Fuzzy/PSO Based Washout Filter for Inertial Stimuli

Fuzzy/PSO Based Washout Filter for Inertial Stimuli
SENSORCOMM 2013 : The Seventh International Conference on Sensor Technologies and Applications
Fuzzy/PSO Based Washout Filter for Inertial Stimuli Restitution in Flight Simulation
Khaled Fellah, Mohamed Guiatni and Yacine Morsly
Control Laboratory
Ecole Militaire Polytechnique
Bordj El Bahri, Algiers, 16111, Algeria
Email: khaled.fellah@gmail.com, mohamed.guiatni@gmail.com, ymorsly@yahoo.fr
Abstract—The aim of this study is to present a new approach
using Particle Swarm Optimization (PSO) algorithm and fuzzy
logic for motion cueing considering both the physical limits
of restitution platforms and realistic sensations. We added the
necessary software in order to restitute the specific force based
on a virtual aircraft. We used Microsoft Flight Simulator software (MSFS) and built-in data structure and methods. Results
using the overall simulation are presented and evaluated on
a motion platform. Interesting sensations have been recorded,
which enhance the realism of the simulation. The obtained results
indicate that the proposed PSO/Fuzzy approach improves the
performance of the classical washout filter based motion cueing
algorithm.
Keywords—Inertial stimuli; flight simulation; washout filter;
fuzzy logic; particle swarm optimization.
I.
I NTRODUCTION
In the last decades, the use of driving simulation for
traffic safety, vehicle design and driver perception studies has
expanded rapidly [1] [2] [3]. This is largely because simulation
saves engineering time and costs, and can be used for studies
of road and traffic safety. Simulation is also a useful and
indispensable tool for aviation research and training. It has
evolved and matured over the last forty years in equal pace
with developments in the aerospace industry. Flight simulation
allows pilots to fly in simulated conditions, without the costs
and safety issues that go with performing real flight.
In addition, recent psychophysical studies have revealed
an unexpectedly important contribution of vestibular cues in
distance perception and steering, prompting a re-evaluation of
the role of visuo-vestibular interaction in driving simulation
studies.
When flight simulation and research are combined, the objective is to measure the human performance in the simulated
environment [4]. Research will pose certain requirements on
the use of simulation hardware and software. It requires generic
tools that can be adjusted to the evolving insight in topics.
This implies that flight simulators (hardware) and simulation
models (software) used for research will often be a tradeoff between realism and flexibility [5]. Therefore, a flight
simulator must include an aircraft model, a display capability
and control hardware [6]. The aircraft model is implemented
as software [7]. Three examples of commonly used flight
simulator packages are Microsoft Flight Simulator (sometimes
abbreviated to MSFS or FS), FlightGear and XPlane.
Copyright (c) IARIA, 2013.
ISBN: 978-1-61208-296-7
The flight controls are hardware providing input to the
aircraft model. In most cases they are used along with an
aircraft-like control input such as a joystick, yoke and rudder
pedals.
While a visual system alone can provide motion cues at low
frequency, physical motion stimuli are necessary to provide
higher frequency cues in the range sensitive to the vestibular
and somatosensory systems.
The adjunction of high fidelity motion cues from a moving
platform in conjunction with visual motion cues have been
shown to produce a rapid onset of vection, or the illusion of
motion, thus reducing the delay associated with visual motion
alone.
A key element in providing physical stimuli in flight
simulators is the cueing algorithm konwn as washout filter that
produces the drive signals used to control the motion system
hardware.
In this paper, we propose a new approach for motion cueing
considering both the physical limits of restitution platforms and
realistic sensations objective. The proposed approach integrates
fuzzy logic and Particle Swarm Optimization (PSO) algorithm
to the classical washout filter. This approach is an improvement
of those presented in the litterature. We added the necessary
software in order to restitute the specific force based on a
virtual aircraft. We used Microsoft Flight Simulator software
(MSFS) and built-in data structure and methods. Results using
the overall simulation are presented and evaluated on a motion
platform.
This paper is organised as follows: Section II gives a background about the evolution of researches on the washout filter.
