Sensor fusion and flight path reconstruction of the ACT/FHS rotorcraft
Tóm tắt
DLR’s active control technology/flying helicopter simulator (ACT/FHS) research rotorcraft supports research in a variety of fields. This paper presents the flight path reconstruction (FPR) of the ACT/FHS for post-flight data processing and its online sensor fusion during flight. Both are fundamental for system identification and flight control research. First, the ACT/FHS rotorcraft, its system architecture and the used sensor instrumentation are described. Then, the implemented unscented and extended Kalman filters are briefly explained and the applied kinematic and measurement models of the FPR are introduced. The wind estimation performance of the FPR is evaluated using simulation and flight test data accordingly. Subsequently, the online sensor fusion is motivated and its behaviour following a simulated differential GPS failure is analysed and explained.
Tài liệu tham khảo
Lantzsch, R., Greiser, S., Wolfram, J., Wartmann, J., Müllhäuser, M., Lüken, T., Döhler, H.-U., Peinecke, N.: ‘ALLFlight: a full scale pilot assistance test environment. In: American Helicopter society 68th annual forum. Forth Worth, Texas (2012)
AGARD: Rotorcraft system identification. AGARD Advisory Report AR280 (1991)
Jategaonkar, R.V.: Flight vehicle system identification. AIAA (2006)
Tischler, M.B., Remple, R.K.: Aircraft and Rotorcraft System Identification: Engineering Methods with Flight-Test Examples, 2nd edn. American Institute of Aeronautics and Astronautics Inc, Reston (2012)
Mulder, J.A., Chu, Q.P., Sridhar, J.K., Breeman, J.H., Laban, M.: Non-linear aircraft flight path reconstruction review and new advances. Progress Aerospace Sci 35, 673–726 (1999)
Teixeira, B.O., La Tôrres, P., Iscold, La Aguirre: Flight path reconstruction—a comparison of nonlinear Kalman filter and smoother algorithms. Aerosp Sci Technol 15, 60–71 (2011)
Fletcher, J.W.: Obtaining consistent models of helicopter flight-data measurement errors using kinematic-compatibility and state-reconstruction methods. In: American Helicopter Society 46th Annual Forum. Washington D.C, USA (1990)
Kaletka, J., Kurscheid, H., Butter, U.: FHS, the new research helicopter: ready for service. Aerosp Sci Technol 9, 456–467 (2005)
Gestwa, M.: DLR’s ACT/FHS research rotorcraft. In: 4 Research (Attachment to 4 Rotors), Ziese Verlag GmbH (Jan 2011)
Gelhaar, B., Oertel, H., Alvermann, K., Bodenstein, M., Gandert, R., Graeber, S., Schwaneck, H.-P.: FHS-experimental system for flying helicopter simulator put into operation. In: American Helicopter Society 59th Annual Forum. Phoenix (2003)
Dittmer, A.: Evaluation of flying helicopter simulator noseboom correction coefficients. CEAS Aeronaut. J. 5, 67–83 (2013)
Guichard, P.: ADS3000 system requirement document DRL03. Sextant Avionique (1997)
Julier, S.J., Uhlmann, J. K.: A new extension of the Kalman filter to nonlinear systems. In: Proceedings of aerosense: the 11th international symposium on aerospace/defence sensing, simulation and controls (1997)
Julier, S.J.: The scaled unscented transformation. Proc Am Control Conf, 6, (2002)
Hartikainen, J., Solin, A., Särkkä, S.: Optimal filtering with Kalman filters and smoothers— a manual for the matlab toolbox EKF/UKF 1.3. Aalto University School of Science, Finland, (2011)
Särkkä, S.: On Unscented kalman filtering for state estimation of continuous-time nonlinear systems. IEEE Trans Autom Control, 52(9) (2007)
Cipra, T., Romera, R.: Kalman filter with outliers and missing observations. Test 6, 379–395 (1997)