Team: Ohio State University (lead), UD, UC, and AFITManpower: 7
faculty, 3 post docs, 12 grad students
Established in Oct 2001 $1M per year shared equally by VA and
AFOSR
Cost share: $700K from State of OH, $1,055K from OSU, UC &
UD
Synergies and leveraging: $6M from NASA, NSF, NIST, DARPA
Formal annual reviews: 100+ attendees from DoD & industry
Executive Board consists of government, industry, academia
Control Science Collaborative CenterCCCS considered a Model
Center
COUNTER is a 6.2 program that is being supported by 6.1 research
into Object Allocation and Cooperative Control Algorithms.
COUNTER Program April 2005 to January 2008Scenario is a small
surveillance UAV identifying targets in cluttered urban terrain.
Small UAV provides Target identification, verification, or tagging
for a separate shooterCommunicates with special operations
personnel on the groundEnvisioned to have optical sensors with wide
field of regard and low resolutionReleases one or more MAVs to fly
close to the potential targets
Micro UAV (MAV) providesFly close to potential targets and find,
identify, verify, or tag targets by providing a close-in
lookProvides a higher resolution image of objects of interest or
provides images of objects that are occuldedMAVs are extensions of
sensors on board the Small UAV The COUNTER functional cooperative
controller mission architecture is illustrated in main figure. The
architecture consists of five major subsystems: SAV, MAV,
cooperative controller, workload manager, and operator &
operator vehicle interface. The Cooperative Controller (CC)
Architecture is essentially a centralized controller with spatially
distributed tasking members. The tasking members are the SAV, MAV,
and operator; which means these members accept and perform tasks
assigned to them by the CC. The SAV subsystem contains three
functional blocks: Trajectory Generation, Flight Dynamics and
Cueing Algorithms. The MAV subsystem contains four functional
blocks: Controller, Trajectory Generation, Flight Dynamics and
Video Processing. The Cooperative Control algorithm consists of two
functional blocks: MAV Object Allocation, and MAV Control Design.
(6.1 research)The Workload Manager attempts to regulate the
operator workload.The Vigilant Spirit Control Station (VSCS) is
being utilized by the COUNTER program to support the control of
multiple heterogeneous UAVs. VSCS is a 6.2 research program within
the Human Effectiveness Directorates ( AFRL/HECI). The VSCS program
focuses on the human interfaces required to allow one operator to
control multiple lethal UAVs in more advanced mission areas such as
auto air refueling and weapons delivery.
Team MemberCOUNTER ContributionsAFRL/VACAManage the program;
Develop cooperative control algorithm using the Engineering
SimulationAFRL/HECIDevelop the Vigilant Spirit Control Station
(OVI) for multiple UAVs; Develop Workload Manager and Supervisor
models AFRL/MNAVProvide two MAVs; Flight test support; Potential
upgrade: collision avoidance techniques, autopilotAFRL/VACDDevelop
the COUNTER Operational Simulation to test Cooperative Control
Algorithm & the OVI Station using the Urban Simulation
EnvironmentAFSOCOperator RequirementsAFRL/SNZTProvide flight test
support vanAFRL/SNATDevelop target cuing algorithms to aid the
operator in selecting targetsAFRL/IFGCAssist in generating logged
video data; UAV communication link study88 WS/WESWeather updates
during the demonstrations
Plot shows 4 Micro UAV tours (red, blue, green & gold). All
vehicles start at the large green dot. Targets are red dots
numbered 1 thru 20.
Objective is to visit all of the targets while maintaining the
maximum possible reserve capacity on each of the MAVs. In order to
do this, the algorithm solves two problems. First, it decides which
UAV needs to find which set of targets; and then which path each
UAV needs to take in order to find these targets. The algorithms
needs to do this in minimum time following minimum path. This is
necessary since micro air vehicles have only flight time of
approximately 25 minutes and we need all these vehicles to have
reserve capacity to carry on other tasks for the mission.