Section III presents the different frames and a brief overview
on the classical washout filter priciple and algorithm. Section
IV presents the coordinate transformation of the orientation
between the body-fixed simulator reference frame and the
inertial reference frame. In Section V, we present the fuzzy
logic washout filter design and the optmization algorithm.
Section VI presents the Microsoft Flight Simulator software
which is used to perform the simulation. Finally, we present
the implementation details and some results about the fuzzy
logic based motion cueing algorithm for flight simulation.
II.
BACKGROUND ON M OTION C UEING A LGORITHMS
Many related researches on washout filter have been presented in the last three decades. Classical washout filter is
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the first scheme that has been proposed, which is composed
of linear low-pass and high-pass filters. Its advantages are
simplicity and easy to adjust. The fixed scheme and parameters
of the classical washout filter cause inflexibility of the scheme
and make the resulting simulator fail to suit all circumstances.
In [8] [9], the authors proposed coordinated adaptive filters,
used to coordinate translational and rotational motion.
Nahon and Reid [10] suggested an adaptive washout algorithm with the same scheme as that for classical washout
filter and with self-tuning of the filter parameters. Sivan et
al. [11] proposed an optimal washout algorithm which takes
into account the vestibular system models. This algorithm uses
techniques of optimal control and employs a cost function that
depends on both sensation error and platform motion. The optimal algorithm designs an optimal structure and a set of optimal
parameters subject to the assumptions of human vestibular
models and platform limitation by solving the Riccati equation
[11]. Telban et al. [12] formulated a linear optimal control
problem similar to [11] and solved the Riccati equation in real
time so that a scalar coefficient that increases control action
can be tuned online. The magnitude of the scalar coefficient
depends upon the platform motion.
With large platform motion, the large coefficient increases
and results in faster control action. Nehaoua et al. [13] applied classical, adaptive, and optimal algorithms and compared
performances of these algorithms in their driving simulator.
Authors in [14] propose the use of a linear optimal controller
synthesized with a quadratic performance index, which has
been applied to a planar model of the Vertical Motion Simulator. Idan and Nahon [15] proposed the use of a robust controller
design based on the model of the motion-base dynamics and
control in order to compensate for changes or uncertainties
in the motion-base dynamics, particularly electrically driven
ones with limited bandwidth and power. In [16], a variation
of the optimal algorithm was formulated, which incorporate
the models of the human vestibular sensation system, i.e.,
the semicircular canals and otoliths. No matter what kind of
platform is used as the simulator, the limited workspace is
an important issue in designing the motion cueing algorithm.
Several works that increase the efficiency of the platform
workspace have been presented.
Huang and Fu [17] proposed a senseless manoeuvre that
moves the operator with the acceleration under the threshold
value of human perception to conserve the workspace. Liao et
al. [18] combined the classical washout filter with an adjustable
scaling filter, a yawing washout filter, a dead zone washout
filter, and an adaptive washout filter in order to complete
the motion planning of the simulator in restricted workspace.
A common method to this problem is the fuzzy controlbased washout filter proposed by [19] [20], which propose
to tune the washout filter parameters based on fuzzy logic in
real-time. The cut-off frequencies of the filters are adjusted
according to the workspace margins and driving conditions.
That algorithm combined characters of motion perception and
actual movement of a train to design the fuzzy control rules.
However, no optimization process is included in these works.
In [21], an optimal washout filter is developed by applying the
algorithm of Sivan et al. [11] to the human vestibular system.
In this later, the fuzzy controller is used to compensate the
filtred signals.
Copyright (c) IARIA, 2013.
ISBN: 978-1-61208-296-7
Linear displacement
a
f
+
f ,f ,f a , a , a x
y
z
x
gg
y
1
s2
HPF
z
LPF
Tilt coordination
Tilt rate limiter
T
Tx ,Ty ,Tz
HPF
1
s
+
+
Angular displacement
Fig. 1: Classical washout filter flowchart.
The aim of this work is minimize the sensation error
produced from the comparison of the human vestibular signals about actual vehicle and simulator. The optimal motion
cueing algorithm is then featured by systematic integration of
linear filters that are determined through an off-line design
process. For the drawback with fixed parameters of the optimal
algorithm, the fuzzy control rules are designed to reduce the
sensation errors. In order to further eliminate the sensation
errors, we propose a new motion cueing algorithm to be
used in a research flight simulator using the Particle Swarm
Optimization algorithm.
