Vibration Control of Vehicle Active Suspension
Using Sliding Mode Under Parameters
Uncertainty
Abstract—This paper introduces a theoretical investigation
of active vehicle suspension system using sliding mode
control (SMC) algorithm to enhance the ride comfort and
vehicle stability under parameters uncertainty. SMC
algorithm is a nonlinear control technique that regulates the
dynamics of linear and nonlinear systems using a
discontinuous control signal. The proposed control
algorithm forces the suspension system to follow the
behavior of the ideal sky-hook system behavior. A
mathematical model and the equations of motion of the
quarter active vehicle suspension system are considered and
simulated using Matlab/Simulink software. The proposed
active suspension is compared with the passive suspension
systems. Suspension performance is evaluated in both time
and frequency domains, in order to verify the success of the
proposed control technique. Also, uncertainty analysis due
to the increased of sprung mass and depreciated of spring
stiffness and damping coefficient is also investigated in this
paper. The simulated results reveal that the proposed
controller using SMC grants a significant enhancement of
ride comfort and vehicle stability even in the existence of
parameters uncertainty.
Index Terms—active vehicle suspension, sliding mode
control, vibration control.
I. INTRODUCTION
The development of good quality control techniques
for vehicle active and semi-active suspension systems is a
main issue for the automotive industry. A good quality
suspension system should enhance the ride comfort and
vehicle stability. To improve ride comfort, it should
minimize the vertical body acceleration of the vehicle due
to the unwanted disturbances of the road surface. In terms
of vehicle stability, however, it should offer a sufficient
tyre-terrain contact and minimize the dynamic deflection
of the tyre. Therefore, good quality suspension systems
are difficult to obtain because they involve a trade-off
between ride comfort and vehicle stability [1].
There are three major classifications of suspension
systems: passive, active, and semi-active [2]. Passive
suspensions using oil dampers are simple, reliable and
cheap. However, performance drawbacks are inevitable.
Active and semi-active have control algorithms which
Manuscript received February 1, 2015; revised April 23, 2015.
force the suspension system to achieve the behavior of
some optimized and reference systems. Active
suspensions use electro-hydraulic actuators which can be
commanded directly to generate a desired control force.
Semi-active suspensions employ semi-active damper
whose force is commanded indirectly through a
controlled change in the dampers’ properties.
Compared with the passive system, active suspensions
can offer high quality performance over a varied
frequency range [3], [4]. Simultaneously, the control
algorithms of active suspension systems have been
introduced in a wide range from primarily linear
quadratic regulator (LQR) controllers to smart and
intelligent controllers based on new findings of
computational intelligence.
In order to improve the performance of active
suspension systems, numerous research investigations
have been achieved on the design and control of active
suspension system algorithm in the last three decades. For
example, optimal control [5], adaptive control [6], [7],
model reference adaptive control [8], H∞ [9], LQG
control [10], fuzzy control [11], and sliding mode control
strategy [12], [13], feedback controller [14] and the
references therein, have been employed in active
suspension systems.
The main contribution of this paper is to enhance the
ride comfort and vehicle stability through using the SMC
control algorithm depends on the ideal sky-hook
reference model to calculate the variable actuator force.
The rest of this paper is organized as follows: an active
vehicle suspension based on the quarter model and the
dynamic equations of motion are explained in the next
section while the description of the SMC control
algorithm is provided in section III. Section VI introduces
the effectiveness of the proposed controller that
illustrated by simulation results. Finally, the conclusion is
given in section V.
