Parameter Estimation from Near Stall Flight Data using Conventional and Neural-based Methods

  • S. Saderla Department of Aerospace and Software Engineering, Gyeongsang National University
  • R. Dhayalan Department of Aerospace Engineering, Indian Institute of Space Science and Technology, Thiruvananthapuram
  • A.K. Ghosh Department of Aerospace Engineering, Indian Institute of Technology Kanpur
Keywords: Parameter estimation, Unmanned aerial vehicle, High angle of attack, Flight data, Conventional maximum likelihood

Abstract

The current research paper is an endeavour to estimate the parameters from near stall flight data of manned and unmanned research flight vehicles using conventional and neural based methods. For an aircraft undergoing stall, the aerodynamic model at these high angles of attack becomes non linear due to the influence of unsteady, transient and flow separation phenomena. In order to address these issues the Kirchhoff’s flow separation theory was used to incorporate the nonlinearity in the aerodynamic model in terms of flow separation point and stall characteristic parameters. The classical Maximum Likelihood (MLE) method and Neural Gauss-Newton (NGN) method have been employed to estimate the nonlinear parameters of two manned and one unmanned research aircrafts. The estimated static stall parameter and the break point, for the flight vehicles under consideration, were observed to be consistent from both the methods. Moreover the efficacy of the methods is also evident from the consistent estimates of post stall hysteresis time constant. It can also be inferred that the considered quasi steady model is able to adequately capture the drag and pitching moment coefficients in the post stall regime. The confidence in these estimates have been significantly enhanced with the observed lower values of Cramer-Rao bounds. Further the estimated nonlinear parameters were validated by performing a proof of match exercise for the considered flight vehicles. Interestingly the NGN method, which doesn’t involve solving equations of motion, was able to perform on a par with the MLE method.

Author Biographies

S. Saderla, Department of Aerospace and Software Engineering, Gyeongsang National University
Dr Subrahmanyam Saderla obtained his BTech (Aeronautical Engineering) from JNTU, Hyderabad in 2008, MTech and PhD (Aerospace Engineering) from IIT Kanpur, in 2010 and 2015, respectively. Presently, he is working as a postdoctoral fellow in the department of aerospace and software engineering at Gyeongsang National University, South Korea. He is mainly working in the areas of Design, flight tests and parameter estimation of unmanned aerial vehicles. His research interests also include real time system identification, high angle of attack aerodynamic modelling and dynamic wind tunnel testing as well as experimental flight dynamics.
R. Dhayalan, Department of Aerospace Engineering, Indian Institute of Space Science and Technology, Thiruvananthapuram
Dr Dhayalan R. has obtained his BTech (Aeronautical Engineering) from Anna University, in 2005. He has obtained his MTech and PhD (Aerospace Engineering) from IIT Kanpur, in 2007 and 2015, respectively. Presently, he is working as a visiting faculty in the Department of Aerospace Engineering at Indian Institute of Space Science and Technology, India. His areas of interest mainly include: Flight Vehicle system identification, flight dynamics, neural modelling and parameter estimation from flight tests of manned and unmanned aircrafts.
A.K. Ghosh, Department of Aerospace Engineering, Indian Institute of Technology Kanpur
Prof. A.K. Ghosh has obtained his BTech, MTech and PhD in Aerospace Engineering from IIT Kanpur. Presently he is a Professor in the Department of Aerospace Engineering at IIT Kanpur. His research interest includes: Flight mechanics, parameter estimation from flight images, neural modelling, design of air borne stores, aircraft bombs, artillery shells and rockets design of control law of guided missiles as well as design and analysis of lighter than air flight systems. His team is also working in the design and development of autonomous unmanned vehicles for tactical surveillance.
Published
2016-12-23
How to Cite
Saderla, S., Dhayalan, R., & Ghosh, A. (2016). Parameter Estimation from Near Stall Flight Data using Conventional and Neural-based Methods. Defence Science Journal, 67(1), 03-11. https://doi.org/10.14429/dsj.67.9995
Section
Aeronautical Systems