Pilot-in-the-Loop Assessment of Neurally Augmented Dynamic Inversion Based Fault Tolerant Control Laws in a Motion-Based Flight Simulator

Sagoo, Girish; Gururajan, Srikanth; Napolitano, Marcello R.; Perhinschi, Mario G.; Gu, Yu; Seanor, Brad; Campa, Giampiero

doi:10.2514/6.2008-6843

Cited by 6 publications

(8 citation statements)

References 7 publications

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“…The simulator has been interfaced (Sagoo et al, 2008) with an external computer on which the aircraft model can be run within the Matlab s /Simulink s environment to drive the entire simulator system. Features data from the flight simulator were collected for several tests at normal flight conditions covering a wide range of flight envelope (Mach numbers between 0.6 and 0.9 and altitudes between 9000 ft and 31,000 ft) at an acquisition rate of 50 Hz to build a set of 378 two-dimensional projections representing the self and nonself of the aircraft model .…”

Section: Experimental Environmentmentioning

confidence: 99%

A dendritic cell mechanism for detection, identification, and evaluation of aircraft failures

Azzawi

Perhinschi

Moncayo

et al. 2015

Control Engineering Practice

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Section: Experimental Environmentmentioning

confidence: 99%

A dendritic cell mechanism for detection, identification, and evaluation of aircraft failures

Azzawi

Perhinschi

Moncayo

et al. 2015

Control Engineering Practice

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“…iii (Forrest et al, 1994) ........ (Perhinschi and Napolitano, 2009) ...................................................................................................... Figure 5.4 Top Level Simulink Model Interfaced with WVU 6-DOF Flight Simulator (Sagoo, 2008) ............................................................................................................ ................................................................................................................................... xii Tables Table 4. 1 Simulated Failures ............................................................................................. (3 Points…”

Section: Immunity-based Detection Identification and Evaluation Of mentioning

confidence: 99%

“…The WVU Flight Simulator has been interfaced (Sagoo, 2008) with an external computer on which the WVU IFCS F-15 research aircraft model can be run within the Matlab/Simulink environment to drive the entire simulator system (see The outputs of this model are sent to X-Plane (Meyer and Van Kampen, 2002), for the control of all the simulator sub-systems including the generation of visual cues. However, the connection of X-Plane to the motion computer is deactivated and the signals from the external computer are sent directly to the motion computer, which drives the motion base.…”

Section: -Interface Of Aircraft Model With the 6 Dof Flight Simulatormentioning

confidence: 99%

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Immunity-based detection, identification, and evaluation of aircraft sub-system failures

Moncayo¹

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for their important comments and helpful suggestions which have been assimilated in this dissertation. My greatest thanks goes to my wife Paola, my parents Bernardo and Nancy, and the rest of our family, for their unconditional love and for supporting me during my whole academic and non-academic life. Many and special thanks to the volunteer pilots Dr. Alejandro Posada, Steven Mullins, Steven Hard, and Ondrej Karas for performing the tests in the flight simulator. They have helped me for achieving the results present in this thesis. Finally, the financial support provided by NASA Aviation Safety Program through a grant within the Integrated Resilient Aircraft Control project is gratefully acknowledged.

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“…Motion drive algorithms convert the motion of the aircraft as resulting from the dynamic model into motion of the platform such that the perception of the pilot is optimized within the physical limitations of the ground based simulator. The simulator has been interfaced [91] with an external computer (see Fig. 4.3) on which the aircraft model can be run within the Matlab ® /Simulink ® environment to drive the entire simulator system.…”

Section: )mentioning

confidence: 99%

Aircraft Abnormal Conditions Detection, Identification, and Evaluation Using Innate and Adaptive Immune Systems Interaction

Azzawi¹

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Abnormal flight conditions play a major role in aircraft accidents frequently causing loss of control. To ensure aircraft operation safety in all situations, intelligent system monitoring and adaptation must rely on accurately detecting the presence of abnormal conditions as soon as they take place, identifying their root cause(s), estimating their nature and severity, and predicting their impact on the flight envelope.Due to the complexity and multidimensionality of the aircraft system under abnormal conditions, these requirements are extremely difficult to satisfy using existing analytical and/or statistical approaches. Moreover, current methodologies have addressed only isolated classes of abnormal conditions and a reduced number of aircraft dynamic parameters within a limited region of the flight envelope.This research effort aims at developing an integrated and comprehensive framework for the aircraft abnormal conditions detection, identification, and evaluation based on the artificial immune systems paradigm, which has the capability to address the complexity and multidimensionality issues related to aircraft systems.Within the proposed framework, a novel algorithm was developed for the abnormal conditions detection problem and extended to the abnormal conditions identification and evaluation. The algorithm and its extensions were inspired from the functionality of the biological dendritic cells (an important part of the innate immune system) and their interaction with the different components of the adaptive immune system. Immunity-based methodologies for re-assessing the flight envelope at post-failure and predicting the impact of the abnormal conditions on the performance and handling qualities are also proposed and investigated in this study.The generality of the approach makes it applicable to any system. Data for artificial immune system development were collected from flight tests of a supersonic research aircraft within a motion-based flight simulator. The abnormal conditions considered in this work include locked actuators (stabilator, aileron, rudder, and throttle), structural damage of the wing, horizontal tail, and vertical tail, malfunctioning sensors, and reduced engine effectiveness. The results of applying the proposed approach to this wide range of abnormal conditions show its high capability in detecting the abnormal conditions with zero false alarms and very high detection rates, correctly identifying the failed subsystem and evaluating the type and severity of the failure. The results also reveal that the post-failure flight envelope can be reasonably predicted within this framework. . . DEDICATION . DEDICATION To my parents, Zeki and Anwar, my wife, Rawaa, and my children, Ahmed and Yisra. iii . . ACKNOWLEDGMENTS . ACKNOWLEDGMENTSIt is a great pleasure to take this opportunity to thank my advisor, Dr. Mario G. Perhinschi. Without his true mentoring, indefinite support, and tireless patience, this work would not have been possible. I would also like to express my sincere thanks to the me...

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Pilot-in-the-Loop Assessment of Neurally Augmented Dynamic Inversion Based Fault Tolerant Control Laws in a Motion-Based Flight Simulator

Cited by 6 publications

References 7 publications

A dendritic cell mechanism for detection, identification, and evaluation of aircraft failures

A dendritic cell mechanism for detection, identification, and evaluation of aircraft failures

Immunity-based detection, identification, and evaluation of aircraft sub-system failures

Aircraft Abnormal Conditions Detection, Identification, and Evaluation Using Innate and Adaptive Immune Systems Interaction

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