active suspension system
TRANSCRIPT
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Active Suspension System Siva Srinivas Anand Selvarajan
MSc Systems Engineering and Engineering Management
South Westphalia University of Applied Sciences
Soest, Germany
Abstract – Fierce competition on the today’s
automotive industry has forced companies to research
alternative strategies to traditional passive suspension
systems. Semi-active and active suspensions are
developed in order to improve the ride comfort and road
holding capability, rather than a conventional static
damper and spring suspension system. A two degree of
freedom (DOF) quarter car model is constructed on the
basis of a four-wheel independent suspension concept to
simulate the actions of an active suspension system. The
main purpose of a suspension system is to support the
vehicle body and increase comfort performance. The
aim of the paper is to demonstrate the application of
intelligent techniques such as Fuzzy Logic Controller
(FLC) and Active Force Control (AFC) to control the
continuous damping of an automotive suspension system.
The proposed control methods were evaluated and
compared to examine the effectiveness of vibration
suppression in the suspension system. The resulting
fuzzy active control strategy provides superior results in
comparison with the fuzzy logic and passive suspension
system.
Keywords: Fuzzy logic controller, Quarter car
suspension, Active force control, Pneumatic actuator.
1. INTRODUCTION
The Suspension system is one of the essential parts of
vehicles, and its main objectives are to ensure high-
quality ride comfort by isolating the vehicle body from
road disturbances; to maintain good road holding
ability; to provide finest vehicle handling capability
and to support the mass of vehicle [1]. The automotive
suspension system is classified into three types:
passive suspension, semi-active suspension and active
suspension system [2]. However, due to the fixed
characteristics such as damper coefficient and spring
stiffness in passive suspensions, it cannot control the
road holding capability and handling of the vehicle
effectively [3] [4]. To the contrary, an active
suspension system has an actuator connected in
parallel to both the spring and damper to inject energy
into the system. The adaptation potential of an active
suspension is one of the key advantages over passive
suspension where the suspension characteristics can
be attuned while driving to accommodate the diverse
terrain conditions of the road being traversed [5].
Fierce competition is driving today's automotive
industry so as to produce cutting edge suspension
system models. Therefore, to overcome the complex
suspension system problems, several control methods
have been proposed. For instance, control approaches
such as adaptive control, Fuzzy Logic Controller
(FLC) and non-linear control are proposed to resolve
the arising problems [6].
In this paper, a suspension system for a quarter car
model is considered, and an active force control
system is designed to reduce large vehicle body
oscillations and to provide better handling while the
vehicle is experiencing any road disturbance such as
terrain irregularities, uneven pavement and cracks.
Thus, the arrangement of this paper is as follows: the
problem is formulated in Section 2, the quarter car
model and controller design are described in
subsequent Sections 3 and 4 respectively, and the
paper is concluded in Section 5 [7].
2. PROBLEM DESCRIPTION
Many researchers have come up with a variety of
vehicle suspension strategies through simulation and
experimental work with the aim to improve the ride
quality and vehicle stability in [8] [9]. Almost all of
the implemented and proposed works for complicated
suspension models are considering uncertainty and
nonlinearity in the dynamics. Indeed, most of them
show a margin of improvement that the proposed
active suspension model could deliver, primarily to
the ride quality and handling aspects, at the expense of
creating additional loads to the system when compared
with the linear active suspension [10]. The use of
several intelligent control approaches such as using a
neural network and fuzzy logic further enhances the
research. Nevertheless, both approaches are often
limited to simulation works in laboratories owing to
inherent computation burden. Furthermore, both
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methods are mathematically intensive and not feasible
in real time implementation [11] [12]. Thus, the aim
of this paper is to demonstrate a unique method to
control a real-time active suspension system, based on
the Active Force Control (AFC) strategy to an active
suspension system. The proposed control method is
capable of improving ride comfort and vehicle road
holding capability through reducing the sprung mass
(car body) motion of a quarter car suspension system
model [12].
