(Georgia Institute of Technology, 2006-05)
Christophersen, Henrik B.; Pickell, R. Wayne; Neidhoefer, James C.; Koller, Adrian A.; Kannan, Suresh K.; Johnson, Eric N.
The Flight Control System 20 (FCS20) is a compact, self-contained Guidance, Navigation,
and Control system that has recently been developed to enable advanced autonomous
behavior in a wide range of Unmanned Aerial Vehicles (UAVs). The FCS20 uses a floating
point Digital Signal Processor (DSP) for high level serial processing, a Field Programmable
Gate Array (FPGA) for low level parallel processing, and GPS and Micro Electro Mechanical
Systems (MEMS) sensors. In addition to guidance, navigation, and control functions, the
FCS20 is capable of supporting advanced algorithms such as automated reasoning, artificial
vision, and multi-vehicle interaction. The unique contribution of this paper is that it gives a
complete overview of the FCS20 GN&C system, including computing, communications, and
information aspects. Computing aspects of the FCS20 include details about the design process,
hardware components, and board configurations, and specifications. Communications
aspects of the FCS20 include descriptions of internal and external data flow. The information
section describes the FCS20 Operating System (OS), the Support Vehicle Interface Library
(SVIL) software, the navigation Extended Kalman Filter, and the neural network based
adaptive controller. Finally, simulation-based results as well as actual flight test results that
demonstrate the operation of the guidance, navigation, and control algorithms on a real
Unmanned Aerial Vehicle (UAV) are presented.
(Georgia Institute of Technology, 2005)
Johnson, Eric N.; Kannan, Suresh K.
For autonomous helicopter flight, it is common to separate the flight control problem into an inner loop that
controls attitude and an outer loop that controls the translational trajectory of the helicopter. In previous work,
dynamic inversion and neural-network-based adaptation was used to increase performance of the attitude control
system and the method of pseudocontrol hedging (PCH) was used to protect the adaptation process from actuator
limits and dynamics. Adaptation to uncertainty in the attitude, as well as the translational dynamics, is introduced,
thus, minimizing the effects of model error in all six degrees of freedom and leading to more accurate position
tracking. The PCH method is used in a novel way that enables adaptation to occur in the outer loop without
interacting with the attitude dynamics. A pole-placement approach is used that alleviates timescale separation
requirements, allowing the outer-loop bandwidth to be closer to that of the inner loop, thus, increasing position
tracking performance. A poor model of the attitude dynamics and a basic kinematics model is shown to be sufficient
for accurate position tracking. The theory and implementation of such an approach, with a summary of flight-test
results, are described.