Virginia Space Grant Consortium
Aerospace Graduate Research Fellowship Application
Academic Year 2001-2002
Development of a Satellite Adaptive Attitude Controller
Submitted by:
Andrew J. Turner
Virginia Polytechnic Institute and State University
Advisor:
Dr. Christopher Hall
VSGC Grad Application
Development of a satellite adaptive attitude controller
Abstract
In this project we will develop an adaptive neural network attitude controller for use in
satellite missions. A network of synaptic nodes will ‘learn’ the satellite flight characteristics and
necessary commands to achieve the mission requirements. Furthermore, the controller will adapt
to physical uncertainties, and system failures using on-line training algorithms. The system will
first be tested in a MatLab-Freeflyer computer simulation, and then on a dynamic air bearing
spacecraft simulator. Following successful ground testing, the adaptive control system will be
used in the control system for the Virginia Tech nanosatellite, HokieSat.
Project Objective
The principal purpose of this project is to develop an adaptive system which controls a
satellite’s attitude while accounting for physical uncertainties and system failures. This
objective is broken down into the following list of tasks:
1. Design an adaptive neural network controller that adequately controls a satellite’s
attitude
Using existing methods of neural network development [1], an adequate controller can be
designed that will control the three axis angles, and three rotation rates of a satellite’s attitude
[2][3][4][5]. However, unlike a traditional satellite, this controller is intended for use on a
nanosatellite approximately 18 inches in diameter that uses three torque-coils for control.
Traditional control laws are difficult to implement since torque-coils can only produce a moment
perpendicular to the local magnetic field, which may not be in the desired control direction.
Therefore the controller must determine a control strategy that will allow the spacecraft to obtain
any attitude orientation, regardless of the local magnetic field.
Andrew Turner
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VSGC Grad Application
Development of a satellite adaptive attitude controller
Figure 1 shows a specific example of a neural network controller that uses an optimizer to
determine the control inputs to the system [5]. The desired attitude is given by (t), and the
optimizer, using the information from the neural network, determines the control signal u(t). The
system response, y(t), and the expected response, yˆ (t ) , are differenced to produce the error, (t).
(t) is used in a training algorithm that determines the appropriate neural network updates to
produce an adequate control response. This updating allows for online training. This loop
continues as the satellite is controlled while the neural network is continuously updated.
u(t)
System
y (t )
(t)
Neural
Network
Optimizer
+

yˆ (t )
e(t)

