Organizational Unit:
Aerospace Design Group

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https://hdl.handle.net/1853/70743
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https://finding-aids.library.gatech.edu/agents/corporate_entities/1134
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  • Item
    Adaptive Control of Evolving Gossamer Structures
    (Georgia Institute of Technology, 2006-08) Yang, Bong-Jun ; Calise, Anthony J. ; Craig, James I. ; Whorton, Mark S. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Design Group ; College of Engineering
    A solar sail is an example of a gossamer structure that is proposed as an propulsion system for future space missions. Since it is a large scale flexible structure that requires a long time for its deployment, active control may be required to prevent it from deviating into a non-recoverable state. In this paper, we conceptually address control of an evolving flexible structure using a growing double pendulum model. Controlling an evolving system poses a major challenge to control design because it involves time-varying parameters, such as inertia and stiffness. By employing a neural network based adaptive control, we illustrate that the evolving double pendulum can be effectively regulated when fixed-gain controllers are deficient due to presence of time-varying parameters.
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    6-DOF Nonlinear Simulation of Vision-based Formation Flight
    (Georgia Institute of Technology, 2005-08) Sattigeri, Ramachandra J. ; Calise, Anthony J. ; Kim, Byoung Soo ; Volyanskyy, Konstantin ; Kim, Nakwan ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Design Group ; College of Engineering
    This paper presents an adaptive guidance and control law algorithm for implementation on a pair of Unmanned Aerial Vehicles (UAVs) in a 6 DOF leader-follower formation flight simulation. The objective of the simulation study is to prepare for a flight test involving a pair of UAVs in formation flight where the follower aircraft will be equipped with an onboard camera to estimate the relative distance and orientation to the leader aircraft. The follower guidance law is an adaptive acceleration based guidance law designed for the purpose of tracking a maneuvering leader aircraft. We also discuss the limitations of a preceding version of the guidance algorithm shown in a previous paper. Finally, we discuss the design of an adaptive controller (autopilot) to track the commands from the guidance algorithm. Simulation results for different leader maneuvers are presented and analyzed.
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    Modeling Urban Environments for Communication-Aware UAV Swarm Path Planning
    (Georgia Institute of Technology, 2010-08) Christmann, Hans Claus ; Johnson, Eric N. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Design Group ; College of Engineering ; Unmanned Aerial Vehicle Research Facility
    The presented work introduces a graph based approach to model urban (or otherwise cluttered) environments for UAS utilization beyond line-of-sight as well as out of direct R/F range of the operator's control station. Making the assumption that some a priori data of the environment is available, the proposed method uses a classification of obstacles with respect to their impact on UAV motion and R/F communication and generates continuously updateable graphs usable to compute traverseable paths for UAVs while maintaining R/F communication. Using a simulated urban scenario this work shows that the proposed modeling method allows to find reachable loiter or hover areas for UAVs in order to establish a multi-hop R/F communication link between a primary UAV and its remote operator by utilizing an overlay of motion (Voronoi based) and R/F (visibility based) specific mapping methods.
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    Experimental Validation of an Augmenting Approach to Adaptive Control of Uncertain Nonlinear Systems
    (Georgia Institute of Technology, 2003-08) Yang, Bong-Jun ; Hovakimyan, Naira ; Calise, Anthony J. ; Craig, James I. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Design Group ; College of Engineering
    A method of adaptive output feedback design for uncertain nonlinear systems is presented. The development is in a form that is suitable for augmenting a linear controller. The approach is applicable to non-affine, non-minimum phase systems having parametric and dynamic uncertainties. A requirement is that the non-minimum phase zeros are represented to a sufficient accuracy in the linear controller design. The approach has been experimentally validated using a 3-disk torsional pendulum and an inverted pendulum.
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    Guidance, Navigation, Control, and Operator Interfaces for Small Rapid Response Unmanned Helicopters
    (Georgia Institute of Technology, 2008-04) Christmann, Hans Claus ; Christophersen, Henrik B. ; Wu, Allen D. ; Johnson, Eric N. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Design Group ; College of Engineering ; Unmanned Aerial Vehicle Research Facility
    This paper focuses on the development of small rapid response reconnaissance unmanned helicopters (1 to 3 kg, electric), for use by the military in urban areas and by civilian first responders, in terms of system architecture, automation (including navigation, flight control, and guidance), and operator interface designs. Design objectives include an effective user interface, a vehicle capable of smooth and precise motion control, an ability to display clear images to an operator, and a vehicle that is capable of safe and stable flight.
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    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. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Design Group ; College of Engineering
    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.
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    A 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. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Design Group ; College of Engineering ; Unmanned Aerial Vehicle Research Facility
    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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    Visual Marker Detection In The Presence Of Colored Noise for Unmanned Aerial Vehicles
    (Georgia Institute of Technology, 2010-04) Shah, Syed Irtiza Ali ; Wu, Allen D. ; Johnson, Eric N. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Design Group ; College of Engineering ; Unmanned Aerial Vehicle Research Facility
    This paper develops a vision-based algorithm to detect a visual marker in real time and in the presence of excessive colored noise for Unmanned Aerial Vehicles. After using various image analysis techniques, including color histograms, filtering techniques and color space analyses, typical pixel-based characteristics of the visual marker were established. It was found that not only various color space based characteristics were significant, but also relationships between various channels across different color spaces were of great consequence. A block based search algorithm was then used to search for those established characteristics in real-time image data stream from a colored camera. A low cost noise and interference filter was also devised to handle excessive noise that was encountered during flight tests. The specific implementation scenario is that of detection of a Blue LED for GeorgiaTech's participating aircraft into the International Aerial Robotics competition. The final algorithm that was implemented on GTAR lama aircraft, used both multiple thresholding and linear confidence level calculations and was successfully used in the competition in 2009.
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    Limited Authority Adaptive Flight Control for Reusable Launch Vehicles
    (Georgia Institute of Technology, 2003-11) Johnson, Eric N. ; Calise, Anthony J. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Design Group ; College of Engineering
    In the application of adaptive flight control, significant issues arise due to limitations in the plant inputs, such as actuator displacement limits, actuator rate limits, linear input dynamics, and time delay. A method is introduced that allows an adaptive law to be designed for the system without these input characteristics and then to be applied to the system with these characteristics, without affecting adaptation. This includes allowing correct adaptation while the plant input is saturated and allows the adaptation law to function when not actually in control of the plant. To apply the method, estimates of actuator positions must be found. However, the adaptation law can correct for errors in these estimates. Proof of boundedness of system signals is provided for a single hidden-layer perceptron neural network adaptive law. Simulation results utilizing the methods introduced for neural network adaptive control of a reusable launch vehicle are presented for nominal flight and under failure cases that require considerable adaptation.
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    An LMI-based Stability Analysis for Adaptive Controllers
    (Georgia Institute of Technology, 2009-06) Yang, Bong-Jun ; Yucelen, Tansel ; Calise, Anthony J. ; Shin, Jong-Yeob ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Design Group ; College of Engineering
    We develop a Linear Matrix Inequality (LMI) tool for analyzing the stability and performance of adaptive controllers that employ σ−modification. The formulation involves recasting the error dynamics composed of the tracking error and the weight estimator error into a linear parameter varying form. We show how stability, convergence rate, domain of attraction, and the transient and steady state behavior of the adaptive control system can be analyzed using the developed LMI tool. It is guaranteed that less conservative estimates for the convergence rate and the size of the ultimate bound for the tracking error are obtained compared to the standard analysis in the literature.