II. QUARTER VEHICLE MODEL
The two-degree-of-freedom (2DOF) system that
represents the quarter vehicle suspension model is
illustrated in Fig. 1. It consists of an upper mass, bm ,
representing the body mass, as well as a lower mass, wm ,
representing the wheel mass and its associated parts. The
Journal of Traffic and Logistics Engineering Vol. 3, No. 2, December 2015
©2015 Journal of Traffic and Logistics Engineering 136doi: 10.12720/jtle.3.2.136-142
H. Metered and Z. Šika Helwan University, Cairo, Egypt
Czech Technical University in Prague, Prague, Czech Republic
Email: [email protected]; [email protected]
vertical motion of the system is described by the
displacements bx and wx while the excitation due to road
disturbance is rx . The suspension spring constant is
sk and the tyre spring constant is tk . Also, sc is the
damping coefficient of the passive damper whereas the
tyre damping is neglected and af represents the actuator
force. The data used here for the quarter vehicle system is
similar as ref. [15] and listed in Table I. Newton’s second
law is applied to the quarter vehicle model and the
equations of motion of bm and wm are:
0)()()(
0)()(
arwtwbswbsww
awbswbsbb
fxxkxxcxxkxm
fxxcxxkxm
(1)
The proposed active suspension system can be
represented in the state space form as follows:
ra xDfBxAx (2)
where, T
wbwb xxxxx ][ ,
w
s
w
s
w
ts
w
s
b
s
b
s
b
s
b
s
m
c
m
c
m
kk
m
k
m
c
m
c
m
k
m
kA
1000
0100
,
T
wb mmB
1100
, and
T
w
t
m
kD
000
Figure 1. Quarter-vehicle active suspension model.
TABLE I. Q [15].
Parameter Symbol Value (Unit)
Mass of vehicle body
Mass of vehicle wheel
Suspension stiffness
Damping coefficient
Tyre stiffness
bm 240 (kg)
Mass of vehicle wheel wm
36 (kg)
Suspension stiffness sk
16 (kN/m)
Damping coefficient sc
980 (Ns/m)
Tyre stiffness tk
160 (kN/m)
III. SLIDING MODE CONTROL ALGORITHM
The SMC algorithm applied in this paper depends on
the ideal sky-hook system, Fig. 2, as a reference model
[16]. As can be seen from this figure, the tyre flexibility
has been ignored for simplicity, since the tyre is much
harder than the suspension spring. The displacement of
the lower mass of the reference system is then taken to be
similar to wx , the displacement of the unsprung mass of
the actual system. Hence, the equation of motion of the
reference system is given by:
wrefbrefsrefbrefsrefbrefb xxkxcxm ,,,,,,
(3)
The major advantages of employing this control
technique are that the system can be designed to be robust
with respect to modeling imprecision, and it can be
synthesized for the linear and nonlinear active system. In
this study, the model reference design approach is chosen.
Therefore, a good reference needs to be considered. In
practice, the vehicle mass varies with the loading
conditions such as the number of riding persons and
payloads. Therefore, we consider that parameter
perturbations of the sprung mass exist in the system. The
possible bound of the mass can be assumed as follows:
bbob mmm and bob mm 2.0 (4)
where, mbo represents the nominal mass and ∆mb is the
uncertain mass. The uncertainty ratio 0.2 is selected here
for the purpose of application.
The sliding surface is defined as:
eeS (5)
where is a constant and
refbb xxe , (6)
Figure 2. Ideal sky-hook reference model
Further, the error between the estimated nominal value
and the real value is assumed to be bounded by
known :
.111
/1
/11
bo
b
bo
b
b
bo
m
m
m
m
m
m (7)
)/1( bob mm is maximum when bob mm 2.0 while
it is minimum when bob mm 2.0 .
So, .25.1
Journal of Traffic and Logistics Engineering Vol. 3, No. 2, December 2015
©2015 Journal of Traffic and Logistics Engineering 137
UARTER EHICLE USPENSION ARAMETERS V S P
The objective of the model reference approach is to
develop a control algorithm which forces the plant to
follow the dynamics of an ideal model. In order to ensure
the state will move toward and reach the sliding surface, a
sliding condition must be defined. The sliding condition
is considered as in ref. [16]
SS S (8)
where is a positive constant. It constrains trajectories
to point towards the sliding surface. In particular, once on
the surface, the system trajectories remain on the surface,
i.e. .0S
From equations (1) and (5),
.)( ,
,
exm
fxx
m
k
exxeeS
refb
b
awb
b
s
refbb
(9)
The best approximation ou of a control law that
would achieve 0S is thus:
,[ ( ) ]so bo b w b ref
bo
ku m x x e x
m (10)
In order to satisfy the sliding condition, despite mass
uncertainty, a term discontinuous across the surface is
added to the expressionou . The desired control
forceaf can be expressed as
)sgn(SKuf oa (11)
where sgn is the signum function.
