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ItemFlight-Test Results of Autonomous Airplane Transitions Between Steady-Level and Hovering Flight(Georgia Institute of Technology, 2008-03) Johnson, Eric N. ; Wu, Allen D. ; Neidhoefer, James C. ; Kannan, Suresh K. ; Turbe, Michael A.Linear systems can be used to adequately model and control an aircraft in either ideal steady-level flight or in ideal hovering flight. However, constructing a single unified system capable of adequately modeling or controlling an airplane in steady-level flight and in hovering flight, as well as during the highly nonlinear transitions between the two, requires the use of more complex systems, such as scheduled-linear, nonlinear, or stable adaptive systems. This paper discusses the use of dynamic inversion with real-time neural network adaptation as a means to provide a single adaptive controller capable of controlling a fixed-wing unmanned aircraft system in all three flight phases: steady-level flight, hovering flight, and the transitions between them. Having a single controller that can achieve and transition between steady-level and hovering flight allows utilization of the entire low-speed flight envelope, even beyond stall conditions. This method is applied to the GTEdge, an eight-foot wingspan, fixed-wing unmanned aircraft system that has been fully instrumented for autonomous flight. This paper presents data from actual flight-test experiments in which the airplane transitions from high-speed, steady-level flight into a hovering condition and then back again.
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ItemFlight Results of Autonomous Fixed-Wing UAV Transitions to and from Stationary Hover(Georgia Institute of Technology, 2006-08) Johnson, Eric N. ; Turbe, Michael A. ; Wu, Allen D. ; Kannan, Suresh K. ; Neidhoefer, James C.Fixed-wing unmanned aerial vehicles (UAVs) with the ability to hover have significant potential for applications in urban or other constrained environments where the combination of fast speed, endurance, and stable hovering flight can provide strategic advantages. This paper discusses the use of dynamic inversion with neural network adaptation to provide an adaptive controller capable of transitioning a fixed-wing UAV to and from hovering flight in a nearly stationary position. This approach allows utilization of the entire low speed flight envelope even beyond stall conditions. The method is applied to the GTEdge, an 8.75 foot wing span fixed-wing aerobatic UAV which has been fully instrumented for autonomous flight. Results from actual flight test experiments of the system where the airplane transitions from high speed steady flight into a stationary hover and then back are presented.
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ItemA Compact Guidance, Navigation, and Control System for Unmanned Aerial Vehicles(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.
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ItemAdaptive Trajectory Control for Autonomous Helicopters(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.
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ItemSmall Adaptive Flight Control Systems for UAVs Using FPGA/DSP Technology(Georgia Institute of Technology, 2004-09) Christophersen, Henrik B. ; Pickell, Wayne J. ; Koller, Adrian A. ; Kannan, Suresh K. ; Johnson, Eric N.Future small UAVs will require enhanced capabilities like seeing and avoiding obstacles, tolerating unpredicted flight conditions, interfacing with payload sensors, tracking moving targets, and cooperating with other manned and unmanned systems. Cross-platform commonality to simplify system integration and training of personnel is also desired. A small guidance, navigation, and control system has been developed and tested. It employs Field Programmable Gate Array (FPGA) and Digital Signal Processor (DSP) technology to satisfy the requirements for more advanced vehicle behavior in a small package. Having these two processors in the system enables custom vehicle interfacing and fast sequential processing of high-level control algorithms. This paper focuses first on the design aspects of the hardware and the low-level software. Discussion of flight test experience with the system controlling both an unmanned helicopter and an 11-inch ducted fan follow.
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ItemDevelopment of an Autonomous Aerial Reconnaissance System at Georgia Tech(Georgia Institute of Technology, 2003-07) Proctor, Alison A. ; Kannan, Suresh K. ; Raabe, Chris ; Christophersen, Henrik B. ; Johnson, Eric N.The Georgia Tech aerial robotics team has developed a system to compete in the International Aerial Robotics Competition, organized by the Association for Unmanned Vehicle Systems, International. The team is a multi-disciplinary group of students who have developed a multi-year strategy to complete all levels and the overall mission. The approach taken to achieve the objectives of the required missions has evolved to incorporate new ideas and lessons learned. This document summarizes the approach taken, the current status of the project, and the design of the components and subsystems.
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ItemNested Saturation with Guaranteed Real Poles(Georgia Institute of Technology, 2003-06) Johnson, Eric N. ; Kannan, Suresh K.The global stabilization of asymptotically null-controllable linear systems with bounded controls has been studied extensively. An early contribution was by Teel [6] who proposed a set of nested saturators to globally asymptotically stabilize the special case of n-integrators with one input. Using this law however, the closed loop system pole locations depend on the choice of coordinate transformation used to arrive at the control law. In this paper we suggest an approach that allows the designer to pick transformations that facilitate the placement of the closed loop poles on the negative real axis.