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Chinese Science Bulletin
2007 SCIENCE IN CHINA PRESS
Springer
www.scichina.com www.springerlink.com Chinese Science Bulletin |
January 2007 | vol. 52 | no. 1 | 1-?
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Advances in Unmanned Aerial Vehicles Technologies Agus Budiyono1
1 Smart Robot Center, Department of Aerospace Information
Engineering, Konkuk University, Seoul, Korea. Previously with
Center for Unmanned System Studies, Institut Teknologi Bandung,
Indonesia
The utilization of unmanned vehicles has become increasingly
more popular today and been successfully demonstrated for various
civil and military applications. The unmanned aerial ve-hicles
(UAVs) have shown applications in different areas including crop
yield prediction, land use surveys in rural and urban regions,
traffic surveillance and weather research. The unmanned small scale
helicopters are particularly suitable for demanding problems which
requires accurate low-speed maneuver and hovering capabilities such
as detailed area mapping. Generally a certain level of autonomous
flight capability is required for the vehicle to achieve its
mission. The basic autonomy level is to maintain its stability
following a desired path under embedded guidance, na-vigation and
control algorithm. The UAV technology trends indicate that to cope
with the more stringent operation requirements, the UAVs should
rely less and less on the skill of the ground pilot and
progressively more on the autonomous capabilities dictated by a
reliable onboard computer system. To systematically develop and
enhance flight autonomy, a rotary wing UAV (RUAV) or model
helicopter has been proposed and used as a flying test-bed at
various major research centers. The ability of the helicopter to
operate in the hovering mode makes it an ideal platform for a
step-by-step autonomous capability development. On the other hand,
a small heli-copter exhibits not only increased sensitivity to
control inputs and disturbances, but also a much richer dynamics
compared to conventional unmanned aerial vehicles (UAVs). The paper
surveys recent advances in modeling, control and navigation of
autonomous unmanned aerial vehicles. Without loss of generality, an
autonomous small scale helicopter research program is taken as a
case study. Approaches to modeling and control for such a vehicle
are presented and discussed. Future directions in the advancement
of UAV technologies are identified and key barriers
hig-hlighted.
Unmanned aerial vehicle, model identification, control,
navigation, trajectory generation
I. Introduction
A widely used definition of UAV is an aerial vehicle (in-cluding
fixed-wing, rotary-wing or airship platform) which can sustain its
flight along a prescribed path without an on-board pilot. The UAV
technology has proven applica-tions in many areas such as
environmental monitoring and protection, meteorological
surveillance and weather re-search, agriculture, mineral
exploration and exploitation, aerial target system, airborne
surveillance for military land operations, and reconnaissance
missions. The unmanned small scale helicopters enjoy no requirement
for runway and are particularly suitable for demanding problems
such
as traffic or volcanic areas surveillance, detailed area
map-ping, video footage recordings and crop dusting or spraying.
Table 1 lists applications of contemporary UAVs in differ-ent
areas.
A recent progress in the supporting technologies has enabled the
development of semi to fully autonomous UAV. This includes the
availability of compact, lightweight, af-fordable motion detecting
sensors essential to the flight control system and compact
lightweight low-cost compu-ting power for autonomous flight
control. A wide varieties of autonomous UAV platforms have been
developed and flown ranging from fixed-wing to rotary wing
platforms,
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several minutes to hours/day in endurance and 100 grams to 800
kg in weight. From all types of UAVs, the small scale
rotorcraft-based vehicle has been considered one that exhibits the
most complex dynamic properties. From the perspective of control
area, the RUAV demonstrates liter-ally all challenges that have
attracted enormous interests from industry and academia alike. The
challenging prob-lems include higher bandwidth, hybrid modes,
non-holonomic, under-actuation, multi input multi output (MIMO),
and non-minimum phase. The paper discusses the advances in UAV
technologies from the perspective of modeling and control of
rotorcraft-based aerial vehicles.
II. Background: Science and Technology
A. Survey of UAVs
The viability of UAV as a multipurpose research vehicle has
driven great interest since recent decades. The basic technology
building blocks responsible for the current ad-vances include
airframes, propulsion systems, payloads, safety or protection
systems, launch and recovery, data processor, ground control
station, navigation and guidance, and autonomous flight
controllers. The following brief survey is focused on the area of
navigation, guidance and control of UAVs. Various control design
for UAVs has been proposed ranging from linear to nonlinear
synthesis, time invariant to parameter varying, and conventional
PID to intelligent control approaches. The developed controllers
have been implemented for different aerial platforms: air-ship
(blimp), fixed-wing UAV, small scale helicopter, quad-rotors, and
MAV.
