Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method
This paper presents the estimation of longitudinal aerodynamic parameters by using Genetic Algorithm (GA) optimized method from simulated and real flight data of ATTAS aircraft. The simulated flight data is deliberately contaminated with 5%, 10%, and 15% of random noise for creating flight data, which bears similarity to real flight data. The proposed methodology utilizes the general notion of output error method, i.e., minimizing the response error between the measured response and estimated response, and the genetic algorithm as the optimization technique for an iterative update of the parameter vector. The longitudinal parameters are estimated by using the proposed method from both simulated data (without and with random noise) and real flight data. The parameter estimates obtained by using the proposed method is compared with the estimates from the Maximum-Likelihood method and data-driven methods viz. Delta method and GPR –Delta method for assessing the efficacy of the methodology. The statistical analysis of the parameter estimates has further cemented the confidence in the estimates obtained by using the proposed method.
Ajoy Kanti Ghosh,
Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method, American Journal of Engineering and Technology Management.
Vol. 4, No. 2,
2019, pp. 34-46.
JATEGAONKAR, R. V., “Flight Vehicle System Identification: A Time Domain Methodology,” AIAA Education Series, AIAA, Reston, VA, 2006.
MEHRA, RAMAN, K., "Maximum likelihood identification of aircraft parameters." Joint Automatic Control Conference. No. 8, 1970.
HAMEL, P. G., AND JATEGAONKAR, R. V., “The Evolution of Flight Vehicle System Identification," AGARD, DLR Germany, 8··10, May 1995.
ROSKAM, J., “Methods for Estimating Stability and Control Derivatives for conventional Subsonic Airplanes,” Roskam Aviation and Engineering Corporation, 1973
ILIFF, K. W., “Parameter Estimation for Flight Vehicle,” Journal of Guidance, Control and Dynamics, Vol. 12, No. 5, 1989, pp 609-622
HAMEL, P. G., “Aircraft Parameter Identification Methods and their Applications Survey and Future Aspect,” AGARD, 13-104, Nov. 1979, Paper 1.
KLEIN, V., AND MORELLI, E. A., “Aircraft system identification—Theory and practice,” AIAA Education Series, Reston, VA, 2006.
RAOL R., JITENDRA, AND SINGH, J., “Flight Mechanics Modeling and Analysis,” CRC Press, Taylor and Francis Group, 2009.
MAINE, K. E., AND ILIFF, K. W., “Application of Parameter Estimation to Aircraft Stability and Control: The Output Error Approach,” NASA RP 1168, Jan. 1986.
MILLIKEN, W. F. JR., "Progress in Dynamic Stability and Control Research," Journal of the Aeronautical Sciences, Vol. 14, No. 9, 1947, pp. 493-519.
GREENBERG, H., “A Survey of Methods for Determining Stability Parameters of an Airplane from Dynamic Flight Measurements," NACA TN-2340, April 1951.
SHINBROT, M., “A Least square Curve Fitting Method with Applications to the Calculation of Stability Coefficients from Transient Response Data," NACA TN 2341, April 1951.
MAINE, R. E. AND ILIFF, K. W. "Formulation and Implementation of a Practical Algorithm for Parameter Estimation with Process and Measurement Noise," Society for Industrial and Applied Mathematics, Journal of Applied Mathematics, Vol. 41, Dec. 1981, pp. 558-579.
BALAKRISHNAN, A. V., “Stochastic System Identification Techniques,” in Stochastic Optimization and Control, edited by H. F. Karreman, John Wiley and Sons, London, 1968.
RAOL, J. R., and JATEGAONKAR, R. V., "Aircraft Parameter Estimation using Recurrent Neural Networks- A Critical Appraisal," A1AA Paper 95-3004, Aug. 1995.
RAISINGHANI, S. C., GHOSH, A. K., and KALRA, P. K., "Two New Techniques for Aircraft Parameter Estimation Using Neural Networks," The Aeronautical Journal, Vol. 102, No. 1011, Jan. 1998, pp. 25-29.
GHOSH, A. K., RAISINGHANI, S. C., and KHUBCHANDANI, S., “Estimation of Aircraft Lateral-Directional Parameters using Neural Networks," Journal of Aircraft, Vol. 35, No. 6, Nov.-Dec. 1998, pp. 876-881.
SINGH, S., "Estimation of Aircraft Parameters from Flight Data using Neural Network-Based Method," Ph. D. Thesis, Aerospace Engineering Dept., IIT Kanpur, April 2007.