III.
C LASSICAL WASHOUT FILTER
A vestibular system, consisting of semicircular canals and
otoliths, has a great role in sensing physical motion cues in a
motion simulator. It is known that the otoliths are able to detect
the high-frequency components of the linearly accelerated
motion via a specific force, while the semicircular canals can
sense the high-frequency components of the angular motion.
Due to the structural limit of the Stewart-Gough platform [22]
[8], a motion base has relatively small workspace compared
with the operating range of a real aircraft. Because of this
limited motion envelope of typical motion bases, filtering is
required between the aircraft motion computed from aircraft
dynamics and the simulated motion commanded to the motion
base. The filter used for this purpose is called a ’washout filter’.
Fig. 1 shows the classical washout algorithm which consists
of a High-Pass Filter (HPF) and a Low-Pass Filter (LPF) [23].
The linear acceleration (i.e., specific force) and the angular
velocity from the vehicle dynamics are sent to the washout
algorithm. The details on how the linear and angular displacements are computed in the washout filter are discussed below.
The specific force f x , fy , and fz are computed by subtracting
g from the given linear acceleration a. The high-frequency
components of the specific force f x are then obtained by the
following 2 nd -order HPF:
fHx
s2
= 2
2
fx
s + 2ζHx ωHx s + ωHx
(1)
where ζ and ω represent the damping ratio and the cutoff
frequency of the HPF, and the subscript H denotes the highfrequency. Note that f Hy and fHz in the y and z axes are
similarly obtained. This HPF filters out low-frequency motion
signals, which tend to lead to large linear displacements that
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has an orientation for X CG , YCG , and ZCG that is parallel to
reference frames F r S and F rA .
C. Simulator Frame
Aircraft
Simulator
The simulator reference frame F r S has its origin at the
centroid of the simulator payload platform, i.e., the centroid
of the upper bearing attachment points. The origin is fixed with
respect to the simulator payload platform. X S points forward
and ZS points downward with respect to the simulator cockpit,
and YS points toward the pilot’s right hand side. The x − y
plane is parallel to the floor of the cockpit.
D. Aircraft Frame
Fig. 2: Frames definition.
cause the motion base to reach its motion limits. The highfrequency motion signals are realized by linear motion of
the platform of the motion base by double integration of
the filtered signals. Low-frequency components of the specific
force in the x axis can be obtained by the following 2nd-order
LPF:
2
fLx
ωLx
= 2
(2)
2
fx
s + 2ζLx ωLx s + ωLx
where the subscript L denotes the low-frequency.
Note that fly is similarly obtained. This low frequency
portion is realized by tilting the platform (i.e., angular motion
about the x− and y− axes), since a human perceives as if
he experienced a specific force when his body tilts. This is
referred to as tilt coordination. Usually, a pitch angle is set
to θytilt = −fLx /g and a roll angle to θ xtilt = −fLy /g for
relatively small angles. The rate of tilting is limited to l − 5 ◦ /s
to prevent the semicircular canal from perceiving this signal
for tilt coordination as the angular motion cue [10]. In the
meantime, the high-frequency components of angular velocity
about the x− axis are obtained by the following first-order
HPF :
θ̇Hx
s
=
(3)
s
+
ω
θ̇x
Hθx
Note that θ̇Hx and θ̇Hx about the y and z axes are similarly
obtained. The high-frequency components are integrated to
give the angular displacement (θ Hx , θHy , θHz ) of the platform.
The orientation angle (θ x , θy ) about the x and y axes are then
obtained by adding the tilt angles (θ xtilt , θytilt ) to the angles
(θHx , θHy ) due to angular motion, while θ z is given as θz since
no tilt coordination is performed about this axis. It should be
noted that the cross feed channel in Fig. 1 includes a ratelimiting element that keeps the tilt rates below 3 ◦ /s.
A. Reference Frames
A series of reference frames are used in the definition of the
motion cueing algorithms. These reference frames are adopted
from [24], defined below and shown in Fig. 2.
The aircraft reference frame F r A has its origin at the same
relative cockpit location as the simulator reference frame F r S .
F rA has the same orientation for X A , YA , and ZA with respect
to the cockpit as the simulator frame F r S .