The research on autonomous airship is reported in (Azin-heira,
2008) where the authors proposed a nonlinear control approach for
the path-tracking of an autonomous underac-tuated airship. A
backstepping controller is designed from the airship nonlinear
dynamic model including wind dis-turbances, and further enhanced to
consider actuators satu-ration. The hover control using the same
approach for such a vehicle is presented in (Azinheira and
Moutinho, 2008). A number of investigations have been conducted for
con-trol and stabilization of quadrotor UAV. In (Raffo, 2008), a
robust control strategy to solve the path tracking problem for such
a vehicle was designed in consideration of external disturbances
like aerodynamic moments. A state parameter control based on Euler
angles and open loop positions state observer was proposed by
Mokhtari and Benallegue (2004). The work was continued in
(Mokhtari, 2005) in which a mixed robust feedback linearization
with linear GH con-troller was applied. An actuator saturation and
constrain on
state space output are introduced to analyze the worst case of
control law design. A different approach was proposed in (Madani,
2007) where a backstepping control running pa-rallel with a sliding
mode observer for a quadrotor vehicle. The sliding mode observer
works as an observer of the qu-adrotor velocities and estimator of
the external disturbances such as wind and parameter uncertainties.
In (Escareno et.al., 2008), the authors proposed a three-rotor
configura-tion which incorporates certain structural advantages in
order to improve the attitude stabilization. The control strategy
is robust with respect to dynamic couplings and to the adverse
torques produced by the gyroscopic-effect and propellers drag.
The research on autonomous flight using model helicopters as a
test-bed has been performed by a large number of teams all over the
world. The MIT UAV team successfully developed an autonomous
aerobatic helicopter in (Gravilets, 2003). The development relied
on the modeling framework of the miniature helicopter dynamics. A
methodology for designing model-based control strategies for
autonomous aerobatic maneuver was proposed and validated
experi-mentally. Referring to previous work by Mettler (Mettler
et.al., 2002) at Carnegie Mellon Robotics Institute, the ba-sis for
a simplified modeling framework was considered to stem from the
fact that the dynamics of small-scale heli-copters is dominated by
the rotor response. The real-time control system was developed
using a Hard-ware-In-the-Loop (HIL) simulation system which allows
high fidelity representation of the signals time-dependence in real
time navigation scheme
At Georgia Tech, the Open Control Platform (OCP)a new
object-oriented real time operating software architec-ture has been
used onboard the GTMAX UAV helicopter to compensate for the
simulated in-flight failure of a low level flight control system.
The viability of designing in-expensive architecture, along with a
relatively simple pro-cessor, will pave the way for the extremely
low-cost flight control and guidance systems. Another novel
contribution was the use of Pseudo Control Hedging (PCH) in the
adap-tive flight control scheme for improving tracking perfor-mance
of a small helicopter. Using this architecture, a con-solidated
reference command that includes position, veloc-ity, attitude and
angular rate may be provided to the control system. At UC Berkeley,
the research on an autonomous helicopter has been conducted as
reported in Koo and Sastry(1998), Koo et.al.(2001) and Kim
et.al.(2003). A helicopter ma-thematical model is first established
with the lump-parameter approach. The control models of the
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RUAVs are then derived by the application of a time-domain
parametric identification method to the flight data of target
RUAVs. The classical control theory and modern linear robust
control theory are applied to the iden-tified model. The proposed
controller are validated in a nonlinear simulation environment and
tested in a series of test flights (Shim, 2000).
The discussion on this paper is centered on model-based control
design and navigation system technology in the framework of recent
advances in UAVs elaborated in the following order. In the section
below (II), the system and technology background of UAVs are
presented including a brief survey of contemporary UAVs, summary of
lessons from the research on RUAV modeling and controls, and
identification of trends in UAV technology. Section III presents
the review of modeling of RUAV using combined first principle and
time-domain identification. Nonlinear dynamic modeling is presented
based on first principle ap-proach using X-cell 60 small scale
helicopter as a test bed. A method for linearization procedure is
elaborated to pro-vide an analytical model for the implementation
of linear control. Section IV is focused on discussion on
simulation, control and guidance for UAV. Some approaches for
con-trol synthesis are demonstrated for illustration. The last
section (V) identifies emerging technologies in the area of aerial
robotic. Concluding remarks on the challenges and future directions
are made in final section (VI).
B. Lessons learned from CentrUMS-ITB UAV Program The research on
RUAV at the Center for Unmanned Sys-tems Studies (CentrUMS)-ITB was
carried out by using a fully instrumented X-cell 60 SE model
helicopter similar to one used by MIT team as shown in Fig. 1. The
mini heli-copter is characterized by a hinge-less rotor with a
diameter of 0.775 m and mass of 8 kg. The X-Cell blades both for
main and tail rotors use symmetric airfoils. The vehicle has been
used by a number of research centers as published in a number of
literatures (Gravilets,2003; Bogdanov,2003; Bogdanov,2004).