Equation (11) can be expressed as
)]sgn([ SKumf oboa (12)
where
bobo
oo
m
KKand
m
uu
To avoid the chattering problem, a saturation function
could be applied to equation (11). Then the equation (11)
can be written as:
S
S
SKf
SKvalff
a
a
a sgn
)(
0
0 (13)
Now, the range of the switching gain K to make the
system stable is to be found.
Equation (8) can be interpreted as
(14)
.)]sgn([)( , exSKum
mxx
m
kS refbo
b
bowb
b
s
.)]sgn()
)([()(
,, exSKxe
xxm
k
m
mxx
m
k
refbrefb
wb
bo
s
b
bowb
s
s
).](1[)]sgn([
)]([)(
, exm
mSK
m
m
xxm
k
m
mxx
m
k
refb
b
bo
b
bo
wb
bo
s
b
bowb
b
s
).(1)]sgn([ , exm
mSK
m
mS refb
b
bo
b
bo
(15)
when 0, SS , Equation (14) becomes
)(1)]sgn([ , ex
m
mSK
m
mrefb
b
bo
b
bo (16)
multiply Eq.(16) by
bo
b
m
m it becomes,
bo
brefb
bo
b
m
mex
m
mK
)(1 ,
)(1 , exm
m
m
mK refb
bo
b
bo
b
(17)
Similarly, when SS ,0 Equation (14) becomes
)(1)]sgn([ , ex
m
mSK
m
mrefb
b
bo
b
bo (18)
multiply Eq.( 18) by
bo
b
m
m it becomes,
bo
brefb
bo
b
m
mex
m
mK
)(1 ,
)(1 , exm
m
m
mK refb
bo
b
bo
b
(19)
Combining two cases from Equations (17) and (19),
bo
brefb
bo
b
m
mex
m
mK )(]1[ ,
bo
brefb
bo
b
m
mex
m
m
,1
ex refb
,)1(
(20)
From equation (10), the right hand side of equation (20)
becomes
w
so
sb
so
sorefb x
m
kx
m
kuex )1()1( ,
w
bo
sb
bo
so x
m
kx
m
ku )1()1()1( (21)
K should be bounded by Equation (21), so it can be
expressed to be:
w
bo
b
bo
o
bo
xm
kx
m
ku
mK 111
)1( (22)
The SMC described above is summarized in Fig. 3.
IV. RESULTS AND DISCUSSION
Suspension working space (SWS), vertical body
acceleration (BA), and tyre deformation (TD) are the three
main performance criteria in vehicle suspension design
that govern the ride comfort and vehicle stability. Ride
comfort is closely related to the BA. To certify good
vehicle stability, it is required that the tyre’s dynamic
deformation )( rw xx should be low [17]. The structural
characteristics of the vehicle also constrain the amount of
S
S
0
0
S
Swhen
Journal of Traffic and Logistics Engineering Vol. 3, No. 2, December 2015
©2015 Journal of Traffic and Logistics Engineering 138
SWS within certain limits. The goal is to minimize SWS,
BA, and TD in order to improve suspension performance.
A. Time Domain Analysis
This section studies suspension performance for two
cases of vibration control; passive suspension and active
damped suspension using the proposed SMC. The above-
mentioned performance criteria are used to quantify the
relative performance of these control methods. Since the
passive suspension is used as a base-line for comparisons.
The value of sc is selected depends on the median-sized
automotive applications [15].
