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Daniel Guggenheim School of Aerospace Engineering

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https://hdl.handle.net/1853/70742

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College of Engineering

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Aerospace Design Group

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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Publication Search Results

Now showing 1 - 10 of 22

Terrain Height Evidence Sharing for Collaborative Autonomous Rotorcraft Operation

(Georgia Institute of Technology, 2013-01) Johnson, Eric N. ; Mooney, John G. ; White, Matthew ; Hartman, Jonathan ; Sahasrabudhe, Vineet

This paper describes recent results from a partnership between the Sikorsky Aircraft Corporation and the Georgia Institute of Technology to develop, improve, and flight test a sensor, guidance, navigation, control, and real-time information sharing system to support collaborative autonomy and high performance nap-of-the-Earth helicopter flight. The emphasis here is on smart and selective sharing of terrain data which (1) minimizes the bandwidth consumed by obstacle/terrain-information-sharing between aircraft, (2) assigns an appropriate level of confidence to the data received from other heterogeneous aircraft, (3) is robust to sensor error and failures, and (4) is robust to entry and exit of vehicles from the network. Results from simulation and flight testing are provided.
Markov Modeling of Component Fault Growth Over A Derived Domain of Feasible Output Control Effort Modifications

(Georgia Institute of Technology, 2012-08) Bole, Brian ; Goebel, Kai ; Vachtsevanos, George J.

This paper introduces a novel Markov process formulation of stochastic fault growth modeling, in order to facilitate the development and analysis of prognostics-based control adaptation. A metric representing the relative deviation between the nominal output of a system and the net output that is actually enacted by an implemented prognostics-based control routine, will be used to define the action space of the formulated Markov process. The state space of the Markov process will be defined in terms of an abstracted metric representing the relative health remaining in each of the system’s components. The proposed formulation of component fault dynamics will conveniently relate feasible system output performance modifications to predictions of future component health deterioration.
Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions

(Georgia Institute of Technology, 2012-06-19) Bole, Brian ; Goebel, Kai ; Vachtsevanos, George J.

A generalized Markov chain representation of fault dynamics is presented for the case that available modeling of fault growth physics and future environmental stresses can be represented by two independent stochastic process models. A contrived but representatively challenging example will be presented and analyzed, in which uncertainty in the modeling of fault growth physics is represented by a uniformly distributed dice throwing process, and a discrete random walk is used to represent uncertain modeling of future exogenous loading demands to be placed on the system. A finite horizon dynamic programming algorithm is used to solve for an optimal control policy over a finite time window for the case that stochastic models representing physics of failure and future environmental stresses are known, and the states of both stochastic processes are observable by implemented control routines. The fundamental limitations of optimization performed in the presence of uncertain modeling information are examined by comparing the outcomes obtained from simulations of an optimizing control policy with the outcomes that would be achievable if all modeling uncertainties were removed from the system.
Flight Testing of Nap of-the-Earth Unmanned Helicopter Systems

(Georgia Institute of Technology, 2011-05) Johnson, Eric N. ; Mooney, John G. ; Ong, Chester ; Sahasrabudhe, Vineet ; Hartman, Jonathan

This paper describes recent results from a partnership between the Sikorsky Aircraft Corporation and the Georgia Institute of Technology to develop, improve, and flight test a sensor, guidance, navigation, control, and real-time flight path optimization system to support high performance nap-of-the-Earth helicopter flight. The emphasis here is on optimization for a combination of low height above terrain/obstacles and high speeds. Multiple methods for generating the desired flight path were evaluated, including (1) a simple processing of each laser scan; and (2) a potential field based method. Simulation and flight test results have been obtained utilizing an onboard laser scanner to detect terrain and obstacles while flying at low altitude, and have successfully demonstrated obstacle avoidance in a realistic semi-urban environment at speeds up to 40 ft/s while maintaining a miss distance of 50 ft horizontally and vertically. These results indicate that the technical approach is sound, paving the way for testing of even lower altitudes, higher speeds, and more aggressive maneuvering in future work.
High Performance Nap-of-the-Earth Unmanned Helicopter Flight

(Georgia Institute of Technology, 2011) Johnson, Eric N. ; Mooney, John G. ; Ong, Chester ; Sahasrabudhe, Vineet ; Hartman, Jonathan

This paper describes recent results from a partnership between the Sikorsky Aircraft Corporation and the Georgia Institute of Technology to develop, improve, and flight test a sensor, guidance, navigation, control, and real-time flight path optimization system to support high performance nap-of-the-Earth helicopter flight. The emphasis here is on optimization for a combination of low height above terrain/obstacles and high speeds. Multiple methods for generating the desired flight path were evaluated, including (1) a simple processing of each laser scan; and (2) a potential field based method. Simulation and flight test results have been obtained utilizing an onboard laser scanner to detect terrain and obstacles while flying at low altitude, and have successfully demonstrated obstacle avoidance at speeds up to 40 ft/s while maintaining a miss distance of 50 ft horizontally and vertically. These results indicate that the technical approach is sound, paving the way for testing of even lower altitudes, higher speeds, and more aggressive maneuvering in future work.
Flight-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.
Adaptive, Integrated Guidance and Control Design for Line-of-Sight-Based Formation Flight

(Georgia Institute of Technology, 2007-10) Kim, Byoung Soo ; Calise, Anthony J. ; Sattigeri, Ramachandra J.