Therefore comparison and validation can be achieved from the
available published results. Using the test bed, studies on
modeling and control of RUAV were conducted. A great deal of effort
was focused on developing nonlinear model based on first principle
ap-proach. The nonlinear model was implemented in Simu-link/Matlab
with parameters are measured independently or obtained from
literatures. Flight tests were conducted to validate the model.
Various control synthesis were studied for performance comparison.
The important lesson learnt from the experience is that a small
scale helicopter is a in-tricate and unstable platform; to utilize
it for a useful re-
search test-bed there is a compelling need for development of
mathematical model that capture the key dynamics of the vehicle
with reasonable level of complexity for the purpose of control
design. A number of key results are pre-sented in Section IV.
Figure 1: Instrumented X-Cell 60 SE- CentrUMS-ITB
C. Trends in UAV Research
More stringent mission requirements have driven the UAVs to have
a higher level of autonomy dictated by a reliable onboard computer
system. The metric for UAV level of autonomy is given in Table 2
(Sholes, 2006). Some key areas in current state-of-the-art aerial
robotic technologies are responsible for enabling AUVs to achieve
its required level of autonomy. Current status of UAV research
activi-ties in these areas can be summarized as the following:
1. State estimation algorithm. To achieve better perfor-mance,
multiple sensors are typically fused together using EKF in a sensor
fusion algorithm. Propagated IMU-data can be fused with discrete
updates from GPS and altimeter. Several design examples are
pro-vided in (Johnson and Kannan, 2002). Recent study include the
use of nonlinear adaptive observers for es-timating speed of UAV
from IMU measurements only without the aid of GPS (Khadidja,
2007).
2. Simultaneous Localization and Mapping. An un-manned aerial
vehicle (UAV) is tasked to explore an unknown environment and to
map the features it finds, but must do so without the use of
infrastructure-based localization systems such as GPS, or any a
priori ter-
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rain data. A statistical estimation technique allows for the
simultaneous estimation of the location of the UAV as well as the
location of the features it sees.
3. Vision for guidance. Computer vision is used as a feedback
sensor in a control loop for an autonomous flight system. (Amidi
et.al, 1998). More recent exam-ple is precision targeting without
using secondary actuation or add-on gimbal system.
4. The use of GPS as attitude sensor. The need for re-duced
complexity avionics system has driven the re-search on the use of
single GPS for obtaining attitude estimate (Kornfeld, 1998).
5. Integrated modeling. Linear model is obtained by us-ing
combination of first principle results and time or frequency domain
identification scheme.
6. Trajectory generation using maneuver automaton. Ve-hicle
motion is described by library of motion primi-tives (Frazzoly
et.al, 2005). The trajectory between two positions and vehicle
states is found by searching the sequence of motion primitives
which will best sa-tisfy an objective function. One important
application of guidance system is collision avoidance between
vehicle at its tight and structured environment or be-tween
vehicles operating in formation or multi agent system.
7. Safety verification. Safety verification or reachability
analysis aims to show that starting at some initial conditions, a
systems cannot evolve to some unsafe regions in the state space.
Unsafe region for UAV ap-plication can be defined in the context of
proximity to obstacles, fuel availability (endurance), un-flyable
zone and/or communication range. A new concept called barrier
certificate is being used for safety veri-fication of hybrid
systems.
III. Modeling of RUAV
The requirement for successful navigation and guidance task is
stabilization of vehicle platform. Viewed as a mul-ti-loop system,
guidance and navigation is represented by the outer-loop and
control and stabilization the inner loop. The design starts from
the most inner loop outward. In this context, to control small
scale helicopter as unstable plat-form with complex dynamics
require sufficiently accurate model. This section elaborates the
modeling technique and the corresponding model-based control
synthesis.
TABLE I
UAV LEVEL OF AUTONOMY
Level Level D escriptor Perception/Situational A w areness10
Fully A utonom ous Cognizant of all w ithin battlespace
9 Battleship sw arm cognizanceKnow s intent of self and others
(friendlyand threat) in a com plex/intenseenvironm ent; on board
tracking
8 Battleship single cognizanceProxim ity Inference - intent of
self andothers (friendly and threat);Reduced dependence on
off-board data
7 Battleship know ledge
Short track aw areness - H istory andpredictive battlespace data
in lim ited range,tim efram e, and num bers; Lim ited
inferencesupplem ented by offboard data
6 Real tim e m ultivehicle cooperationRanged aw areness - on
board sensing forlong range, supplem ented by off-boarddata
5 Real tim e m ultivehicle coordinationSensed aw areness - Local
sensors to detectexternal targets (friendly and threat) fusedw ith
off-board data
4 Fault/Event A daptive vehicle O ff-board A w areness -
friendly system scom m unicate data
3 Robust response to real tim efaults/event H ealth/status
history and m odels
2 Changeable m ission H ealth/status sensors
1 Execute preplanned m ission Preloaded m ission data; Flight
Control andN avigation Sensing
0 Rem otely Piloted Vehicle Flight Control (attitude, rates)
sensing; O nBoard Cam era
.