A well-known real-world road bump is used in this
section to reflect the transient response characteristics
which defined by [18] as:
otherwise ,0
5.00.5for ,))5.0(cos(1c
br
r V
dtta
x
(23)
where a is the half of the bump amplitude, bcr dV /2 ,
bd is the bump width and cV is the vehicle velocity. In
this study a = 0.035 m, bd = 0.8 m, cV = 0.856 m/s, as in
[18].
The time history of the suspension system response
under road bump disturbance excitation is shown in Fig.
4. The displacement of the road input signal is shown in
Fig. 4(a) and the SWS, BA, and TD responses are given in
Figs. 4 (b, c, and d) respectively. The latter figures show
the comparison between the controlled active using SMC
controller and the passive suspension systems. From
these results it is clearly seen that the SMC controlled
active suspension system can dissipate the energy due to
bump excitation very well, cut down the settling time,
and improve both the ride comfort and vehicle stability.
Figure 3. Schematic diagram of the sliding mode control algorithm
0 1 2 3 4 50
0.02
0.04
0.06
0.08
Time (s)
Ro
ad
Dis
pla
ce
me
nt (m
)
(a)
0 1 2 3 4 5-0.04
-0.02
0
0.02
0.04
Time (s)
Su
sp
en
sio
n W
ork
ing
Sp
ace
(m
)
(b)
Passive
SMC
0 1 2 3 4 5-3
-2
-1
0
1
2
3
Time (s)
Bo
dy A
cce
lera
tio
n (
m/s
2)
(c)
Passive
SMC
0 1 2 3 4 5-4.5
-3
-1.5
0
1.5
3
4.5x 10
-3
Time (s)
Tyre
De
fle
ctio
n (
m)
(d)
Passive
SMC
Figure 4. System response under road bump excitation. (a- Road Displacement b- SWS c- BA d- TD)
Also, Fig. 4 shows that the proposed active suspension
controlled using the SMC have the lowest peaks for the
SWS, BA, and TD, demonstrating their effectiveness at
improving the ride comfort and vehicle stability. The
controlled system using SMC controller can reduce
maximum peak-to-peak of SWS, BA, and TD by 15.9 %,
46.4 % and 57.2 %, respectively, compared with the
passive suspension system. Figure 5 shows the
improvement percentage of PTP for the active suspension
controlled using the SMC compared to the passive
suspension system. The results confirm that the active
vehicle suspension system controlled using SMC offers a
superior performance.
B. Frequency Domain Analysis
Road irregularities are the main source of disturbance
that causes unwanted vehicle body vibrations. These
irregularities are usually randomly distributed. The
random nature of the road irregularities is due to
construction tolerances, wear and environmental action.
The road surface irregularities have naturally been
Journal of Traffic and Logistics Engineering Vol. 3, No. 2, December 2015
©2015 Journal of Traffic and Logistics Engineering 139
described as a white noise random road profile defined by
[18] as:
nrr VWVxx (24)
where nW is white noise with intensity V 22 , is the
road irregularity parameter, and 2 is the covariance of
road irregularity. In random road excitation,
( -1m 0.45= and 22 mm 300= ) the values of road
surface irregularity were selected assuming that the
vehicle moves on the paved road with the constant speed
m/s 20 =V , as in [18].
In order to improve the ride comfort, it is important to
isolate the vehicle body from the road disturbances and to
decrease the resonance peak of the body mass around 1
Hz which is known to be a sensitive frequency to the
human body [19]. Moreover, in order to improve the
vehicle stability, it is important to keep the tyre in contact
with the road surface and therefore to decrease the
resonance peak around 10 Hz, which is the resonance
frequency of the wheel [19]. In view of these
considerations, the results obtained for the excitation
described by equation (24) are presented in the frequency
domain.
Figure 5. % improvements of PTP values compared to passive system.
