Abstract
State estimation methods are the popular means of validating aerodynamic characteristics on maneuvering Aircraft. This work deals with adaptation of familiar filtering methods for Aircraft landing maneuvers, to estimate the aircraft touchdown states. The mathematical model for two-point landings (main wheel in contact with the ground and nose wheel airborne) consists of nonlinear flight mechanics equations representing Aircraft longitudinal dynamics. A nonlinear 6 DOF pilot in loop simulation model is used for the measurement of data generation that was mixed with process and measurement noises. These values are used for posterior state correction in the implementation of Kalman filter. With the state values just before the initiation of flare as initial conditions, filters such as Upper Diagonal factorized form of Adaptive Extended Kalman Filter (UDAEKF) and Unscented Kalman Filter (UKF) is implemented in Matlab environment. The estimated states and measured data are compared using performance metrics for vertical acceleration (Nz) which brings out the possibility of over quantification (3.5%) and under quantification (11.3%) at onset of touchdown having an impact on landing loads. As observed, the performance of UKF is two and half times faster than UDAEKF through superior state propagation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lacagnina M (2004) Flight safety digest: avoiding hard landings. Tech Publ 23(8):249–250. Flight Safety Foundation
Airplane strength and rigidity: landing and ground loads, May 1960
Airplane strength and rigidity: ground loads for navy acquired airplanes, May 1987
Sartor P, Schmidt RK, Becker W, Worden K, Bond DA, Staszewski WJ (2010) Conceptual design of a hard landing indication system using flight parameter sensor simulation model. In: 27th international congress of the aeronautical science
Crassidis JL, Junkins JL (2012) Optimal estimation of dynamic systems, 2nd edn. CRC Press
Julier SJ, Uhlmann JK (1995) A new extension of Kalman filter to nonlinear systems. In: Proceedings of the American control conference, pp 1628–1632
Chen G, Chui CK (1991) A modified adaptive Kalman filter for real-time applications. IEEE Trans Aerosp Electron Syst 27(1):149–154
Mulder JA, Chu QP, Sridhar JK, Breeman JH, Laban M (2010) Non-linear aircraft flight path reconstruction review and new advances. Prog Aerosp Sci 35:673–726
Chowdhary G, Jategaonkar R (2010) Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter. Aerosp Sci Technol 14:106–117
Napolitano MR Aircraft Dynamics: from modeling to simulation. Wiley
Jategaonkar RV, Thielecke F (2000) Aircraft parameter estimation—a tool for development of aerodynamic databases. SaÅdhanaÅ 25(Part 2):119–135
Bierman GJ (1977) Factorization method for discrete sequential estimation. Academic Press, New York
Jetto L, Longhi S, Vitali D (1999) Localization of a wheeled mobile robot by sensor data fusion based on fuzzy logic adapted Kalman filter. Control Engg Pract 4:763–771
Suresh PS, Niranjan SK, Shankar K (2018) State estimation on landing maneuver on High performance Aircraft. J Inst Eng (India): Ser C, Aerosp Mech Eng. Springer, s40032-017-0428-0
Wan EA, van der Merwe R (2006) The unscented Kalman filter for nonlinear estimation. Center for Spoken Language and Understanding, OGI School of Science and Engineering. http://cslu.cse.ogi.edu/nsel/ukf/
Inspection process following high load events, Technical Report 05-01, Aerospace Industry Association, Sept 2012
Acknowledgements
The authors from Aeronautical Development Agency acknowledge “The Programme Director” for the facility provided to publish this work. Special thanks to Technology Director (Airframe) and Group Director (Loads, Airframe) for their technical support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Suresh, P.S., Sura, N.K., Shankar, K. (2020). State Estimation Using Filtering Methods Applied for Aircraft Landing Maneuver. In: Singh, B., Roy, A., Maiti, D. (eds) Recent Advances in Theoretical, Applied, Computational and Experimental Mechanics. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-1189-9_27
Download citation
DOI: https://doi.org/10.1007/978-981-15-1189-9_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1188-2
Online ISBN: 978-981-15-1189-9
eBook Packages: EngineeringEngineering (R0)