A. Methods of Modeling
The approach to helicopter modeling can be in general di-vided
into two distinct methods. The first approach is known as first
principle modeling based on direct physical understanding of forces
and moments balance of the ve-hicle. The challenge of this approach
is the complexity of the mathematical model involved along with the
need for rigorous validation. The method is primarily suitable for
one with a strong background in flight physics. The second method
based on system identification (Tischler and Cauffman, 1992;
Mettler et.al., 2002, Tischler and Remple, 2006) basically arises
from the difficulty of the former ap-proach. The frequency domain
identification starts with the estimation of frequency response
from flight data recorder from an instrumented flight-test vehicle.
The parameterized dynamic model can then be developed in the form
of a li-near state-space model using physical insight and
frequen-cy-response analysis. The identification can also be
con-ducted in time-domain.
In what follows, the author argues that, any modeling should
start from adequate basis in first-principle. In prac-tice, the
above two methods can be used in an integrated scheme for
developing an accurate small scale rotorcraft vehicle model for the
purpose of control design. The mod-eling based on neural networks
with appropriate structure and training method can be viewed as a
viable alternative.
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Meanwhile, a new modeling scheme based on Linear Pa-rameter
Varying (LPV) identification is attractive for RUAV
application.
B. Equation of Motion of RUAV The motion of a vehicle in
three-dimensional space can
be represented by the position of the center of mass and the
Euler angles for the vehicle rotation with respect to the inertial
frame of reference. The Euler-Newton equations are derived from the
law of conservation of linear and an-gular momentum. Assuming that
vehicle mass is m and inertial tensor I, the equations of motion
are given by:
I
I
dVm Fdt
dI Mdt
=
=
K K
KK K
(1)
where [ ]TF X Y Z=K is the vector of external forces act-ing on
the helicopter center of gravity and [ ]TM L M N=K is the vector of
external moments. For helicopter, the external forces and moments
consists of forces generated by the main rotor, tail rotor;
aerodynamics forces from fuselage, horizontal fin and vertical fin
and gravitational force. For computational convenience, the
Euler-Newton equations describing the rigid-body dynamics of the
helicopter is then represented with respect to body coordinate
system by us-ing the kinematic principles of moving coordinate
frame of reference as the following:
( )
( )
mV m V F
I I M
+ =+ =
K K KKK KK K
(2)
Here the vector [ ]TV u v w=K and [ ]Tp q r =K are the fuselage
velocities and angular rates in the body coordi-nate system,
respectively. For the helicopter moving in six degrees of freedom,
the above equations produce six differential equations describing
the vehicles transla-tional motion and angular motion about its
three refer-ence axes. From here, we can express the mathematical
expression for external forces and moments of the helicopter as a
function of the control inputs and the vehicle states.
( ) sinX m u rv qw mg = + + ( ) sin cosY m ru v pw mg = + ( )
cos cosZ m qu pv w mg = + ( )xx yy zzL I p I I qr= ( )yy zz xxM I q
I I pr= ( )zz xx yyN I r I I pq=
(4)
The forces and moments components consist of contri-bution from
main rotor, tail rotor, fuselage, horizontal fin and vertical
fin.
1) Main Rotor: The main rotor thrust equations are ex-pressed
as:
( ) ( )2 2MR MRMR MR TT R R C = (5) where the thrust coefficient
is given by
( ) 2MR MR MR MR 0MR MR 01 1 1 12 2 3 2T zC a = + + (6)
and the inflow ratio, advance ratio and normal airflow component
are respectively given by
( ) ( )
( ) ( )
iMR MR0MR 22
MR MR 0MR MR
2 2
MR MRMR MR
R 2
R R
T
w z
a a az
w C
u v w
= + +
(7)
Here , a and w are solidity ratio, lift curve slope and
coefficient of non-ideal wake contraction of the main rotor. The
above equations must be solved iteratively to obtain the thrust.
The main rotor torque can be approx-imated as a resultant of
induced torque due to generated thrust, and torque due to profile
drag on the blade.
( ) ( )2 2MR MR MRMR MR QQ R R R C = (8) where the torque
coefficient is given by
( )MR 0
2MR MR MR 0MR MR MR
1 718 3Q D z T
C C C = + + (9)
and 0D
C is the profile drag coefficient of the main rotor. The
representation of the main rotor tip path plane dy-
namics is given by
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( ) ( ) Lo1 a 1 ae 1 1 eMR MRMR MRs s
s sz
a u a wa a q AR R
= + + +
( ) Lat1 ae 1 1 e LatMR MRs
s sb vb b p B
R
= +
(10)
where alat
B and longA steady-state lateral and longitu-
dinal gains from the cyclic inputs to the main rotor flap
angles; lat and long are the lateral and longitudinal cyc-lic
control inputs; e is the effective rotor time constant for a rotor
with the stabilizer bar.