2 4 6 8 10 12 14 160
0.01
0.02
0.03(a)
Frequency (Hz)
Su
sp
en
sio
n W
ork
ing
Sp
ace
(m
)
Passive
SMC
2 4 6 8 10 12 14 160
0.75
1.5
2.25
(b)
Frequency (Hz)
Bo
dy A
cce
lera
tio
n (
m/s
2)
Passive
SMC
2 4 6 8 10 12 14 160
1.2
2.4
3.6x 10
-3
(c)
Frequency (Hz)
Tyre
De
fle
ctio
n (
m)
Passive
SMC
Figure 6. System response under random road excitation. (a- SWS b- BA c- TD)
Fig. 6 shows the modulus of the Fast Fourier
Transform (FFT) of the SWS, BA, and TD responses over
the range 0.5-16 Hz. The FFT was appropriately scaled
and smoothed by curve fitting as done in [20]. It is
evident that the lowest resonance peaks for body and
wheel can be achieved using the proposed SMC
controller. According to these figures, just like for the
bump excitation, the controlled system using SMC
controller can dissipate the energy due to road excitation
very well and improve both the ride comfort and vehicle
stability.
In the case of random excitation, it is the root mean
square (RMS) values of the SWS, BA, and TD, rather than
their peak-to-peak values, that are relevant. The
controlled system using SMC controller has the lowest
levels of RMS values for the SWS, BA, and TD. SMC
controller can reduce maximum RMS values of SWS, BA,
and TD by 33.1 %, 27.9 and 44.5 %, respectively,
compared with the passive suspension system. Figure 7
shows the improvement percentage of RMS for the active
suspension controlled using the SMC compared to the
passive suspension system. The results again confirm
that the semi-active vehicle suspension system controlled
using SMC controller can give a superior response in
terms of ride comfort and vehicle stability.
Figure 7. % improvements of RMS values compared to passive system
C. Uncertainity Analysis
In order to prove the robustness of the proposed SMC
for vibration control of vehicle active suspension, the
sprung mass is increased by 30%, the suspension spring
constant is reduced by 20%, and also, the damping
coefficient of the passive damper is reduced by 20%. In
this test, the road displacement was simulated as a band-
limited Gaussian white-noise signal which was band
Journal of Traffic and Logistics Engineering Vol. 3, No. 2, December 2015
©2015 Journal of Traffic and Logistics Engineering 140
limited to the range 0–3 Hz; this frequency range is
appropriate for automotive applications and previous
published work used a similar range (0.4–3 Hz such as in
reference [21]), with 0.02m amplitude, as in reference
[21], this random road is shown in Fig. 8 (a). The zoomed
responses of SWS, BA, and TD are shown in Fig. 8 (b, c,
and d), respectively. Similar to the above results, the
proposed SMC still offer a significant improvement under
the existence of parameter uncertainty.
0 2 4 6 8-0.02
-0.01
0
0.01
0.02
Time (s)
Ro
ad
Dis
pla
ce
me
nt (m
)
(a)
3 3.5 4 4.5 5 5.5 6-0.033
-0.022
-0.011
0
0.011
0.022
0.033
Time (s)
Su
sp
en
sio
n W
ork
ing
Sp
ace
(m
)
(b)
Passive
SMC
3 3.5 4 4.5 5 5.5 6-1.5
-1
-0.5
0
0.5
1
1.5
2
Time (s)
Bo
dy A
cce
lera
tio
n (
m/s
2)
(c)
Passive
SMC
3 3.5 4 4.5 5 5.5 6-3
-2
-1
0
1
2
3x 10
-3
Time (s)
Tyre
De
fle
ctio
n (
m)
(d)
Passive
SMC
Figure 8. System response under uncertain parameters. (a- Road Displacement b- SWS c- BA d- TD)
V. CONCLUSION
In this paper, a sliding mode controller (SMC) is
applied as an effective control technique for active
vehicle suspension system to improve the ride comfort
and road holding. A mathematical model of an active
damped quarter-vehicle suspension system was derived
and simulated using Matlab/Simulink software. The
proposed controller is applied to force the system to
emulate the performance of an ideal reference system
depends on the ideal sky-hook system behavior. The
system performance generated by the proposed SMC
algorithm is compared with the passive suspension
system. System performance criteria were assessed in
time and frequency domains in order to prove the
suspension efficiency under bump and random road
excitations. Theoretical results showed that the SMC
controller potentially offers a significantly superior ride
comfort and road holding over the passive suspension
system. Under the presence of parameter uncertainties
due to the increased of the sprung mass and depreciated
suspension stiffness and damping, desired performance is
still achieved for the proposed SMC.