+
 (t)
Figure 1: Neural Network Controller Block Diagram
2. Determine system response to uncertainties and system failures
By modeling the satellite system in a simulated environment such as MatLab, the neural
network controller can be written in MatLab script and tested. This model will be used with the
FreeFlyer satellite simulation interface to fully simulate the satellite’s orbit and attitude over the
mission lifetime. Changing such parameters as atmospheric drag, moments of inertia, and
control torques will allow the neural network response to these effects to be characterized.
Andrew Turner
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VSGC Grad Application
Development of a satellite adaptive attitude controller
3. Develop network algorithms to employ on-line training and adapt to system changes
The neural network controller shown above will use on-line adaptive, or “Self-Learning”
algorithms to adjust the synaptic node weights. However, the back-propagation algorithms that
determine the actual values of the weight updates between successive time steps must be
determined and optimized. A set of tests will be developed that can fully characterize the
controller response to system failures. Using this characterization, the online training algorithms
will be modified to produce a satisfactory satellite response to the simulated uncertainties and
failures.
4. Test controller on a ground-based spacecraft simulator
Colleagues at Virginia Tech are currently developing a Distributed Spacecraft Attitude
Control System Simulator (DSACSS). The simulator uses a network of hardware systems,
including computers, sensors, and actuators, mounted on three-axis air-bearing tables to model
an orbiting satellite. The adaptive control system developed in this project will be tested using
the DSACSS. The simulated network will be implemented in C/C++ and installed on the flight
computer. A series of tests will be designed to measure the control effectiveness, and to
characterize the response to system failures and uncertainties. These results will be compared to
the simulated results produced in the MatLab-Freeflyer code.
5. Determine controller effectiveness on an actual nanosatellite
Virginia Polytechnic Institute and State University is building a 15-kg nanosatellite,
HokieSat, which will launch from the space shuttle in 2002. Its mission is to demonstrate
formation flying with two other nanosatellites being built by the University of Washington and
Utah State University, as well as to collect ionospheric measurements using GPS and 2 types of
Andrew Turner
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VSGC Grad Application
Development of a satellite adaptive attitude controller
electron probes [6][7]. Following successful completion of the ground testing of the controller,
the system will be installed on HokieSat and tested during the mission.
Justifications
The majority of current satellite control technologies employ classical methods that are
determined a priori. However, due to changes in the satellite system, or unknown flight
characteristics, these systems may prove to be unreliable. Adaptive control systems provide a
robust means of controlling a satellite in the face of these uncertainties and failures.
In this research project we will develop a control system that will adapt to the satellite’s
flight characteristics. By using ‘soft-computing’ techniques such as neural networks and genetic
algorithms, the control system can learn and adapt to changes in the controllability of the
satellite.
The use of an attitude control system can greatly reduce the costs of satellite
development. An adaptive control system can learn from a model of the satellite and from the
actual flight mission instead of having to specify the classical control law. Furthermore, a welldesigned neural network can be reused for many missions, without having to change the base
structure of the system. This reuse can greatly reduce the cost of having to develop a control
system from the ground up. Also, the adaptive control system can recover from system failures
that may otherwise cripple a spacecraft such as Mariner 2 and PanAmSat Corporation's Galaxy 4
[Space News, 25-31 May 1998, p. 3]. The robust satellite can recover from these failures
without having to wait for human control and updating of its software.
References
Andrew Turner
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VSGC Grad Application
Development of a satellite adaptive attitude controller
1. D.H. Nguyen, B. Widrow. “Neural Networks for Self-Learning Control Systems.”
International Journal of Control. Vol. 54, No. 6, pp. 1439, 1991.
2. P.J. Antsaklis, K.M. Passino, et al. “Towards Intelligent Autonomous Control Systems:
Architecture and Fundamental Issues.” Journal of Intelligent Robotic Systems. Vol. 1, pp.
315-342, 1989.
3. K. KrishnaKumar. “Adaptive Neuro-Control for Spacecraft Attitude Control.”
Neurocomputing, Vol. 9, No. 2, pp. 131-148. Oct 1995.
4. J.J. Sheen, R.H. Bishop. “Adaptive Nonlinear Control of Spacecraft.” Proceedings of the
American Control Conference. Baltimore, Maryland, pp. 2867-2871. June 1994.
5. J.R. Noriega, H. Wang. “A Direct Adaptive Neural-Network Control for Unknown Nonlinear
Systems and Its Application,” IEEE Transactions on Neural Networks, Vol. 9, No. 1, pp. 2734. Jan 1998.
6. M. Campbell, R. R. Fullmer, and C. D. Hall, "The ION-F Formation Flying Experiments,"
AAS/AIAA Space Flight Mechanics Meeting, Clearwater, FL, Jan 23-26, 2000.
7. N. Davis, J. DeLaRee, C. D. Hall, W. L. Stutzman, and W. A. Scales, "Virginia Tech
Ionospheric Scintillation Measurement Mission," AIAA/Utah State University Conference on
Small Satellites, Logan, UT, Aug 31- Sep 3, 1999, Paper SSC99-III-3.
Andrew Turner
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VSGC Grad Application
Development of a satellite adaptive attitude controller
Applicant: Andrew James Turner
Education
M.S. in Aerospace Engineering, Virginia Tech, expected May 2002
B.S. in Aerospace Engineering (minor-Computer Science), University of Virginia, May 2000
Research and Experience

University Nanosatellite Program design team, Virginia Tech, Blacksburg, Virginia.
HokieSat Attitude Dynamics & Control Team. Jun 2000 – Present

Attitude Dynamics and Control Research Assistant, Virginia Tech, Blacksburg, Virginia.
Fall 1998 – Present

Software Development, Analytical Graphics Inc., Malvern, PA. May 1998 – Aug 1998

Research Assistant, Dynamics and Control System Laboratory, University of Virginia,
Charlottesville, Virginia. Spacecraft & Robotics Simulation & Testing. Dec 1997 – Sep
1998

University of Virginia Solar Airship Project, Charlottesville, Virginia. Control System
Lead, Program Lead, Vice President of Engineering. Oct 1996 – May 2000
Conference Publication

“Development of a Semi-Autonomous Control System for the UVA Solar Airship Aztec,”
3rd Annual Airship Convention, Friedrichshafen, Germany, Jul 2000.
Awards and Honors

Research & Design Symposium Finalist. May 2000

Engineer in Training. Designated Apr 2000

Raven Society Scholar. Apr 1999

University of Virginia Academic Dean’s List. Jan 1998 – May 2000

University of Virginia Solar Airship - Most Outstanding Engineer. May 1999, 2000

Rodman Engineering Honors Student. Sept 1996 – June 2000
Plan of Study
Spring 2001

Applied Numerical Methods

Advanced Orbital Mechanics

Virtual Environments
Fall 2001

Advanced Aerodynamics/Hydrodynamics

Vehicle Structures

Optimization Techniques
Spring 2002

Optimal Control

Advanced Spaceflight Dynamics

Introduction to Artificial Intelligence
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