2) Tail Rotor: The tail rotor thrust can be computed by the
following equation:
rTR r TRvT mY mY v = + (11)
And the normal velocity component to the tail rotor is
TR a TR TRv v l r h p= + (12)The tail rotor torque is computed
using similar equations
for main rotor with tail rotor parameters substituted into the
main rotor parameter.
3) Fuselage: For hover and low speed forward flight, the rotor
downwash is deflected by the forward and side velocity. This
deflection creates a force opposing the movement. The fuselage
forces of the helicopter can be expressed as
fus fus a12 x
X S V u =
fus fus a12 y
Y S V v =
( )fus fus a iMR12 zZ S V w w = (13)
4) Horizontal tail: The horizontal tail generates lift and a
stabilizing pitching moment around the center of gravity. This will
also compensate the destabilizing effect of the main rotor flapping
due to vertical speed. The horizontal tail fin forces and moments
of the helicopter referenced to body coordinate system are
HF 0X = HF 0Y =
( )( )
HF HF HF a HF HF
2 2HF HF a HF
12
12
LZ S C u w w
Z S u w
= +
= +
(14)
5) Vertical tail: The vertical tail forces can be approx-imated
by the following expression
VF 0X = ( )( )
VF VF VF VF VF VF
2 2VF VF VF VF
12
12
LY S C V v v
Y S V v
= +
= +
(15)
C. First Principle Model
The detailed equations of motion as presented previously are the
basis for first principle modeling. It is a bottom-up physical
modeling. A study by Weilenmann (1994) was an attempt to use
first-principle approach to model the heli-copter dynamics. The
modeling however was limited only to hovering condition. Some
simplified version of helicop-ter model existed including the
Minimum-Complexity Helicopter Simulation Math Model (Heffley and
Mnich, 1988) spanning from the previous work by Heffley et.al.(1979
and 1986). In 2003, Gavrilets (Gavrilets, 2003) presented a
nonlinear model helicopter based on first prin-ciple approach used
for an aerobatic maneuver control. The work however does not
present workable procedures for developing linear model for the
purpose of control design. The step-by-step development of linear
model requires the calculation of a trim condition around which the
vehicle motion will be linearized. The trim conditions for the
heli-copter are chosen operating points within which we solve the
equilibrium condition ( , ) 0f x u =K K K by first setting the
states to the values which characterize the corresponding flight
condition. For the case of RUAV, the solution of trim condition is
achieved through an iterative process. The no-tion of stability
derivatives used in the modeling arises from Taylors series
expansion of external forces and mo-ments around an equilibrium
condition where only first order effects are retained. The external
forces and moments are thus expressed in terms of product of
derivatives and the rigid-body vehicle states and control inputs.
The linea-rized equations of motion can finally be expressed in the
form of state space readily usable for control synthesis. For more
detail explanation, the readers are referred to (Bu-diyono, 2007b).
As needed, the first principle model can also be refined by the
system identification technique as presented in the following
section.
D. Identification Modeling The first principle approach
typically requires the detail knowledge regarding the system
behavior. The use of sys-tem identification modeling either in time
or frequency domain on the other hand is more practical. The system
identification approach requires experimental input-output data
collected from the flight tests of the vehicle. Thus the flying
test-bed must be outfitted with adequate instruments
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to measure both state and control variables. To utilize
ex-perimental data to build a parameterized model however, a model
structure and decent initial conditions in the optimi-zation scheme
would be required to achieve convergence. The model structure and
its initial value in this case can be provided by prediction of
first principle calculation. In structured parameterization scheme,
Predication Error Minimization (PEM) method can be utilized to
estimate the parameters. With the method, the parameters of a model
are chosen so that the difference between predicted output of the
model and the measured output is minimized with the following
process.
Given the time domain description of a system:
( ) ( ) ( ) ( ) ( )y t G q u t H q e t= + (16) and by observing
the input (u) and output (y) data, the
error, e(t) can be computed as: 1( ) ( )[ ( ) ( ) ( )]e t H q y
t G q u t= (17)
PEM uses optimization to minimize the cost function, defined
by:
2
1( , ) ( )
N
Nt
V G H e t=
= (18) The result of combined first principle and identification
modeling is illustrated in Fig. 2. The figure shows the for-ward
velocity flight data (solid thick line) compared with the first
principle model (solid thin line) and identification model (dashed
line). The figure shows that the fitness ratio of the flight data
for first principle and identification model is 19.87% and 24.34%
respectively.