ACKNOWLEDGMENT
This publication was supported by the European social
fund within the frame work of realizing the project
"Support of inter-sectoral mobility and quality
enhancement of research teams at Czech Technical
University in Prague", CZ.1.07/2.3.00/30.0034. Period of
the project’s realization 1.12.2012 – 30.6.2015.
REFERENCES
[1] T. Gillespie, Fundamentals of Vehicle Dynamics, SAE
International, 1993.
[2] J. Cao, H. Liu, P. Li, and D. Brown, “State of the art in vehicle
active suspension adaptive control systems based on intelligent methodologies,” IEEE Trans. on Intelligent Transportation
Systems, vol. 9, pp. 392-405, 2008.
[3] J. Cao, P. Li, and H. Liu, “An interval fuzzy controller for vehicle
active suspension systems,” IEEE Trans. on Intelligent Transportation Systems, vol. 11, pp. 885-895, 2010.
[4] H. Gao, J. Lam, and C. Wang, “Multi-objective control of vehicle active suspension systems via load-dependent controllers,”
Journal of Sound and Vibration, vol. 290, pp. 654-675, 2006.
[5] D. Hrovat, “Survey of advanced suspension developments and related optimal control applications,” Automatica, vol. 33, pp.
1781-1817, 1997. [6] I. Fialho and J. Balas, “Road adaptive active suspension design
using linear parameter-varying gain-scheduling,” IEEE Trans. on
Control Systems Technology, vol. 10, pp. 43-54, 2002. [7] R. Rajamani and J. Hedrick, “Adaptive observers for active
automotive suspensions: Theory and experiment,” IEEE Trans. on Control Systems Technology, vol. 3, pp. 86-93, 1995.
[8] B. Mohan, J. Modak, and S. Phadke, “Vibration control of
vehicles using model reference adaptive variable structure control,” Advances in Vibration Engineering, vol. 2, pp. 343-361,
2003. [9] H. Gao, W. Sun, and P. Shi, “Robust sampled-data H∞ control for
vehicle active suspension systems,” IEEE Trans. on Control
Systems Technology, vol. 18, pp. 238-245, 2010. [10] S. Chen, R. He, H. Liu, and M. Yao, “Probe into necessity of
active suspension based on LQG control,” Physics Procedia, vol. 25, pp. 932-938, 2012.
Journal of Traffic and Logistics Engineering Vol. 3, No. 2, December 2015
©2015 Journal of Traffic and Logistics Engineering 141
[11] P. Gong, X. Teng, and C. Yun, “Fuzzy logic controller for truck
active suspension and related optimal control method,” Applied
Mechanics and Materials, vol. 441, pp. 821-824, 2013. [12] S. Vaijayanti, B. Mohan, P. Shendge, and S. Phadke, “Disturbance
observer based sliding mode control of active suspension systems,” Journal of Sound and Vibration, vol. 333, pp. 2281-
2296, 2014
[13] Q. Yun, Y Zhao, and H. Yang, “A dynamic sliding-mode controller with fuzzy adaptive tuning for an active suspension
system,” Proc. IMechE Part D: Journal of Automobile Engineering, vol. 221, pp. 417-428, 2007
[14] R. Shamshiri and I. Wan, “Design and analysis of full-state
feedback controller for a tractor active suspension: Implications for crop yield,” Int. J. Agric. Biol., vol. 15, pp. 909-914, 2013.