E. Inertial Frame
The inertial reference frame F r I is earth-fixed with Z I
aligned with the gravity vector g. Its origin is located at the
center of the fixed platform motion base. X I points forward
and YI points to the right hand side with respect to the
simulator pilot.
F. Reference Frame Locations
In Fig. 2, four vectors are illustrated, which define the
relative location of the reference frames. R I defines the
location of F rS with respect to F rI . RS defines the location of
F rP S with respect to F rS . Similarly, RA defines the location
of F rP A with respect to F rA , where RA = RS . RCG defines
the location of F rA with respect to F rCG .
IV.
C OORDINATE T RANSFORMATIONS
The orientation between the body-fixed simulator reference
frame F rS and the inertial reference frame F r I can be specified by three Euler angles: β = [ϕ, θ, ψ] that define a sequence
of rotations that carry F r S into F rI . A vector V expressed
in the two frames can be related by the transformation matrix
LSI (F rS to F rI ), V I = LSI V S with:
⎡
⎤
cθcψ sφsθcψ − cφsψ cφsθcψ + sφsψ
⎢
⎥
LSI = ⎣ cθsψ sφsθsψ + cφcψ cφsθsψ − sφcψ ⎦ (4)
−sθ
sφcθ
cφcθ
The angular velocity of F r S with respect to F rI can be related
to the Euler angle rates β̇ by the following expression. Let ω ss
represent the components of this angular velocity in frame
F rS , then β̇ = Ts ωss , where :
⎡
⎤
1 sinφtanθ cosφtanθ
⎢
cosφ
−sinφ ⎥
Ts = ⎣ 0
(5)
⎦
0 sinφ/cosθ cosφ/cosθ
B. Aircraft Center of Gravity
The aircraft center of gravity reference frame F r CG has
its origin at the center of gravity of the aircraft. Frame F r C G
Copyright (c) IARIA, 2013.
ISBN: 978-1-61208-296-7
The specific force is defined as:
S
= aspS − g s
fpS
(6)
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TABLE I.
where aspS is the linear acceleration and g s is the gravitational
acceleration. It is noted that human sense of motion is more
sensitive to high-frequency components of the motion than
low-frequency ones.
R ULE BASED SYSTEM FOR THE HPF CUTT- OFF
FREQUENCIES SELECTION .
Displacement limit (DL)
or angle limit (AL)
VF
VF
F
F
M
M
N
N
VN
VN
HPF cutt-off frequencies
VS
S
M
B
VB
A. Nonlinear Input Scaling
Limiting and scaling are applied to both aircraft translational input signals a ss and rotational input signals ω ss . Limiting
and scaling modify the amplitude of the input uniformly across
all frequencies. Limiting is a nonlinear process that clips the
signal so that it is limited to be less than a given magnitude.
Limiting and scaling can be used to reduce the motion response
of a flight simulator. A third-order polynomial scaling has been
implemented in the simulator motion cueing algorithms.
When the magnitude of the input to the simulator motion
system is small, the gain is desired to be relatively high, or the
output will be below the pilot’s perception threshold. When the
magnitude of input is high, the gain is desired to be relatively
low or the simulator may attempt to go beyond the hardware
limits. Let us define the input as x and the output as y. Now,
define xmax as the expected maximum input and y max as the
maximum output, and s 0 and s1 as the slopes at x = 0 and
x = xmax , respectively. Four desired characteristics for the
nonlinear scaling are expressed as:
⎧
x=0⇒y=0
⎪
⎪
⎪
⎨ x = xmax ⇒ y = ymax
(7)
ẏ|x=0 = s0
⎪
⎪
⎪
⎩
ẏ|x=xmax = s1
A third-order polynomial is then employed to provide functions with all the desired characteristics. This polynomial will
be of the form:
y = c0 + c1 x + c2 x2 + c3 x3
where :
⎧
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎩
V.
c0 = 0
c1 = s0
c2 = x−2
max (3ymax − 2s0 xmax − s1 xmax )
c3 = x−3
max (s0 ymax − 2ymax + s1 xmax )
(8)
(9)
P ROPOSED WASHOUT F ILTER D ESIGN
Washout filters have parameters that can be adjusted to
alter the motion responses of a simulator. Since the parameters
of most classical washout filters remain fixed during their
operation, they cannot cope with various flight conditions
efficiently. On-line tuning of the washout filters can enable the
simulator to offer better simulator motions within the limited
motion range. As part of the design of the experiment, the
underlying factor for the entire project was the sensed specific
force that results from the aircraft motion. This section focuses
on design of a fuzzy model, which shall generate the adequate
parameters for the eight filters.