Figure 2: Comparison of first principle and ID result
TABLE II
STABILITY DERIVATIVES COMPARISON BETWEEN FIRST PRINCIPLE
PREDICTION AND IDENTIFICATION
First Principle Pre-
diction Identification
Yv -0.3471 -0.8652 Yr -16.5191 -16.286
Yb1s 10.1395 134.74 Lu -0.0106 -0.03 Lw 0.1098 0.0703 Lv -0.2486
-0.217 Lp -40.8739 1.3026 Lb1s 408.5485 320.53 Nw 1.0103 1.3669 Nv
2.5045 2.1817 Np 0.1406 -1.2065 Nr -0.9758 -0.695
Ba1s / e
0 0.0656
Further comparison between the first principle prediction and
identification result is given in Table II.
E. Linear Parameter Varying Identification All previous modeling
schemes boil down to the develop-ment of linear model associated
with a certain flight condi-tion as shown in Fig. 3. The design of
global nonlinear control is then predicated on the notion of gain
scheduling. The drawback of this approach is that control designs
based on linearized dynamics might become deteriorated when it is
applied beyond the vicinity of equilibrium. In contrast, LPV
control technique explicitly takes into account the change in
performance due to real-time parameter varia-tions. Therefore, this
control technique gives a promising potential in designing control
systems which is robust over the entire operating envelope.
Hover
Accelerate
Cruise
Deccelerate
Maneuvers
Ascend
Descend
Piourette
RUAVsFLIGHTCONDITIONS
Figure 3: RUAVs flight conditions
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0 50 100 150 200 250 300-5
0
5
10
15
20
25
time(seconds)
u(m
/s)
Estimated ResponsePlant Response
Figure 4: Result of LPV identification for forward speed
The LPV identification scheme employs recursive least square
technique implemented on the LPV system represented by dynamics of
helicopter during a transition. The airspeed as the scheduling of
parameter trajectory is not assumed to vary slowly. The exclusion
of slow para-meter change requirement allows for the application of
the algorithm for aggressive maneuvering capability without the
need of expensive computation. Fig. 4 shows the result of LPV
identification for varying forward speed. More detail account can
be found in (Bu-diyono, 2008b).
IV. Simulation, Control and Guidance
To date various control techniques have been designed for
rotorcraft vehicles ranging from classical
sin-gle-output-single-output PID controller (Shim, 2000) to
nonlinear (Koo, 1998; Boussios, 1998; Devasia, 1999; Buskey et.al.,
2001, Harbick, 2004) and from non-aggressive flight (Corke et.al.,
2000; Castillo et.al., 2005) to aggressive flight (Gavrilets
et.al., 2001). To cover a wide region in the flight envelope, a
gain schedule tech-nique is typically employed as in Shamma and
Athans (1991). A control using state-dependent Riccati equation was
proposed by Bogdanov and Wan (2003) and Bogdanov et.al.(2003). The
scheme was implemented on X-Cell heli-copter (Bogdanov et.al,
2004). A control synthesis based on behavioral approach was
suggested by Fagg et.al. (1993) and Buskey et.al. (2002,2003).
Fuzzy (Jang and Sun, 1995) and adaptive control have been also
synthesized for control of RUAV (Hovakimyan et.al. 2000; Johnson
and Kannan, 2002; Kannan and Johnson, 2002; Kim et.al., 2002; Kutay
et.al., 2002, Sanchez et.al., 2005). Bagnell and Schneider (2001)
proposed a control using reinforcement learning. A Lyapunov control
design was proposed by Mazenc et.al.
(2003). Overall, there exists a tendency in the area of RUAVs
that more research has been done in control design methodolo-gies
than in developing dynamics model. The author argues that modeling
is prerequisite of good control design. In order that a control
system can be successfully designed and implemented for a vehicle
(system), the dynamics cha-racteristics of the vehicle must be
well-understood. In line with this argument, Mettler (2003) viewed
that the tenden-cy to get around modeling efforts by searching for
perfect control methodology is not productive and can even lead to
inaccurate or misleading conclusions regarding the appli-cability
or performance of certain control techniques. Flight simulation
based on the developed model can be used to complement flight
testing (Johnson et.al., 1996; Johnson and DeBitetto, 1997;
Munzinger, 1998; Perhinschi and Prasad, 1998; Johnson and Fontaine,
2002; Johnson and Mishra, 2002; Lee and Horn, 2005). Guidance can
be viewed as the most outer loop of multi-loop control sys-tem.