[15] S. Tu, R. Hu, and S. Akc, “A study of random vibration characteristics of the quarter-car model,” Journal of Sound and
Vibration, vol. 282, pp. 111-124, 2005.
[16] A. Lam and H. Liao, “Semi-active control of automotive suspension systems with magnetorheological dampers,”
International Journal of Vehicle Design, vol. 33, pp. 50-75, 2003.
[17] R. Rajamani, Vehicle Dynamics and Control, Springer Science
and Business Media, New York, 2006. [18] S. Choi and W. Kim, “Vibration control of a semi-active
suspension featuring electrorheological fluid dampers,” Journal of Sound and Vibration, vol. 234, pp. 537-546, 2000.
[19] D. Fischer and R. Isermann, “Mechatronic semi-active and active
vehicle suspensions,” Control Engineering Practice, vol. 12, pp. 1353-1367, 2004.
[20] H. Metered, “Application of nonparametric magnetorheological damper model in vehicle semi-active suspension system,” SAE
International Journal of Passenger Cars, Mechanical Systems, vol.
5, pp. 715-726, 2012. [21] H. Metered, P. Bonello, and S. Oyadiji, “An investigation into the
use of neural networks for the semi-active control of a magnetorheologically damped vehicle suspension,” Proceedings
of the Institution of Mechanical Engineers, Part D: Automobile
Engineering, vol. 224, pp. 829-848, 2010.
Hassan Metered was born in Alexandria, Egypt. He obtained his B.Sc. and M.Sc.
degrees in Automotive Engineering from
Helwan University, Egypt, in 1998 and 2004, respectively. He has a Ph.D. degree in
Mechanical Engineering from Manchester University, UK, in 2010. From 2010 to 2013
he was a Lecturer of vehicle dynamics and
control at Helwan University. From Jan. 2014 until now, he is a Postdoctoral Senior
Researcher at the Czech Technical University in Prague, Czech Republic.
His interested research areas are active & semi-active vehicle
suspension systems using smart fluid dampers controlled with advanced control strategies (e.g. Sliding Mode Control, Optimal Pole Placement
and Linear Quadratic Gaussian, Fuzzy Logic Control, and optimized PID), Mechatronics systems, Real-time Hardware in the loop simulation
(HILS) of Mechanical systems, modeling and identification of non-
linear systems, Artificial intelligence application in mechanical systems such as neural networks and ANFIS.
Zbynek Šika: 1990 Ing., FME CTU in
Prague, diploma thesis. 1999 Ph.D., FME
CTU in Prague, doctoral thesis “Synthesis and Analysis of Redundant Parallel Robots”. 2005
Doc., FME CTU in Prague, inaugural dissertation “Active and Semi-active
Suppression of Machine Vibration”. 2010
Prof., branch Applied mechanics, professor lecture “Optimization of Mechanical and
Mechatronical Systems”.
1994~05 Assistant Professor at the Dept. of Mechanics, FME, CTU in
Prague. 2005-10 Associated Professor at the Dept. of Mechanics,
Biomechanics and Mechatronics, FME, CTU in Prague. 2010~today Full Professor at the Dept. of Mechanics, Biomechanics and
Mechatronics, FME, CTU in Prague. Areas of the scientific activities: calibration and control of robots, active
and semi-active vibration control of machines, synthesis and
optimization of mechanical systems, redundantly actuated parallel kinematic machines, and vehicle system dynamics & control.
Selected research projects participated by applicant within last 5 years:
GAČR project 13-39057S, GAČR project P101/11/1627, TAČR project
TE01020075, and EC FP7 projects.
Multibody system dynamics and kinematics, Redundantly actuated parallel kinematic machines, and Vehicle system dynamics & control.
Journal of Traffic and Logistics Engineering Vol. 3, No. 2, December 2015
©2015 Journal of Traffic and Logistics Engineering 142
