No CrossRef data available.
Article contents
A comparative study of parameter estimation techniques applied to jettisoned external stores
Published online by Cambridge University Press: 27 January 2016
Abstract
The present work evaluates the performance of different optimisation techniques on a parameter identification problem of aeronautical interest. In particular, the focus is on the classical Least Square (LS) and Maximum Likelihood (ML) methods and on the CMAES (Covariance Matrix Adaptation Evolution Strategy), DE (Differential Evolution), GA (Genetic Algorithm) and PSO (Particle Swarm Optimisation) Meta-Heuristic methods. The test problem is the reconstruction from flight test data of the aerodynamic parameters of an external store jettisoned from a helicopter. Different initial conditions and the presence of measurement noise are considered. This case is representative of a class of problems of difficult solution because of nonlinearity, ill-conditioning, multidimensionality, non separability, and fitness function dispersion. Only reference algorithm implementations found in literature are used. The performance of each algorithm are defined in terms of fitness function value, sum of absolute errors of the estimated coefficients, computational time and number of function evaluations. The results show the efficiency of CMAES in finding the best estimates with the least computational cost. Moreover, tests reveal that traditional methods depend heavily on problem characteristics and loose accuracy at the increase of the number of unknowns.
- Type
- Research Article
- Information
- Copyright
- Copyright © Royal Aeronautical Society 2014
References
1.
Klein, V.
Estimation of aircraft aerodynamic parameters from flight data, Progress in Aerospace Sciences, 26, pp 1–11, 1989.Google Scholar
2.
Maine, R.E. and Iliff, K.W. Identification of Dynamic Systems – Applications to Aircraft Part 1: The Output Error Approach. AGARD Flight Test Techniques Series – Volume 3, AGARD – NATO Advisosy Group for Aerospace Research and Development, April 1986.Google Scholar
3.
Hamel, P.G. and Jategaonkar, R.
Evolution of flight vehicle system identification, J Aircr, 1996, 33, (1), pp 9–28.Google Scholar
4.
Mulder, J.A., Sridhar, J.K. and Breeman, J.H. Identification of Dynamic Systems – Applications to Aircraft Part 2: Nonlinear Analysis and Manoeuvre Design. AGARD Flight Test Techniques Series – Volume 3, AGARD – NATO Advisosy Group for Aerospace Research and Development, April 1986.Google Scholar
5.
Bobashev, S.V., Mende, N.P., Popov, P.A.
Sakharov, V.A., Berdnikov Viktorov, V.A., Oseeva, S.I. and Sadchikov, G.D.
Algorithm for determining the aerodynamic characteristics of a freely flying object from discrete data of ballistic experiment, Part 1, Technical Physics, 2009, 54, (4), pp 504–510.Google Scholar
6.
Bobashev, S.V., Mende, N.P., Popov, P.A., Sakharov, V.A., Berdnikov, V.A., Viktorov, S.I.
Oseeva, , and Sadchikov, G.D.
Algorithm for determining the aerodynamic characteristics of a freely flying object from discrete data of ballistic experiment, Part 2, Technical Physics, 2009, 54, (4), pp 511–519.Google Scholar
7.
Shi, Y., Qian, W., Wang, Q. and He, K. Aerodynamic parameter estimation using genetic algorithms. In IEEE Congress on Evolutionary Computation, 2006.Google Scholar
8.
Goes, L.C.S. and Viana, F.A.C. Life cycle and gradient based optimization applied to estimation of aircraft aerodynamic derivatives by the output error method. In 2nd International Conference on Engineering Optimization, 2010.Google Scholar
9.
Yang, X.S.
Nature-Inspired Meta-heuristic Algorithms:
2nd Ed, Luniver Press, Frome, UK, 2010.Google Scholar
10.
Goldberg, D.E.
Genetic algorithm in search, optimisation and machine learning, AddisonWesley, Reading, MA, 1989.Google Scholar
11.
Nelles, O.
Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models, Springer, 2001.Google Scholar
12.
Iliff, K.W., Maine, R.E. and Montgomery, T.D. Important factors in the maximum likelihood analysis of fight test maneuenvers. Technical Paper 1459, NASA – National Aeronautics and Space Administration, April 1979.Google Scholar
13.
Morelli, E.A. and Klein, V. Accuracy of aerodynamic model parameters estimated from flight test data. J Guidance, Control and Dynamics, 1997.Google Scholar
14.
Holland, J.H.
Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, USA, 1975.Google Scholar
15.
Price, K. and Storn, R.M. Differential evolution – a simple and effcient heuristic for global optimization over continuous spaces, J Global Optimization, 1997.Google Scholar
16.
Price, K.
Storn, R.A. and. Lampinen, J.A.
Differential Evolution: A Practical Approach to Global Optimization, Springer, 2005.Google Scholar
17.
Hansen, N.
The CMA evolution strategy: a comparing review. In Lozano, J.A.
Lar- ranaga, P.
Inza, I. and Bengoetxea, E. (Eds), Towards a new evolutionary computation. Advances on estimation of distribution algorithms, pp 75–102. Springer, 2006.Google Scholar
19.
Kennedy, J., Eberhart, R.C. and Shi, Y.
Swarm intelligence, Morgan Kaufmann Publishers, San Francisco, USA, 2001.Google Scholar
20.
Baranowski, L.
Equations of motion of a spin-stabilized projectile for flight stability testing, J Theoretical and Applied Mechanics, 2013, 51, (1), pp 235–246.Google Scholar
21.
Fantinutto, G., Guglieri, G. and Quagliotti, F.
Flight control system design and optimisation with a genetic algorithm, Aerospace Science and Technology, 2005, 9, (1), pp 73–80.Google Scholar
22.
Jorgensen, L.H. Prediction of static and aerodynamic characteristics for slender bodies alone and with lifting surfaces to very high angles of attack. Technical Report TR R-474, NASA – National Aeronautics and Space Administration.Google Scholar
23.
Jorgensen, L.H. Prediction of static and aerodynamic characteristics for space-shuttle-like and other bodies at angles of attack from 0° to 180°. Technical Report TN D-6996, NASA- National Aeronautics and Space Administration.Google Scholar