A. Simulation environment for UAV
Research in control engineering regularly produces new
theoretical insights and algorithms that promise substantial
improvement over the state of the practice. However, it is only a
small fraction of this research that ultimately sees practical
application (Samad et.al, 2004). The area of con-trol for UAVs is
not an exception. The need to close the gap between theory and
application of control to UAVs in real operating conditions has
been addressed by creating simu-lation environment where actual
time-dependent signals are taken into account. Implementation and
testing of control systems by a hardware-in-the-loop (HIL)
simulation is in-creasingly being required for the design as it
becomes a very versatile tool in acquiring real data without taking
a risk of losing any expensive instrumented UAVs. HIL si-mulation
is characterized by the operation of real compo-nents in connection
with real-time simulated components. Usually, the control system
hardware and software is the real system while the controlled plant
can be either fully or partially simulated. The high-confidence
control can be achieved by developing increasingly higher fidelity
models and simulations through successive improvements. It should
be ensured that the plant model is a sufficiently ac-curate
approximation of reality and that assumptions about disturbances
and the operational environment are valid. The implementation of
HILS for various RUAVs at Smart Robot Center (Konkuk University) is
illustrated in Fig. 5.
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CHEN LiQun et al. Chinese Science Bulletin | Jan??? 2007 | vol.
52 | no. ? | ?-? 9
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PC104
long
lat
col
ped
,,
Motiontable
IronBird
HardwareIntheLoop(HIL)Simulator
IMU
HostPC xPC
RS232 RS232
Windows
FlightcontrolexecSensorsignalprocessorGCScommunication
FlightGearCockpitview
Matlab/Simulink6DOFheli nonlinearmdlRTW/XPCTargetappl.
HealthmonitoringInteractiveautopilotingGroundcontrolGUIHighlevelcontroller
RTsimulink executionBidirectionalcommunication
Figure 5:Simulation environment for UAV control synthesis
B. Control Synthesis Given the sufficiently accurate model, the
control synthesis of RUAV can be conducted and validated within
real-time simulation environment. Various control techniques have
been developed thus far in Budiyono (2005a, 2005b) and Budiyono
et.al. (2004, 2005, 2007a). Referring to the tax-onomy of flight
conditions of RUAV (Fig. 3), the control design can be classified
into the following different ap-proaches:
1. Classical control. Since the problem of RUAV control is a
MIMO problem, the design procedure of classical approach is to be
conducted in cascaded multi-loop SISO system starting from the
innermost loop out-ward. The cascaded multi-loop SISO approach
how-ever has limitations in its implementation. To imple-ment this
control approach for a small scale helicopter, a pitch and roll
attitude control system is often subor-dinated to a, respectively,
longitudinal and lateral ve-locity control system in a nested
architecture. The re-quirement for this technique to work is that
the inner attitude control loop must have a higher bandwidth than
the outer velocity control loop. While this is va-
lid for a relatively large unmanned helicopter such as Yamaha
R-50, for a class of high-performance heli-copters, such as the
X-Cell 60, or helicopters where this bandwidth separation is not
sufficient, a simulta-neous design will be necessary (Mettler,
2003). The simultaneous design is provided by modern control
synthesis.
2. Modern MIMO control. To control a model helicopter as a
complex MIMO system, an approach that can synthesize a control
algorithm to make the helicopter meet performance criteria while
satisfying some physical constraints is required. To address a MIMO
problem, LQR and H are the most popular control design procedures.
These methods however also have drawbacks that can inhibit a
practical implementation. They include dealing with higher than
necessary order of controller, non-existence of formal parameter
tun-ing and weight selection procedures, possible exclu-sion of
good controllers, and difficulty in integrating state variable
constraints (Manabe, 2002).
3. Algebraic control. The CDM is one of such ap-proaches where
control design process is based on
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10 CHEN LiQun et al. Chinese Science Bulletin | Ja??? 2007 |
vol. 52 | no. ? | ?-?
coefficient diagram representing criteria of good de-sign. The
use CDM thus far has been limited to SISO or SIMO applications.
Some trial designs for MIMO have been made (Manabe, 2002), but
formal design procedures to implement CDM for MIMO has not been
established yet. The typical approach in solving MIMO problem thus
far has been to decompose MIMO problems into series of SISO or SIMO
prob-lems and proceed with design by standard CDM. The first
attempt that demonstrates a successful imple-mentation of CDM-based
LQR technique without the need of decomposing a MIMO problem into a
series of SISO or SIMO problems was presented in (Bu-diyono, 2007).
Fig. 6 shows the result of design for step response of u and w
subjected to 30% parameter variation.