TABLE II.
R ULE BASED SYSTEM FOR THE LPF CUTT- OFF
FREQUENCIES SELECTION .
Angle limit (AL)
Low-frequency
specific force (LFSF)
VF
F
M
N
VS
VBN
S
MN
M
B
VB
VN
VBF
BN
BM
BF
MM
MF
SN
SM
VBF
BN
BM
BF
SF
MM
MF
SN
SM
VSN
BN
BM
BF
VBM
VBF
the input-output relations are not clearly identified. To this
end, the fuzzy logic is used which takes the Displacement
Limit (DL), the Angle Limit (AL) and the Low-Frequency
Specific Force (LFSF) as inputs and then provides the proper
parameters of the six HPFs and the two LPFs as outputs. Fig.
3 illustrates the flowchart showing this fuzzy logic system.
Triangular functions are used for each variable. There were 5
linguistic terms for each input variable. These terms are:
•
Displacement limit (DL) & angle limit (AL): VF (Very
Far), F(Far), M(Medium), N(Near) and (VN) Very
Near.
•
Low-frequency specific force (LFSF): VS (Very
Small), S (Small), M (Medium), B (Big) and VB (Very
Big).
2) Rule-based system: Once the proper inputs were created,
the next step is to create the output’s characteristic behavior.
The output variables was classified as :
•
HPF cutt-off frequencies: VS (Very Small), S (Small),
M (Medium), B (Big) and VB (Very Big).
•
LPF cutt-off frequencies: VSF (Very Small with Far
Limit), VSM (Very Small with Medium Limit), VSN
(Very Small with Near Limit), SF (Small with Far
Limit), SM (Small with Medium Limit), SN (Small
with Near Limit), MF (Medium with Far Limit), MM
(Medium with Medium Limit), MN (Medium with
Near Limit), BF (Big with Far Limit), BM (Big with
Medium Limit), BN (Big with Near Limit), VBF (Very
Big with Far Limit), VBM (Very Big with Medium
Limit), VBN (Very Big with Near Limit).
A. Fuzzy Logic Washout Filter Design
The next step is to create a rule base that would govern the
operation of the fuzzy system. The proper conditions must be
created to implement a system that will allow for perceptible
specific force reproduction while taking into account the
motion limits of restitution platform. The fuzzy rules are shown
in Tables I and II.
1) Fuzzification: In this research, fuzzy logic [25] is suggested to tune the filter parameters, since it is known that
the fuzzy logic is effectively applicable to a system in which
In order to select the optimal fuzzy membership parameters, we use the Particle Swarm Optimization algorithm (PSO).
Some of the attractive features of the PSO include the ease of
Copyright (c) IARIA, 2013.
ISBN: 978-1-61208-296-7
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SENSORCOMM 2013 : The Seventh International Conference on Sensor Technologies and Applications
Linear displacement
a
f
+
f ,f ,f a , a , a x
y
z
x
gg
T
T ,T ,T x
y
y
1
s2
HPF
z
implementations use a setting with c 1 = c2 = 2 . To implement
the PSO algorithm to solve our problem, the following steps
were taken:
•
Step 1: The initial population and initial velocity for
each particle should be generated randomly.
Tilt coordination
•
Step 2: The objective function is to be evaluated for
each individual.
Tilt rate limiter
•
Step 3: The individual that has the minimum objective
function should be selected as the global position.
•
Step 4: The i t h individual is selected.
•
Step 5: The best local position (P best) is selected for
the it h individual.
•
Step 6: The modified velocity for the i t h individual
needs to be calculated based on the local and global
positions and (10)).
•
Step 7: The modified position for the i t h individual
should be calculated based on (10) and then checked
with its limit.
•
Step 8: If all individuals are selected, go to the next
step, otherwise i = i + 1 and go to step 4.
