings of the 4th Interna-
tional Symposium on Advanced Vehicle Control, Nagoya,
1998, pp. 623-628.
[30] F. Jiang, “A Novel Approach to a Class of Antilock
Brake Problems,” Ph.D. Dissertation, Cleveland State
University, Cleveland, 2000.
[31] Y. Wang, T. Schmitt-Hartmann, M. Schinkel and K. J.
Hunt, “A New Approach to Simultaneous Stabilization
and Strong Simultaneous Stabilization with D Stability
and Its Application to ABS Control Systems Design,”
European Control Conference, Porto, 2001, pp. 1291-
1294.
[32] S. Solyom, “Synthesis of a Model-Based Tire Slip Con-
troller,” Synthesis of a Model-Based Tire Slip Controller,
Vol. 41, No. 6, 2004, pp. 475-499.
[33] S. Drakunov, Ü. Özgüner, P. Dix, and B. Ashrafi, “ABS
Control Using Optimum Search via Sliding Modes,”
IEEE Transactions on Control Systems Technology, Vol.
3, 1995, pp. 79-85. doi:10.1109/87.370698
[34] M. Schinkel and K. Hunt, “Anti-lock Braking Control
Using a Sliding Mode Like Approach,” Proceedings of the
2002 American Control Conference, Anchorage, 2002, pp.
2386-2391.
[35] M. C. Wu and M. C. Shih, “Hydraulic Anti-Lock Braking
Control Using the Hybrid Sliding-Mode Pulse Width
Modulation Pressure Control Method,” Proceedings of
the Institution of Mechanical Engineers, Vol. 215, 2001,
pp. 177-187. doi:10.1109/87.748153
[36] C. Ünsal and P. Kachroo, “Sliding Mode Measurement
Feedback Control for Antilock Braking Systems,” IEEE
Transactions on Control Systems Technology, Vol. 7, No.
2, March 1999, pp. 271-281.
[37] W. Ting and J. Lin, “Nonlinear Control Design of
Anti-lock Braking Systems Combined with Active Sus-
pensions,” Technical report of Department of Electrical
Engineering, National Chi Nan University, 2005.
[38] R.-G. Wang, Z.-D. Liu and Z.-Q. Qi, “Multiple Model
Adaptive Control of Antilock Brake System via Back-
stepping Approach,” Proceedings of 2005 International
Conference on Machine Learning and Cybernetics,
Guangzhou, 2005, pp. 591-595.
[39] T. A. Johansen, J. Kalkkuhl, J. Lüdemann and I. Petersen,
“Hybrid Control Strategies in ABS,” Proceedings of the
2001 American Control Conference, Arlington 2001, pp.
1704-1705.
[40] H. S. Tan and M. Tomizuka, “An Adaptive Sliding Mode
Vehicle Traction Controller Design,” Proceedings of the
1989 American Control Conference, Pittsburgh, 1989, pp.
1053-1058.
[41] Y. K. Chin, W. C. Lin and D. Sidlosky, “Sliding-Mode
ABS Wheel Slip Control,” Proceedings of 1992 ACC,
Chicago, 1992, pp. 1-6.
[42] J. C. Gerdes, A. S. Brown and J. K. Hedrick, “Brake
System Modeling for Vehicle Control,” Proceedings In-
ternational Mechanical Engineering Congress and Expo-
sition, San Francisco, 1995, pp. 4756-4763.
[43] D. Cho and J. K. Hedrick, “Automotive Powertrain Mod-
eling for Control,” Transactions ASME Journal of Dy-
namic Systems, Measurements and Control, Vol.111,
No.4, December 1989, pp. 568-576.
doi:10.1115/1.3153093
[44] E. Kayacan and O. Kaynak, “A Grey System Modeling
Approach for Sliding Mode Control of Antilock Braking
System,” IEEE Transactions On Industrial Electronics,
Vol. 56, No. 8, August 2009, pp. 3244-3252.
doi:10.1109/TIE.2009.2023098
[45] W. Ting and J. Lin, “Nonlinear Control Design of
Anti-lock Braking Systems Combined with Active Sus-
pensions,” Technical Report of Department of Electrical
Engineering, National Chi Nan University, 2005.
[46] B. Ozdalyan, “Development of A Slip Control Anti-Lock
Braking System Model,” International Journal of Auto-
motive Technology, Vol. 9, No. 1, 2008, pp. 71-80.
doi:10.1007/s12239-008-0009-6
[47] A. B. Will and S. H. Zak, “Antilock Brake System Mod-
elling and Fuzzy Control,” International Journal of Vehi-
cle Design, Vol. 24, No. 1, 2000, pp. 1-18.
doi:10.1504/IJVD.2000.001870
[48] J. R. Layne, K. M. Passino and S. Yurkovich, “Fuzzy
Learning Control for Antiskid Braking Systems,” IEEE
Transactions on Control Systems Technology, Vol. 1, No.
2, 1993, pp. 122-129.
doi:10.1109/87.238405
[49] G. F. Mauer, “A Fuzzy Logic Controller for an ABS
Braking System,” IEEE Transactions on Fuzzy Systems,
Vol. 3, No. 4, 1995, pp. 381-388. doi:10.1109/91.481947
[50] K. Lee and K. Park, “Optimal Robust Control of a Con-
tactless Brake System Using an Eddy Current,” Mecha-
tronics, Vol. 9, No. 6, 1999, pp. 615-631.
doi:10.1016/S0957-4158(99)00008-2
[51] W. K. Lennon and K. M. Passino, “Intelligent Control for
Brake Systems,” IEEE Transctions on Control Systems
Technology, Vol. 7, No. 2, 1999, pp. 188-202.
doi:10.1109/87.748145
[52] C. Unsal and P. Kachroo, “Sliding Mode Measurement

A. A. ALY ET AL. 195
Feedback Control for Antilock Braking Systems,” IEEE
Transctions on Control Systems Technology, Vol. 7, No.
