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Kalman-Filter-Based Adaptive Control: Flight Testing on General Aviation Aircraft

2017; American Institute of Aeronautics and Astronautics; Volume: 40; Issue: 9 Linguagem: Inglês

10.2514/1.g002693

1533-3884

Melvin Rafi, James E. Steck, John Watkins,

Risk and Safety Analysis

No AccessThe Kalman Filter and Its Aerospace ApplicationsKalman-Filter-Based Adaptive Control: Flight Testing on General Aviation AircraftMelvin Rafi, James E. Steck and John WatkinsMelvin RafiWichita State University, Wichita, Kansas 67260*Graduate Research Assistant, Aerospace Engineering. Student Member AIAA.Search for more papers by this author, James E. SteckWichita State University, Wichita, Kansas 67260†Professor, Aerospace Engineering. Associate Fellow AIAA.Search for more papers by this author and John WatkinsWichita State University, Wichita, Kansas 67260‡Professor and Chair, Electrical Engineering and Computer Science.Search for more papers by this authorPublished Online:7 Aug 2017https://doi.org/10.2514/1.G002693SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Yodo N. and Wang P., “Resilience Modeling and Quantification for Engineered Systems Using Bayesian Networks,” Journal of Mechanical Design, Vol. 138, No. 3, Jan. 2016, Paper 031404. doi:https://doi.org/10.1115/1.4032399 CrossrefGoogle Scholar[2] Yodo N. and Wang P., “Engineering Resilience Quantification and System Design Implications: A Literature Survey,” Journal of Mechanical Design, Vol. 138, No. 11, Sept. 2016, Paper 111408. doi:https://doi.org/10.1115/1.4034223 CrossrefGoogle Scholar[3] Kim B. and Calise A., “Nonlinear Flight Control Using Neural Networks,” Guidance, Navigation, and Control Conference and Exhibit, AIAA Paper 1994-3646-CP, Aug. 1994. doi:https://doi.org/10.2514/6.1994-3646 LinkGoogle Scholar[4] Rokhsaz K., Steck J. and Shue S., “Longitudinal Flight Control Decoupling Using Artificial Neural Networks,” 32nd Aerospace Sciences Meeting and Exhibit, AIAA Paper 1994-0274, Jan. 1994. doi:https://doi.org/10.2514/6.1994-274 LinkGoogle Scholar[5] Kim B. and Calise A., “Nonlinear Flight Control Using Neural Networks,” Journal of Guidance, Control, and Dynamics, Vol. 20, No. 1, 1997, pp. 26–33. doi:https://doi.org/10.2514/2.4029 JGCODS 0731-5090 LinkGoogle Scholar[6] Schumacher C., “Adaptive Flight Control Using Dynamic Inversion and Neural Networks,” Guidance, Navigation, and Control Conference and Exhibit, AIAA Paper 1999-4086, Aug. 1999. doi:https://doi.org/10.2514/6.1999-4086 LinkGoogle Scholar[7] Balakrishnan S. and Esteban S., “Nonlinear Flight Control System with Neural Networks,” Atmospheric Flight Mechanics Conference and Exhibit, AIAA Paper 2001-4257, Aug. 2001. doi:https://doi.org/10.2514/6.2001-4257 LinkGoogle Scholar[8] Steck J., Rokhsaz K. and Shue S.-P., “Linear and Neural Network Feedback for Flight Control Decoupling,” IEEE Control Systems Magazine, Vol. 16, No. 4, Aug. 1996, pp. 22–30. doi:https://doi.org/10.1109/37.526912 ISMAD7 0272-1708 CrossrefGoogle Scholar[9] Steck J. 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All rights reserved. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the ISSN 0731-5090 (print) or 1533-3884 (online) to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. TopicsAerodynamic PerformanceAerodynamicsAeronautical EngineeringAeronauticsAircrewAirspeedAviationAviation SafetyCivil AviationCommercial AviationFlight TestTurbulenceTurbulence Models KeywordsKalman FilterFlight TestingAircraftsModel Reference Adaptive ControlGeneral AviationAirspeedFlight Path AngleNeural NetworksFlying WiresTextron AviationAcknowledgmentThis material is based upon work supported by NASA under award number NNXO9AP20A. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NASA.PDF Received26 December 2016Accepted10 July 2017Published online7 August 2017