4. Hybrid approach. In the hybrid approach, each linear model in
Fig. 3 can be considered as a hybrid auto-maton. To represent an
RUAV flying over wider flight envelope therefore, the approach
leads to a switching problem representing a change from one mode to
another. A synthesis of switched control systems for model
helicopter excited with external switches that bring changes of
dynamics from hover to cruise by satisfying some constraint in the
trajectories can thus be performed. Piecewise quadratic
Lyapunov-like functions that leads to linear matrix inequalities
(LMIs) for performance analysis and controller syn-thesis can be
considered. State jumps of the controller responding to switched of
plant dynamics are ex-ploited to improve control performance
(Sutarto et.al., 2006). The result is illustrated in Fig. 7 showing
comparison between performance of LQR and Switched Linear
Control.
0 10 20 30 40 50 60 70 80 90-0.2
0
0.2
0.4
0.6
0.8
1
1.2
u (m
/s)
t (s)
0 10 20 30 40 50 60 70 80 90-0.2
0
0.2
0.4
0.6
0.8
1
w (m
/s)
t (s)
nominal-30% in xu,xa,mq+30% in xu,xa,mq
Figure 6: CDM-LQR control design
0 5 10 15 20 25 30-15
-10
-5
0
5
10
15
20
25Speed w
Time (Second)
ft/se
c
LQROutput SLCState SLC
Figure 7: Comparison of Switched Linear Control and LQR
5. LPV approach. The control design is performed based on the
model developed through LPV iden-tification. Model Predictive
Control (MPC) can be a good candidate for such an approach.
V. Emerging Technologies
Issues pertaining to increased demand for higher perfor-mance
and safety have pushed the UAV design beyond conventional
approaches. Some emerging technologies can be summarized in the
following paragraph.
A. Bio-inspired Technologies and Biorobotics
The emerging field of unmanned system technologies largely
relies on the ability of an onboard mechanism that replaces or
imitates a human operator. To successfully design an unmanned
system or vehicle therefore it is im-portant to study the human
intelligent at all levels: reason-ing, perception, development and
learning. Moreover, the compelling need to learn from nature stems
from the fact that although the present conventional approach to
engi-neering design may exceed nature in some regards, they are not
superior to many designs in nature. Using conventional approach,
present day UAVs can perform different control functions including
altitude and speed hold, obstacle avoidance, terrain following
navigation, and autonomous landing. Flying insects can perform all
those and beyond, remarkably well using ingenious strategies for
perception and navigation in three dimensions. Insects infer
distances to potential obstacles and objects of interest from image
motion cues that result from their own motion in the envi-ronment.
The angular motion of texture in images is de-noted generally as
optic or optical flow. Computationally, a strategy based on optical
flow is simpler than is stereosco-
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CHEN LiQun et al. Chinese Science Bulletin | Jan??? 2007 | vol.
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py for avoiding hazards and following terrain (Thakoor, S.
et.al. , 2003).
Recent studies also demonstrate that insects can perform extreme
maneuvering capabilities far beyond those achieved by conventional
UAVs. Flapping wing, morphing wing, formation flight, neuro-control
and swarming are just a few examples of natural phenomena much
related to UAVs advanced design features. More research should be
consistently conducted for harvesting design principles from nature
that would extend present UAV technologies out of its conventional
boundaries.
B. Multi UAV Systems
One primary feature of high autonomy UAVs is their ability to
perform coordination and cooperation functions. This capability is
termed Level 5 and 6 in Table 2. Research in this area
(collaborative sensing and exploration, synchro-nized motion
planning, and formation or cooperative con-trol) has been gaining
more interests in recent past as shown for example in (Seiler,
2001) and Mot et al. (2002a, 2002b). A particular class of tasks
for such mul-ti-agent UAV systems involve surveillance of a region
and tracking of targets cooperatively. Cooperative agents are
typically desired to handle a particular task with higher
robustness, higher performance (faster or more accurately) or task
simply otherwise unattainable by single agent. UAVs formation
control can be achieved through hierar-chical (leader-follower) or
non-hierarchical approach.
Cooperative multi-agents naturally lead to hybrid system
abstraction. The hybrid model would capture both UAV dynamics and
mode switching logic that supervises lower level control switches.
It will be desirable in this regards to have a formal tool that can
verify the performance and safety of such a system where high
fidelity simulation can be conducted prior to flight tests. Future
research direction in multi UAVs system should address this
need.
VI. Concluding Remarks
The paper discussed recent progress in the technology for
unmanned aerial vehicles from the modeling, control and guidance
perspectives. Dynamics of rotorcraft-based un-manned aerial vehicle
is presented to describe the underly-ing principle of modeling for
the control synthesis. The modeling based on first principle,
system identification and LPV identification is presented briefly
for illustration. A number of major trends in aerial robotics are
discussed: state estimation algorithm, SLAM, vision for guidance,
integrated modeling, maneuver automaton and safety veri-
fication. Future challenges for advancing aerial robotics
technology will be pivoted on exploitation of biomimetic principles
for achieving higher peformance and develop-ment of formal model
and analysis tool to synthesize col-laborative aerial robotics
behavior.
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