3. QUARTER CAR MODEL
In this study, the vehicle model considered is a quarter
car model. Typically, a suspension system consists of
sprung mass (car body), unsprung mass (wheel, brake,
steering hub), spring and dampers. The passive
suspension system for quarter car model consists of
one wheel, one-fourth of the body mass and
components of suspension as shown in Figure 1(a).
The active suspension system for quarter car model
has a hydraulic actuator in parallel with the spring as
shown in Figure 1(b).
FIGURE 1: Passive (a) and Active (b) quarter car models
[13]
The quarter car system modelling assumptions are as
follows: The tire is constantly in contact with the
surface of the road and the friction effect is ignored so
that the residual damping is not considered into
vehicle modelling; the tire is modelled as a linear
spring without damping; the wheel and body have no
rotational motion; the spring and damper are linear in
behaviour [13]. Thus, the quarter car model with two
degrees of freedom model uses a unit to create the
control force between wheel mass and body mass.
The motion equations of the wheel and car body are as
follows:
mbz’’b = fa − k1 (zb − zw ) − c (z’b – z’w )
mwz’’w = −fa + k1 (zb − zw ) − k2 (zw – zr)
EQUATION 1, 2: Motion equations of the wheel and car
body [14]
The following constants and variables which respect
to the static equilibrium position
FIGURE 2: Suspension system block diagram [15]
mb car body mass (one quarter of the total body
mass), 250 kg
mw wheel mass, 35 kg
k1 spring stiffness of the body, 16 000 N/m
k2 spring stiffness of the wheel, 160 000 N/m
fa desired force by the cylinder
cs damping ratio of the damper, 980 Ns/m
zr displacement of the road
zb displacement of the body
zw displacement of the wheel
In order to model the road input, assume that the
vehicle is moving with a constant forward speed. Then
the vertical velocity can be taken as a white noise
process which is nearly true for most of the real
roadways. The following variables are considered so
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as to transform the motion equations of the quarter car
model into a state space model.
x=[x1, x2, x3, x4]T
EQUATION 3: Quarter car motion equation [14]
where:
x1= zb-zw body displacement
x2= zw-zr wheel displacement
x3= z’b absolute velocity of the body
x4= z’w absolute velocity of the wheel
The next step is to write the motion equations of the
quarter car model for the active suspension in state
space form.
FIGURE 3: State space form [14]
4. CONTROLLER DESIGN
Usually, the main controller design of a suspension
system is very hard because it is crucial to deal with
the complicated system to obtain the good control
effect. A fuzzy controller is designed in order to enable
the air suspension control system with a definite
degree of adaptive capacity. The input variables of the
fuzzy controller were sprung mass velocity and
suspension deflection while the output variable was air
spring force. Hence to describe two input and output
variables five fuzzy language subsets were obtained
through simulation. Trapezoidal and triangle functions
were the subsets of membership functions. By means
of the fuzzy conditional statement the rule is in the
form of the linguistic variables. The characteristic of
the passive suspension system is used to derive each
rule. The rule base of the fuzzy is a combination of all
possible control rules and is abridged in Table 1. As a
fuzzy implication function, Mamdani’s minimum
operation rule is used. A defuzzification method called
centre of gravity method is used since a non-fuzzy
value of control is vital [14].
EQUATION 4: Centre of gravity [14]
fa – Air spring force
𝜇𝐷 (𝑓) – Membership function
TABLE 1: Rule Base [14]
41. Active Force Control (AFC) system.
In order to keep the system stable and robust through
the compensating action of control strategy for the
unknown disturbances, an AFC system is designed.
Mass estimator plays a vital role in defining the
efficiency of the AFC strategy because the body
acceleration and the actuator force are easily obtained.
