Parameter estimation of a single turbo-prop aircraft dynamic model

단발 터어보프롭 항공기 동적 모델의 파라메터추정

  • Lee, Hwan (Dept.of Aerospace Engineering, Chosun University) ;
  • Lee, Sang-Kee (Dept.of Aerospace Engineering, Chosun University)
  • 이환 (조선대학교 항공우주공학과) ;
  • 이상기 (조선대학교 우주항공공학과)
  • Published : 1998.02.01

Abstract

The modified maximum likelihood estimation method is used to estimate the nondimensional aerodynamic derivatives of a single turbo-prop aircraft at a specified flight condition for the best deduction of the dynamic characteristics. In wind axes the six degree of freedom equations are algebraically linearized so that the linear state equation contains aerodynamic derivatives in a state-space form and is used in the maximum likelihood method. The simulated data added with the measurement noise is used as a flight test data which is necessary to the estimation of nondimensional aerodynamic derivatives. It is obtained by implementing the 6-DOF nonlinear flight simulation. In the flight simulation, the effects of several control input types, control deflection amplitudes, and the turbulence intensities on the statistical convergence criteria are also examined and quantitative analysis of the results is discussed.

Keywords

References

  1. AIAA Journal of Guidance, Control and Dynamics v.12 no.5 Parameter estimation for flight vehicles K. W.Iliff
  2. AGARD-AG-300 v.3 AGARD flight test techniques series volume 3 on identification of dynamic system-applications to aircraft Part 1: The output error approach R. E. Maine;K. W. Iliff
  3. Journal of Guidance, Control and Dynamics v.16 no.1 Digital simulation of atmospheric turbulence for dryden and von karman models T. R. Beal
  4. Aircraft control and simulation B. L. Stevens;F. L. Lewis
  5. Airplane flight dynamics and automatic flight controls. Part Ⅰ·Ⅱ J. Roskam
  6. Dynamics of flight stability and control B. Etkin
  7. SIAM J. Appl. Math. v.41 no.3 Formulation and implementation of a practical algorithm for parameter estimation with process and measurement noise R. E. Maine;K. W. Iliff
  8. AGARD-AG-300 v.2 AGARD flight test techniques series volume 2 on identification of dynamic system R. E. Maine;K. W. Iliff
  9. NASA Technical Paper 1690 Programmer's manual for MMLE3, a general fortran program for maximum likelihood parameter estimation R. E. Maine
  10. DFVLR-FB 87-20 Maximun likelihood estimation of parameters in linear systems with process and measurement noise R. Jategaonkar;E. Plaetschke
  11. Applied optimal control and estimation F. L. Lewis
  12. Kalman filtering M. S. Grewal;A. P. Andrews