•
Step 9: If the current iteration number reaches the
predetermined maximum iteration number, the search
procedure is stopped, otherwise go to step 2.
LPF
HPF
z
1
s
+
+
Angular displacement
Fig. 3: Fuzzy/washout filter architecture.
implementation and the fact that no gradient information is
required. In addition, PSO has the same effectiveness (finding
the true global optimal solution) as the Genetic Algorithms
(GA) but with significantly better computational efficiency
(less function evaluations).
B. Particle Swarm Optimization Algorithm (PSO)
PSO is a stochastic optimization algorithm [26] [27] [28].
The main idea of the PSO is the mathematical modeling and
simulation of the food searching activities of a flock of birds. In
the multidimensional space, each particle is moved toward the
optimal point by changing its position according to a velocity.
The velocity of a particle is calculated by three components:
inertia, cognitive, and social. The inertial component simulates
the inertial performance of the bird to fly in the previous
direction. The cognitive component models the memory of the
bird about its previous best position. The social component
models the memory of the bird about the best position among
the particles. The particles move around the multidimensional
search space until they find the optimal solution. Based on the
above discussion, the mathematical model for PSO is given as
follows:
⎧ t+1
= ω.Vit+1 + c1 .rand1 (.) . (P besti − Xit )
⎪
⎨ Vi
+c2 .rand2 (.) . (Gbest − Xit )
(10)
⎪
t+1
t+1
⎩ X
t
=
X
+
V
,
i
=
1,
2,
3,
....,
N
swarm
i
i
i
where, i is the index of each particle, t is the current iteration
number, rand1 and rand2 are random numbers between 0 and
1. P besti is the best previous experience of the i th particle
that is recorded. Gbest is the best particle among the entire
population. N Swarm is the number of the swarms. Constants c 1
and c2 are the weighting factors of the stochastic acceleration
terms, which pull each particle towards the P best i and Gbest.
ω is the inertia weight. As indicated in (10), there are three
tuning parameters; ω, c1, and c 2 that each of them has a
great impact on the algorithm performance. The inertia weight
ω controls the exploration properties of the algorithm. The
learning factors c 1 and c2 determine the impact of the personal
best P besti and the global best Gbest, respectively.
If c1 > c2 , the particle has the tendency to converge to
the best position found by itself P best i rather than the best
position found by the population Gbest, and vice versa. Most
Copyright (c) IARIA, 2013.
ISBN: 978-1-61208-296-7
The objective function is to minimize the sensation error
between the spectific force estimated on the aircraft and the
one restituted on the simulator based on the proposed fuzzy
washout algorithm with respect to the following constraints:
•
The negative acceleration is limited to 0.17m/s 2 after
high pass filtering.
•
The washout rate is limited to 0.048m/s.
•
The output of the low pass filter after tilt coordination
is limited to 5◦ /s.
The last Gbest is the solution of the problem.
VI.
E XPERIMENTS AND I MPLEMENTATION D ETAILS
A. Virtual aircraft model and instrumentation
The use of non-flight-certified Commercial-Off-The-Shelf
(COTS) solutions has proved its fidelity and coordination
characters as a flight training device [29]. MSFS is a flight
simulator program, marketed and often seen as a video game.
However, it is less a game than an immersive virtual environment since it is very realistic, (see Fig. 4). Its first version
appeared in 1982, whereas, its most recent versions, Century
of Flight and Flight Simulator X appear respectively in 2003
and 2006 [30].
The long history, the consistent popularity and the open
nature of flight simulator structure have encouraged a very
large body of freeware and payware add-on packages to be
developed. These add-ons, widely available over the internet,
are very helpful because they, not only, permit to change
internal aspects of the simulator (airplanes, scenery...), but also
to interface it with external software and hardware such as
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8
Estimated specific foce on the aircraft
Restitued specific force on the simulator using the proposed algorithm
6
Specific force (m2/s)
4
2
0
−2
−4
−6
−8
0
0.2
0.4
0.6
0.8
1
1.2
Time (Ts =20ms)
1.4
1.6
1.8
2
4
x 10
Fig. 6: Real specific force vs restituted specific force using the
proposed algorithm.
Fig. 4: Virtual Mi24 helicopter under the MSFS environment.
