Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-19T00:18:05.539Z Has data issue: false hasContentIssue false
  • English
  • Français

Flight control strategy for jet transport in severe clear-air turbulence based on flight data mining

Published online by Cambridge University Press:  27 July 2022

R.C. Chang Open the ORCID record for R.C. Chang [Opens in a new window] ,
Y. Wang Open the ORCID record for Y. Wang [Opens in a new window]  and
W. Jiang Open the ORCID record for W. Jiang [Opens in a new window]
R.C. Chang
Affiliation:
Flight College, Changzhou Institute of Technology, Changzhou, Jiangsu213032, People’s Republic of China
Y. Wang*
Affiliation:
School of Aeronautics, Chongqing Jiaotong University, Chongqing, 400074, People’s Republic of China Green Aviation Technology Research Institute, Chongqing401135, People’s Republic of China
W. Jiang
Affiliation:
Flight College, Changzhou Institute of Technology, Changzhou, Jiangsu213032, People’s Republic of China Jiangsu Nanfang Bearing Co., Ltd, Changzhou, Jiangsu213163, People’s Republic of China
*
*Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a new concept of the control strategy in prevention program for the airlines to prevent the injuries of passengers and crew members for transport aircraft. A twin-jet transport aircraft encountered severe clear-air turbulence at transonic flight in descending phase is the study case of the present paper. The nonlinear and unsteady flight controllability models based on flight data mining and the fuzzy-logic modeling of artificial intelligence technique, are utilised to support this new concept. The proposed flight controllability models with the function of nonlinear dynamic inversion are employed to provide flight control strategy through flight simulations of dynamic inversion process; it is an innovation in mathematical modelling of aerospace engineering. Since the sudden plunging motion with the abrupt change in attitude and gravitational acceleration (i.e. the normal load factor) to affect the flight safety the most, hazard mitigation is a great concern for the aviation community. The present study is initiated to examine possible mitigation concepts of accident prevention to provide a training course for loss of control in-flight program to the airlines.

Keywords

Flight data miningPlunging motionFlight Data Recorder (FDR)Nonlinear dynamic inversionHazardous mitigation
Type
Research Article
Information
The Aeronautical Journal , Volume 126 , Issue 1306 , December 2022 , pp. 2083 - 2110
Check for updates
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Chang, R.C., Ye, C.-E., Lan, C.E. and Guan, M. Flying qualities for a twin-jet transport in severe atmospheric turbulence. J. Aircr., 2009, 46, (5), pp 1673–1680.Google Scholar
IATA: Cabin Crew Turbulence related Injuries. Safety trend evaluation. Analy. Data Exchange Syst., 2005, Special Issue 2, pp 19–22.Google Scholar
Hamilton, D.W. and Proctor, F.H. An aircraft encounter with turbulence in the vicinity of a thunderstorm. 21st AIAA Applied Aerodynamics Conference 2003, June 26, 2003? AIAA International, Orlando, FL, United States, 2003, pp 1–9.Google Scholar
Sheu, D. and Lan, C.-T. Estimation of turbulent vertical velocity from nonlinear simulations of aircraft response. J. Aircr., 2011, 48, (2), pp 645–651.Google Scholar
Chang, R.C. Fuzzy logic-based aerodynamic modeling with continuous differentiability. Math. Probl. Eng., 2013, Article ID 609769, 2013, p 14.Google Scholar
Han, J., Kamber, M. and Pei, J. Data Mining: Concepts and Techniques, 3rd ed., USA: Morgan Kaufmann Publishers, 2011.Google Scholar
Wolberg, G. and Alfy, I. An energy-minimization framework for monotonic cubic spline interpolation, J. Comput. Appl. Math., 2002, 143, (2), pp 145–188.Google Scholar
Lan, C.E., Wu, K. and Yu, J. Flight characteristics analysis based on QAR data of a Jet transport during landing at a high-altitude airport. Chinese J. Aeronaut., 2012, 25, (1), pp 13–24.Google Scholar
Dadios, E.P. Fuzzy Logic – Emerging Technologies and Applications, London: IN-TECH, 2012.Google Scholar
Roskam, J. Airplane Flight Dynamics and Automatic Flight Controls, Lawrence, Kansas: DAR Corporation, 2018.Google Scholar
Sugar-Gabor, O. A general numerical unsteady non-linear lifting line model for engineering aerodynamics studies. Aeronaut. J., 2018, 122, (1254), pp 1199–1228.Google Scholar
Brandon, J.M., Lan, C.E., Li, J. and Wang, Z. Estimation of unsteady aerodynamic models from flight test data. nAIAA Atmospheric Flight Mechanics Conference and Exhibit, AIAA, Montreal, Canada, 2001, pp 1–8.Google Scholar
Richardson, T.S. and Lowenberg, M.H. A continuation design framework for nonlinear flight control problems. Aeronaut. J., 2006, 110, (1104), pp 85–96.Google Scholar
Rodić, A.D. and Stojković, I.R. Dynamic inversion control of quadrotor with complementary Fuzzy logic compensator. 11th Symposium on Neural Network Applications in Electrical Engineering, 20–22 September 2012, Belgrade, Serbia, 2012, pp 53–58.Google Scholar
Alam, M. and Celikovsky, S. On the internal stability of non-linear dynamic inversion: application to flight control. IET Control Theory Appl., 2017, 11, (12), pp 1849–1861.Google Scholar
Lan, C.E. and Chang, R.C. Unsteady aerodynamic effects in landing operation of transport aircraft and controllability with fuzzy-logic dynamic inversion, Aerosp. Sci. Technol., 2018, 78, pp 354–363.Google Scholar
Karasan, A., Ilbahar, E., Cebi, S. and Kahraman, C. A new risk assessment approach: safety and critical effect analysis (SCEA) and its extension with Pythagorean fuzzy sets. Saf. Sci., 2018, 108, pp 173–187.Google Scholar
Dang, Q.V., Vermeiren, L., Dequidt, A. and Dambrine, M. Robust stabilizing controller design for Takagi-Sugeno fuzzy descriptor systems under state constraints and actuator saturation. Fuzzy Sets Syst., 2017, 329, pp 77–90.Google Scholar
Shi, K., Wang, B., Yang, L., Jian, S. and Bi, J. Takagi–Sugeno fuzzy generalized predictive control for a class of nonlinear systems. Nonlinear Dyn., 2017, 89, (1), pp 169–177.Google Scholar