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Estimation of the aerodynamical parameters of an experimental airship

Estimation of the aerodynamical parameters of an experimental airship. Intelligent Robots and Systems IROS 2005 Workshop on Robot Vision for Space Applications. Diego Patino, Leonardo Solaque, Simon Lacroix, Alain Gauthier LAAS – CNRS. Group RIA. Francia.

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Estimation of the aerodynamical parameters of an experimental airship

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  1. Estimation of the aerodynamical parameters of an experimental airship Intelligent Robots and Systems IROS 2005 Workshop on Robot Vision for Space Applications Diego Patino, Leonardo Solaque, Simon Lacroix, Alain Gauthier LAAS – CNRS. Group RIA. Francia. Universidad de los Andes. Depto de Eléctrica. Depto de Matemáticas. Colombia. 2005

  2. Index • Introduction • Dynamical Model • Brief Description of the technique • Results • Concluding Remarks Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  3. Introduction LAAS - CNRS Universidad de los Andes Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  4. Introduction European Project Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  5. Introduction • We want to have a dynamical model in order to design proper control laws. • We have the following measures: • Position in the 3D Space • Velocity in the 3D Space • Angles Roll, Pitch and Yaw • What can be used??? Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  6. Introduction Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  7. Dynamical model • Three reference frames: • R0: Global Frame. • Rd: Local frame (C) • Ra: Aeronautical frame (C) Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  8. : Translation and rotation velocities : Positions and angles in the global frame. : Matrix composed of masses and momentums. :Coriollis force and Centrifugal force : Archimedes force and the body weight. : Propulsion force vector : Aerodynamical vector Dynamical model Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  9. : Matrix of jointed masses : Phenomena of “translation-rotation” and “rotation-rotation” by the Bryson’s theory. : Stationary effort and moments in C of the dirigible. Dynamical model Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  10. Dynamical model To relate the measures with the state variables, we use the matrix J1 and J2: Most of the aerodynamical parameters are found in Tsta: Parameters to identify: 36!!!! Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  11. Brief description of the Kalman filter Consider the non-linear system: The extended Kalman Filter is an usual algorithm: Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  12. Brief description of the Kalman filter To Compute sigma points: Unscented Kalman Filter (UKF): Propagation Probability density characterized by the mean and teh covariance PXY: Cross-covariance matrix PYY: Measure covariance matrix Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  13. Results Test I: Model of Simulation AS500 Electronic compass signals Inputs GPS Signals Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  14. Results Test I: Model of Simulation AS500 Parameters: ClC3 and ClC1 EKF UKF Parameter CN2 Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  15. Results Test I: Model of Simulation AS500 Parameters: a66, a46, a44 Parameters: CN1, CnC1,CnC3 Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  16. Results Comparison between Real, EKF and UKF Test I: Model of Simulation AS500 Parameters: CnC1, x2m2, x2m1 and xm2 Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  17. º Test II: Real Signals KARMA Compass Signal T =0.08 seg. Inputs GPS Signals T = 1 seg. Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  18. Results Test II: Real Signals KARMA Parameters: CmC3, CnC3, CmC2, CT3, a33, CL2 Parameters: a11, a22, a33, CnC3 Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  19. Results Test II: Real Signals KARMA Parameters: CN1, CN2, CN3 Parameters for the real blimp Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  20. Results Test II: Real Signals KARMA Comparison between model and real data Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

  21. Concluding Remarks • We have show two different techniques for parameter estimation based on Kalman filtering. • The UKF presents a better behavior for the identification task than the EKF. • Those techniques can be used in others applications where the parameter estimation is not an easy task. • The noise has to be consider. We had to synchronize the measure in order to use them for the identification. • Next task: Design of Coltrol laws!!!! Estimation of the aerodynamical parameters of an experimental airship. IROS 2005.

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