8
6
Specific force (m/s2)
4
2
0
−2
−4
−6
−8
0
Estimated specific force on the aircraft
Restituted specific force using the proposed algorithm
Restituted specific force using the classical washout algorithm
0.2
0.4
0.6
0.8
1
1.2
Time (Ts=20ms)
1.4
1.6
1.8
2
4
x 10
Fig. 7: Restituted specific force using the classical washout
algorithm vs restituted specific force using the proposed algorithm.
8
6
Specific force (m2/s)
4
2
0
−2
−4
Fig. 5: The MOOG 6DOF 2000E 170 E131 motion platform.
−6
−8
0
homebuilt cockpits. Among the most famous add-ons, we cite
a dynamic link library add-on made by Pete Dowson [31],
called Flight Simulator Universal Inter-Process Communication (FSUIPC). This module is designed to allow external (i.e.,
separate) programs to communicate with and control MSFS in
real time. In other words, they permit to read from and write
in MSFS while it’s running and place it in a 64 Kb buffer. So
to achieve a variable we have only to know its offset (address)
in this buffer. However, before writing or reading data from
FSUIPC, we have to scale it to the desirable unit.
Our algorithm was developed to achieve the desired motion
cues at an update rate of 60 Hz. Since the computer image
generator, which provides the out-the window visual imagery
to the simulator pilot, also runs at 60 Hz, the motion cues
would be synchronous with the visual cues. The HPF and PHF
filters are implemented using the tustin operator which consists
−1
to replace the Laplace operator by s = T2s 1−z
1+z −1 , where z is
the discret operator, and T s is the sample time. The proposed
algorithm was successfully implemented on the MOOG 6DOF
2000E 170 E131 motion platform (see Fig.5).
Copyright (c) IARIA, 2013.
ISBN: 978-1-61208-296-7
Estimated specific force on the aircraft
Restitued specific force using the proposed algorithm
Restitued specific force using the (Song et al, 2003) algorithm
0.2
0.4
0.6
0.8
1
1.2
Time (Ts= 20ms)
1.4
1.6
1.8
2
4
x 10
Fig. 8: Restituted specific force using the [19] algorithm vs
restituted specific force using the proposed algorithm.
B. Results and Discussion
Figs. 6 to 9 show an example of simulated manoeuvres
for a MI24 helicopter. Fig. 6 presents comparison between the
specific force estimated on the aircraft and that restituted on the
simulator based on the proposed algorithm. The specific force
is estimated using the vestibular model as explained above.
Fig. 7 shows a comparison between the produced specific
force using the classical washout algorithm and that produced
using the proposed algorithm. We remark from this figure that
the specific force signal produced using our algorithm is highly
important and closer to the real signal in comparison with the
result obtained using the classical algorithm.
While comparing the obtained results in Fig. 8, we can
see that the proposed algorithm allows to get a specific force
closer to the one estimated on the aircraft in comparison
with the results obtained using the approach proposed in [19].
241
SENSORCOMM 2013 : The Seventh International Conference on Sensor Technologies and Applications
0.25
0.2
0.15
Position (m)
0.1
0.05
0
−0.05
−0.1
−0.15
Position command using the proposed algorithm
Position command using the classical washout algorithm
−0.2
−0.25
0
0.2
0.4
0.6
0.8
1
1.2
Time (Ts = 20ms)
1.4
1.6
1.8
2
4
x 10
Fig. 9: Comparison of the position cammands using the classical
washout algorithm vs restituted the position cammands using the
proposed algorithm.
Therefore, it allows to the pilot to get a realistic sensation on
the simulator. In addition, the magnitude of position commands
sent to the motion platform are always within its displacement
limits, as seen from Fig. 9. In this way, we can preserve the
motion platform from damage and maintain its functionality.
VII.
C ONCLUSION
This paper reported a successful achievement of a new
fuzzy washout algorithm implementation for inertial stimuli
restitution in Flight Simulation. The integration of the PSO
algorithm has allowed to select the optimal parameters of
the fuzzy models. The related built-in software have been
integrated successfully with COTS software that provides
the aircraft dynamics in a virtual environment. The different
simulations proved the effectiveness of the proposed algorithm.
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