This algorithm immediately provides a good feasible solution.
This makes the algorithm a good candidate for implementation in
realistic systems. The quality of the solution monotonically
improves over algorithm run-time until the optimal solution is
reached.
Referring to the block diagram on the previous chart, we have
completed the work corresponding to the block MAV object
allocation. We are now in the process of taking human operator
inputs into consideration in order for MAVs to decide their plan of
action. For example, MAVs may go take another look at an object, or
they may decide to fly around an object to get 360 degree view,
etc. This work is represented by the block MAV control design.
It is necessary to note that we are addressing long term basic
research needs while we use spin offs of our research results to
solve near term problems. Our 6.1 work is playing a critical role
in our important 6.2 program. Our 6.1 research will continue after
the 6.2 program for example, by extending our resource allocation
research to address more complex battle scenarios such as pop up
threats, decoys, false information in the network etc..Over the
past year we have continued the development of our nonlinear model
for a scramjet powered hypersonic vehicle. As the aircraft flies
its mission, it will be subject to a very harsh thermal environment
that will require a thermal protection system to maintain
acceptable internal temperatures of the aircraft structure.
Preliminary analysis conducted by NASA Langley during the NASP
program has shown that the natural frequencies of the structure are
reduced when the temperature of the structure is increased.
However, the effects on the aircraft dynamics have not been
quantified. Therefore, we have undertaken an effort to include the
aerothermal effects on the structural dynamics of the aircraft. As
a first step, we have come up with a means of including the
temperature effects on the structural dynamics throughout the
mission. Work is currently underway to include an unsteady heat
transfer model for a thermal protection system based on legacy
concepts in the open-literature in order to determine the
temperature of the structure due to the integral heat load. This
capability will give us the ability to study a wide range of flight
control related issues at any point along the vehicles
trajectory.
The second item that I would like to mention is that a study has
been completed to investigate how the aircraft controllability can
be improved. For the configuration that we have studied (a scaled
X-43), there is a real, right-half plane transmission zero
associated with the flight path angle dynamics that is
significantly closer to the imaginary axis than what is seen on
conventional aircraft. It can be shown that the frequency of this
zero is related to the instantaneous center-of-rotation for the
aircraft, which in this case is well forward of the
center-of-gravity. One way to move this zero to the right and away
from the imaginary axis is to move the center-of-rotation closer to
the center-of-gravity. This can be accomplished by the addition of
another set of control effectors that are ahead of the cg-- for
example a set of canards. The flight control engineer can then
schedule the effectors such that the RHP zero moves to the right be
some predetermined amout, thus improving the lower bound on the
available bandwidth, resulting in enhanced control system
performance.Currently, we are using detailed experimental data for
the purpose of developing reduced-order flow models.Order reduction
is done by Proper Orthogonal Decomposition (POD) of experimental
data, then Galerkin projection of the Navier-Stokes equations onto
the POD modes.The velocity data is complemented with surface
pressure data and stochastic estimation method is used to obtain
time coefficients for the POD modes in real-time.The picture on the
top-right is a photograph of the wind tunnel with side wall
removed, which shows the cavity and the synthetic type (or a
compression driver) actuator. The picture on the bottom-left shows
velocity field with intricate patterns of the cavity flow at Mach
0.3 obtained experimentally using particle imaging velocimetry
(PIV) technique both velocity vectors and magnitude are shown. The
surface pressure measurements at the numbered locations are used
along with stochastic estimation to obtain POD time
coefficients.The bottom right figure compares the pressure spectra
at the cavity floor, for the baseline case (no control), open-loop
control, and LQ control based on the reduced order model. The
open-loop case is doing a good job in reducing the resonance tone,
but leaves a tone at the forcing frequency (3920 Hz) and its
sub-harmonic. On the other hand, the LQ control distribute the
energy over various frequencies.
