Temporal Prediction of Aircraft Loss-of-Control: A Dynamic Optimization Approach

Chaitanya Poolla¹, Abraham K. Ishihara²

¹Electrical and Computer Engineering, Carnegie Mellon University (SV), Moffett Field, CA, USA.
²Research Faculty, Electrical and Computer Engineering, Carnegie Mellon University (SV), Moffett Field, CA, USA.

DOI: 10.4236/ica.2015.64023                

Abstract

Loss of Control (LOC) is the primary factor responsible for the majority of fatal air accidents during past decade. LOC is characterized by the pilot’s inability to control the aircraft and is typically associated with unpredictable behavior, potentially leading to loss of the aircraft and life. In this work, the minimum time dynamic optimization problem to LOC is treated using Pontryagin’s Maximum Principle (PMP). The resulting two point boundary value problem is solved using stochastic shooting point methods via a differential evolution scheme (DE). The minimum time until LOC metric is computed for corresponding spatial control limits. Simulations are performed using a linearized longitudinal aircraft model to illustrate the concept.

Keywords

Pilot Assistance, Loss of Control, Aircrafts, Dynamic Optimization, Temporal Prediction, Pontryagin Maximum Principle, Differential Evolution, Stochastic Shooting Point Methods

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Worldwide Operations (2012) Statistical Summary of Commercial Jet Airplane Accidents. Technical Report, Boeing.
[2]	Authority, Civil Aviation (2013) Global Fatal Accident Review 2002-2011. Tso.
[3]	Jacobson, S. and Edwards, C.A. (2010) Aircraft Loss of Control Study. NASA Internal Report.
[4]	Kwatny, H.G., et al. (2012) Nonlinear Analysis of Aircraft Loss of Control. Journal of Guidance, Control, and Dynamics, 36, 149-162.
[5]	Michales, A.S. (2012) Contributing Factors among Fatal Loss of Control Accidents in Multiengine Turbine Aircraft.
[6]	Wilborn, J.E. and Foster, J.V. (2004) Defining Commercial Transport Loss-of-Control: A Quantitative Approach. AIAA Atmospheric Flight Mechanics Conference and Exhibit.
[7]	Randall, L. (2012) Brooks. LOC-I Training Foundations and Solutions. Technical Report, Boeing.
[8]	Krishnakumar, K., et al. (2014) Initial Evaluations of LoC Prediction Algorithms using the NASA Vertical Motion Simulator. SciTech 2014.
[9]	Barlow, V., Stepanyan, J. and Kalmanje, K. (2012) Estimating Loss-of-Control: A Data Based Predictive Approach.
[10]	Pontryagin, L.S., et al. (1962) The Mathematical Theory of Optimal Processes (International Series of Monographs in Pure and Applied Mathematics. Interscience, New York.
[11]	Qin, A.K., Huang, V.L. and Suganthan, P.N. (2009) Differential Evolution Algorithm with Strategy Adaptation for Global Numerical Optimization. IEEE Transactions on Evolutionary Computation, 13, 398-417.
[12]	Okdem, S. (2004) A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm. Turkish Journal of Electrical Engineering and Computer Sciences, 12.
[13]	Bersini, H., et al. (1996) Results of the First International Contest on Evolutionary Optimisation (1st ICEO). Proceedings of IEEE International Conference on Evolutionary Computation, 20-22 May 1996, 611-615.