2, 1999, pp. 271-280. doi:10.1109/87.748153
[53] C.C. Lee, “Fuz zy Logic in Cont rol Systems: Fuzzy Logic
Controller Part I, II,” IEEE Transactions on Systems,
Man, and Cybernetics, Vol. 20, No. 2, 1990, pp. 404-435.
doi:10.1109/21.52551
[54] S.W. Kim and J.J. Lee, “Design of a Fuzzy Controller
with Fuzzy Sliding Surface,” Fuzzy Sets and Systems, Vol.
71, No. 3, 1995, pp. 359-369.
doi:10.1016/0165-0114(94)00276-D
[55] B. J. Choi, S. W. Kwak and B. K. Kim, “Design of a
Single-Input Fuzzy Logic Controller and Its Properties,”
Fuzzy Sets Systems, Vol. 106, No. 3, 1999, pp. 299-308.
doi:10.1016/S0165-0114(97)00283-2
[56] S. Kumar, K. L. Verghese and K. K. Mahapatra, “Fuzzy
Logic Based Integrated Control of Anti-Lock Brake Sys-
tem and Collision Avoidance System Using CAN for
Electric Vehicles,” IEEE International Conference on
Industrial Technology, Gippsland, 2009, pp. 1-5.
doi:10.1109/ICIT.2009.4939720
[57] L. X. Wang, “Adaptive Fuzzy Systems and Control: De-
sign and Stability Analysis,” Prentice-Hall, Inc., Upper
Saddle River, 1994.
[58] H. Lee and M. Tomizuka, “Robust Adaptive Control
Using a Universal Approximator for SISO Nonlinear
Systems,” IEEE Transactions on Fuzzy Systems, Vol. 8,
No. 1, 2001, pp. 95-106.
[59] C. K. Chen and M. C. Shih, “PID Type Fuzzy Control for
Antilock Brake System with Parameter Adaptation,”
JSME International Journal, Series C, Vol. 47, No. 2,
2004, pp. 675-685. doi:10.1299/jsmec.47.675
[60] C.-M. Lin, C.-F. Hsu, “Self-Learning Fuzzy Slid-
ing-Mode Control for Antilock Braking Systems,” IEEE
Transactions On Control Systems Technology, Vol. 11,
No. 2, 2003, pp. 273-278.
doi:10.1109/TCST.2003.809246
[61] H. Tan and M. Tomizuka, “A Discrete-Time Robust Ve-
hicle Traction Controller Design,” American Controls
Conference, Pittsburgh, 1989, pp. 1053-1058.
[62] H. Tan and M. Tomizuka, “Discrete-Time Controller
Design for Robust Vehicle Traction,” IEEE Control Sys-
tems Magazine, Vol. 10, No. 3, 1990, pp. 107-113.
doi:10.1109/37.55132
[63] R. Fling and R. Fenton, “A Describing-Function Ap-
proach to Antiskid Design,” IEEE Transactions on Ve-
hicular Technology, Vol. 30, No. 3, 1981, pp.134-144.
doi:10.1109/T-VT.1981.23895
[64] S. Yoneda , Y. Naito h and H. Kigosh i, “Rea r Brake Lo ck-
Up Control System of Mitsubishi Starion,” SAE paper
830482, 1983.
[65] T. Tabo, N. Ohka, H. Kuraoka and M. Ohba, “Automo-
tive Antiskid System Using Modern Control Theory,”
IECON, Vol. 1, pp. 390-395, 1985.
[66] H. Takahashi and Y. Ishikawa, “Anti-Skid Braking Con-
trol System Based on Fuzzy Inference,” US Patent No.
4842342, 1989.
[67] R. Guntur and H. Ouwerkerk, “Adaptive Brake Control
System,” Proceedings of the Institution of Mechanical
Engineers, 1972, pp. 855-880.
[68] J. R. Laynet, K. M. Passinot and S. Yurkovich, “Fuzzy
Learning Control for Anti-Skid Braking Systems,” IEEE
Transactions on Control Systems Technology, Vol. 1, No.
2, 1993, pp. 122-129. doi:10.1109/87.238405
[69] J. Laynet and K. M. Passino, “Fuzzy Model Reference
Learning Control for Cargo Ship Steering,” IEEE Control
Systems Magazine, Vol. 13, No. 6, September 1992, pp.
23-24. doi:10.1109/37.248001
[70] R-E. Precup, St. Preitl, M. Balas, V. Balas, “Fuzzy Con-
trollers for Tire Slip Control in Anti-lock Braking Sys-
tems,” IEEE International Conference on Fuzzy Systems,
Budapest, 2004, pp. 1317-1322.
[71] M. Stan, R.-E. Precup and S. A. Paul, “Analysis of Fuzzy
Control Solutions for Anti-Lock Braking Systems,”
Journal of Control Engineering and Applied Informatics,
Vol. 9, No. 2, 2007, pp. 11-22.
[72] R. Keshmiri and A. M. Shahri, “Intelligent ABS Fuzzy
Controller for Diverse Road Surfaces,” World Academy
of Science, Engineering and Technology, Vol. 2, No. 2,
2007, pp. 62-67.
[73] M. Karaköse and E. Akin, “Dynamical Fuzzy Control
with Block Based Neural Network,” Technical Report,
Department of Computer Engineering, Fırat University,
2006.
[74] A. A. Aly, “Intelligent Fuzzy Control for Antilock Brake
System with Road-Surfaces Identifier,” 2010 IEEE In-
ternational Conference on Mechatronics and Automation,
Xi’an, 2010, pp. 2292-2299.