PEYADA, N. K., and GHOSH, A. K., "Aircraft Parameter Estimation using New Filtering Technique based on Neural Network and Gauss-Newton Method," Aeronautical Journal, UK, Vol. 113, No. 1142, April 2009.
PEYADA, N. K., and GHOSH, A. K., "Parameter Estimation from Real Flight Data using Neural Network based Method," INCPAA- 2008, Mathematical Problems in Engineering, Aerospace and Sciences, University of Genoa, Italy, June 25-27, 2008.
KUMAR, R., and GHOSH, A. K., ''Nonlinear Longitudinal Aerodynamic Modeling using Quasi-steady Stall Model and Neural Gauss-Newton Method," Journal of Aircraft, AIAA, USA, Vol. 48, No. 5, Sept.-Oct. 2011, pp. 1809-1812.
KUMAR, R., and GHOSH, A. K., ‘‘Nonlinear Aerodynamic Modeling of Hansa-3 Aircraft using Neural Gauss-Newton Method," Journal of Aerospace Sciences and Technologies, AeSI, India, Vol. 63, No. 3, August 2011, pp. 194-204.
NELLES, O., “ Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models, Springer-Vwrlag Berlin Heidelberg, 2001.
KUMAR, A., and GHOSH, A. K., “ANFIS-Delta Method for Aerodynamic Parameter Estimation using Flight Data,” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2018
KUMAR, A., and GHOSH, A. K., “Decision Tree and Random Forest Methods Based Novel Unsteady Aerodynamics Modeling Using Flight Data," Journal of Aircraft. 2018 Sep: 1-7.
KUMAR, A., and GHOSH, A. K, “A GPR Based Novel Approach for Aerodynamic Parameter Estimation from Flight Data," International Review of Aerospace Engineering, 2018
KUMAR, A., and GHOSH, A. K., “GPR based Novel Approach for Nonlinear Aerodynamic Modeling from Flight Data," The Aeronautical Journal, 2018, pp 1-14.
SIMON, D., “Evolutionary optimization algorithms,” John Wiley & Sons, 2013.
MITCHELL, M., “An Introduction to genetic algorithms,” MIT Press, 1998.
HOLLAND, J. H., “Genetic algorithms,” Scientific American, 267 (1), 66-73, 1992.
HOLLAND, J. H., and GOLDBERG, D., “Genetic algorithms in search, optimization and machine learning,” Massachusetts: Addison-Wesley, 1989.
NEJATI, V., and MATSUUCHI, K., “Aerodynamics design and genetic algorithms for optimization of airship bodies,” JSME International Journal Series B Fluids and Thermal Engineering., 46 (4), 610-617, 2003.
ALLAIRE, F. C., TARBOUCHI, M., LABONTÉ, G., and FUSINA, G., “FPGA implementation of genetic algorithm for UAV real-time path planning in Unmanned Aircraft Systems.” Springer, Dordrecht, 495-510, 2008.
HE, Y., QU, Q., and AGARWAL, R. K., “Shape Optimization of an Aerofoil in Ground effect for Application to WIG Craft.” 53rd AIAA Aerospace Sciences Meeting, 0758, 2015.
LEE, J., HONG, C. H., KIM, B. S., PARK, K., and AHN, J., “Optimization of wings in ground effect using multi-objective genetic algorithm” 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 1422, January 2010.
LEE, S. H., and LEE, J., “Optimization of three-dimensional wings in ground effect using multi-objective genetic algorithm” Journal of Aircraft., 48 (5), 1633-1645, 2011.
LI, S., ZHOU, D., ZHANG, Y., and QU, Q., “Aerodynamic investigation of RAE2822 aerofoil in ground effect” Procedia Engineering., 126, 174-178, 2015.
MAGRINI, A., and BENINI, E., “Aerodynamic Optimization of a Morphing Leading Edge Aerofoil with a Constant Arc Length Parameterization” Journal of Aerospace Engineering. 31 (2), 04017093, 2017.
KALE, S., JOSHI, P., and PANT, R., "A generic methodology for determination of drag coefficient of an aerostat envelope using CFD.” AIAA 5th ATIO and 16th Lighter-Than-Air Sys Tech. And Balloon Systems Conference, 7442, September 2005.
KUMAR, A., “Machine Learning Methods for Aerodynamic Modelling and Parameter Estimation," Ph. D. Thesis, Aerospace Engineering Dept., IIT Kanpur, Jan 2018.
NAPOLITANO R. M., “Aircraft Dynamics -From Modelling to Simulation,” 1st Edition, Wiley, November 2011.