The two inputs of the AFC scheme are the active force
pneumatic actuator and body acceleration
components. Three controller loops are used in AFC
system. Active force control loop is integrated with
inner Fuzzy Tracking controller and Outer Fuzzy
Logic controller. Hence, to compute the optimum
target commanded force in the outer loop, an FLC is
used. A fuzzy controller is used to carry out the force
tracking of the pneumatic actuator. AFC uses Fuzzy
Logic (Intelligent Method), Sugeno type Fuzzy
Inference System for the mass estimation.
Membership function representing the suspension
deflection and estimated mass are chosen as Gaussian
functions. The singleton value of the estimated mass
is achieved as output. A detailed description of the
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overall derivation of important mathematical
equations and stability criterion are explained in [16]
[17].
Q = F – Ma
EQUATION 5: Disturbance force estimation [17]
Q – Estimated disturbance force
F – Measured Force
M – Estimated Mass
a – Measured acceleration
If the above parameters are estimated properly, then a
guaranteed robust AFC performance is accomplished
[17] [18].
FIGURE 4: Block diagram of AFC system for air spring
suspension [14].
The geometrical road profile is considered to generate
a random base excitation for the 3 – degree of freedom
active suspension simulation disturbance. The ride
comfort is compared with the passive suspension to
verify the fuzzy air spring actuator controlled
suspension. For modelling the non-physical two
degree of freedom quarter car model with actuators in
one analysis loop. The suspension deflection is
considered to verify the system. The vertical
acceleration of seat and the vertical acceleration of
sprung mass are shown in the Fig 4&5 respectively.
From the figure, we can easily identify that both the
AFC system and Fuzzy controlled are of the same
trend, because of the fact that the force between the
sprung and unsprung mass developed by the
pneumatic actuator develops is of desired level. The
root mean square acceleration of the system also
reduces the vibration due to the lower transmissibility
of air spring [19] [20].
FIGURE 5: Acceleration of seat for random road [19]
FIGURE 6: Acceleration of sprung mass for random road
[19]
5. CONCLUSION
Thus, we have discussed the designing of active force
control loop for pneumatic air spring actuator in this
paper. In order to improve and control the ride comfort
and road handling in a quarter car model, Mamdani
and Sugeno fuzzy control techniques are used.
MatLab/Simulink is used to obtain the simulation
results. Evaluation among the fuzzy, passive and
active force control systems shows that the
performance of air spring suspension with AFC
system can improve the road handling and the ride
comfort.
6. REFERENCES
[1].R. Rajamani, Vehicle Dynamics and Control, 2nd ed. Boston,
MA: Springer US, 2012.
[2].N. A. Milad Geravand, “Fuzzy Sliding Mode Control for
applying to active vehicle suspensions,” vol. 5, no. 1, pp. 48–57,
http://www.wseas.us/e-library/transactions/control/2010/42-
131.pdf, Jan.2010.
[3].M. Mailah, “Simulation of a suspension system with adaptive
fuzzy active force control,” Int j simul model, vol. 6, no. 1, pp. 25–
36, 2007.
[4].Mouleeswaran Senthil kumar, “Genetic algorithm-based
proportional derivative controller for the development of active
5
suspension system,” vol. 36, no. 1392-124X, pp. 58–67,
http://itc.ktu.lt/itc361/Sentil361.pdf, 2007.
[5].Gigih Priyandoko, Musa Mailah, H Jamaluddin, “Vehicle active
suspension system using skyhook adaptive neuro active force
control,” vol. 23, no. 3, pp. 855–868,
http://www.researchgate.net/profile/Hishamuddin_Jamaluddin/pub
lication/49910352_Vehicle_ac
tive_suspension_system_using_skyhook_adaptive_neuro_active_f
orce_control/links/00b7d52b0ec f928e82000000.pdf#page=67,
2009.
[6].Ayman A. Aly, and Farhan A. Salem, “Vehicle Suspension
Systems Control: A Review,” vol. 2, no. 2, pp. 46–54,
http://researchpub.org/journal/jac/number/vol2-no2/vol2-no2-
6.pdf, Jul. 2013.