This paper presents an integrated guidance and control design for formation flight using a combination of adaptive output feedback and backstepping techniques. We formulate the problem as an adaptive output feedback control problem for a line-of-sight-based formation flight configuration of a leader and a follower aircraft. The design objective is to regulate range and two bearing angle rates while maintaining turn coordination. Adaptive neural networks are trained online with available measurements to compensate for unmodeled nonlinearities in the design process. These include uncertainties due to unknown leader aircraft acceleration, and the modeling error due to parametric uncertainties in the aircraft aerodynamic derivatives. One benefit of this approach is that the guidance and flight control design process is integrated. Simulation results using a nonlinear 6 degrees-of-freedom simulation model are presented to illustrate the efficacy of the approach by comparing the performance with an adaptive timescale separation-based guidance and control design.
Adaptive Output Feedback Control of a Flexible Base Manipulator

(Georgia Institute of Technology, 2007-07) Yang, Bong-Jun ; Calise, Anthony J. ; Craig, James I.

This paper considers augmentation of an existing inertial damping mechanism by neural network-based adaptive control, for controlling a micromanipulator that is serially attached to a macromanipulator. The approach is demonstrated using an experimental test bed in which the micromanipulator is mounted at the tip of a cantilevered beam that resembles a macromanipulator with its joint locked. The inertial damping control combines acceleration feedback with position control for the micromanipulator so as to simultaneously suppress vibrations caused by the flexible beam while achieving precise tip positioning. Neural network-based adaptive elements are employed to augment the inertial damping controller when the existing control system becomes deficient due to modeling errors and uncertain operating conditions. There were several design challenges that had to be faced from an adaptive control perspective. One challenge was the presence of a nonminimum phase zero in an output feedback adaptive control design setting in which the regulated output variable has zero relative degree. Other challenges included flexibility in the actuation devices, lack of control degrees of freedom, and high dimensionality of the system dynamics. In this paper we describe how we overcame these difficulties by modifying a previous augmenting adaptive approach to make it suitable for this application. Experimental results are provided to illustrate the effectiveness of the augmenting approach to adaptive output feedback control design.
Real-Time Vision-Based Relative Aircraft Navigation

(Georgia Institute of Technology, 2007-03) Johnson, Eric N. ; Calise, Anthony J. ; Watanabe, Yoko ; Ha, Jin-Cheol ; Neidhoefer, James C.

This paper describes two vision-based techniques for the navigation of an aircraft relative to an airborne target using only information from a single camera fixed to the aircraft. These techniques are motivated by problems such as "see and avoid", pursuit, formation flying, and in-air refueling. By applying an Extended Kalman Filter for relative state estimation, both the velocity and position of the aircraft relative to the target can be estimated. While relative states such as bearing can be estimated fairly easily, estimating the range to the target is more difficult because it requires achieving valid depth perception with a single camera. The two techniques presented here offer distinct solutions to this problem. The first technique, Center Only Relative State Estimation, uses optimal control to generate an optimal (sinusoidal) trajectory to a desired location relative to the target that results in accurate range-to-target estimates while making minimal demands on the image processing system.The second technique, Subtended Angle Relative State Estimation, uses more rigorous image processing to arrive at a valid range estimate without requiring the aircraft to follow a prescribed path. Simulation results indicate that both methods yield range estimates of comparable accuracy while placing different demands on the aircraft and its systems.
Modeling, Control, and Flight Testing of a Small Ducted Fan Aircraft

(Georgia Institute of Technology, 2006-07) Johnson, Eric N. ; Turbe, Michael A.

Small ducted fan autonomous vehicles have potential for several applications, especially for missions in urban environments. This paper discusses the use of dynamic inversion with neural network adaptation to provide an adaptive controller for the GTSpy, a small ducted fan autonomous vehicle based on the Micro Autonomous Systems' Helispy. This approach allows utilization of the entire low speed flight envelope with a relatively poorly understood vehicle. A simulator model is constructed from a force and moment analysis of the vehicle, allowing for a validation of the controller in preparation for flight testing. Data from flight testing of the system is provided.