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Parameter Estimation Techniques for Helicopter Dynamic Modelling.

By Ping Li, Ian Postlethwaite and Matthew C. Turner


This paper investigates the problem of estimating parameters in a state-space helicopter dynamical model for helicopter controller design and evaluation. Two methods, PEM (the prediction error method) and RBPF (the Rao-Blackwellized particle filter-based method), are presented in the paper. Computer simulations are carried out to assess and compare the performance of the two methods with different assumptions on the initial conditions of the parameters to be estimated. Based on this assessment, an estimation scheme that combines the two methods is proposed. The proposed scheme is\ud then applied to real data from EH101 helicopter flight tests and a vertical model including rotor coning dynamics is identified

Topics: Parameter estimation, State-space model, Rao-Blackwellized particle filter, Prediction error method, Helicopter dynamic modelling
Publisher: Institute of Electrical and Electronics Engineers (IEEE).
Year: 2007
DOI identifier: 10.1109/ACC.2007.4282197
OAI identifier:

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  3. (1979). Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems doi
  4. (2001). Combined parameter and state estimation in simulation-based filtering. doi
  5. (2004). Estimation of parameters in a linear state space model using a Rao-Blackwellised particle filter. doi
  6. (1996). Helicopter Flight Dynamics. doi
  7. (2005). Marginalized Particle Filters for Mixed Linear/Nonlinear State-space Models. doi
  8. (1996). Monte Carlo filter and smoother for nonGaussian nonlinear state space models. doi
  9. (2001). Monte Carlo filters for non-linear state estimation, doi
  10. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. doi
  11. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering, Statistics and computing,
  12. (1979). Optimal Filtering. doi
  13. (2003). Parameter estimation of railway vehicle dynamic model using Rao-Blackwellised particle filter. doi
  14. (2003). Particle filters for system identification of state-space models linear in either parameters or states.
  15. (2004). Particle methods for change detection, system identification and control. doi
  16. (1982). Stochastic Models, Estimation, doi
  17. (2007). Subspace-based system identification for helicopter dynamic modelling. doi
  18. (1999). System Identification—Theory for the User. PrenticeHall, Upper Saddle River,
  19. (1987). The role of modelling and flight testing in rotorcraft parameter identification.
  20. (1983). Theory and Practice of Recursive Identification. doi

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