[7]. N. Patrascoiu, A. Zaharim, and K. Sopian, Latest trends in
circuits, control and signal processing: Proceedings of the 12th
international conference on instrumentation, measurement, circuits
and systems (IMCAS '13), proceedings of the 13th international
conference on robotics, control and manufacturing technology
(ROCOM '13), proceedings of the 13th international conference on
multimedia systems & signal processing (MUSP '13) : Kuala
Lumpur, Malaysia, April 2-4, 2013 / editors: Nicolae Patrascoiu,
Azami Zaharim, Kamaruzzaman Sopian.
[8].A. Alleyne and J. K. Hedrick, “Nonlinear adaptive control of
active suspensions,” IEEE Trans. Contr. Syst. Technology, vol. 3,
no. 1, pp. 94–101, 1995.
[9].T. Yoshimura and A. Takagi, “Pneumatic active suspension
system for a one-wheel car model using fuzzy reasoning and a
disturbance observer,” (eng), Journal of Zhejiang University.
Science, vol. 5, no. 9, pp. 1060–1068, 2004.
[10]. D. Hrovat, “Survey of Advanced Suspension Developments
and Related Optimal Control Applications11This paper was not
presented at any IFAC meeting. This paper was recommended for
publication in revised form by Editor Karl Johan Åström, 22 Simple,
mostly LQ-based optimal control concepts gave useful insight about
performance potentials, bandwidth requirements, and optimal
structure of advanced vehicle suspensions. The present paper
reviews these optimal control applications and related practical
developments,” Automatica, vol. 33, no. 10, pp. 1781– 1817, 1997.
[11].D. V. F.J. D’Amato, “Fuzzy control for active suspensions,”
Mechatronics, vol. 10, pp. 897–920, 2000.
[12].M. Zhao, International Conference on Neural Networks and
Brain, 2005, ICNN&B '05: 13 - 15 Oct. 2005, [Beijing, China].
Piscataway, NJ: IEEE Operations Center, 2005.
[13].Nusantoro, G. D, & Priyandoko, G, “PID State Feedback
Controller of a Quarter Car Active Suspension System,” journal of
Basic and Applied Scientific Research, vol. 1, no. 11, pp. 2304–
2309,
https://scholar.google.de/scholar?oi=bibs&hl=en&cluster=220928
206406818257, 2011.
[14].P. Sathiskumar, J. Jancirani, John Dennie, “Reduction of axis
acceleration of quarter car suspension using pneumatic actuator and
active force control technique,” Journal of VibroEngineering, vol.
16, no. 3, pp. 1416–1423, 2013.
[15].A. A. Aly, “Robust Sliding Mode Fuzzy Control of a Car
Suspension System,” IJITCS (International Journal of Information
Technology and Computer Science), vol. 5, no. 8, p. 46–53, 2013.
[16].L. P. Rajeswari K., “Simulation of suspension system with
intelligent active force control,” in IEEE International Conference,
2010.
[17].B. J. Hewit J. R., “Fast dynamic decoupled control for robotics
using active force control. Transactions on Mechanisms and
Machine Theory,” vol. 16, no. 5, pp. 535-542, 1981.
[18].B. P. O. S. Metered H., “The experimental identification of
magnetorheological dampers and evaluation of their controllers,”
Mechanical Systems and Signal Processing, vol. 24, no. 4, pp. 976
- 994, 2010.
[19].J. J. Gavriloski V., “Dynamic behavior of an air spring
elements.,” Machines, Technologies, Materials, International
Virtual Journal, pp. 24-27, 2006.
[20].G. A. S. V. G. V. K. V. Ramji K., “Road roughness
measurements using PSD approach,” Journal of the Institution of
Engineers, vol. 85, pp. 193-201, 2004.