Authors

Abstract

This paper deals with the estimation of gyro model parameters and GEO satellite moment of inertia at the same time in transfer orbit phase. In order to fuse information of attitude determination subsystem sensors, an extended Kalman Filter has been employed. The estimation variables are including: quaternion, angular velocity, moments of inertia, and gyro sensor model parameters which are bias and scale factor vectors. The satellite motion equations along with gyro sensor and quaternion measurements have been used to design an Extended Kalman Filter in order to estimate the desired states. The disturbance torque effect on moment of inertia identification has been also considered. Estimation results via some case studies demonstrate the numerical simulation section exhibit robustness and efficiency of Kalman Fillter.

Keywords

  1. [1] Gadelha de Souza, L. C, “Experimental Parameters Estimation of Satellite Attitude Control Simulator,” Journal of Aerospace Engineering, Sciences and Applications, Vol. 1, No 2, 2008, pp. 14-22.
  2. [2] Carter, M. T., Vadali, S. R. and Chamitoff, G. E., “Parameter Identification for the International Space Station Using Nonlinear Momentum Management Control,” Proceedings of the AIAA Guidance, Navigation, and Control Conference, AIAA 97 3524, 1997, pp. 252–262.
  3. [3] Bergmann, E. V. and Dzielski, J., “Spacecraft Mass Property Identification with Torque-Generating Control,” Journal of Guidance, Control and Dynamics, Vol. 13, No. 1, 1990, pp. 99-103.
  4. [4] Creamer, G., DeLaHunt, P., Gates, S. and Leyenson, M. “Attitude Determination and Control of Clementine during Lunar Mapping,” Journal of Guidance, Control and Dynamics, Vol. 19, No. 3, 1996, pp. 505-511.
  5. [5] Gebre-Egziabher, D., Hayward, R. C. and Powell, J. D., “Design of Multi-Sensor Attitude Determination Systems,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 40, No. 2, 2004, pp. 627-648.
  6. [6] Wertz, J. R., (Editon), Spacecraft Attitude Determination and Control, D. Reide Publishing Company, 1978, pp. 414-416.
  7. [7] Pittelkau, M. E., “Everything is Relative in Spacecraft System Alignment Calibration,” Journal of Spacecraft and Rockets, Vol. 39, No. 3, 2002, pp. 460-466.
  8. [8] Pittelkau, M. E. “Kalman Filtering for Spacecraft System Alignment Calibration,” Journal of Guidance, Control and Dynamics, Vol. 24, No. 6, 2002, pp. 1187-1195.
  9. [9] Lai, K. L., Crassidis, J. L. & Harman, R. R., “In-Space Spacecraft Alignment Calibration using the Unscented Filter,” Proceedings of AIAA Guidance, Navigation, and Control Conference Exhibit, Austin, Texas, USA, 2003.
  10. Ma, G. and Jiang, X., “Unscented Kalman Filter for Spacecraft Attitude Estimation and Calibration using Star Tracker Measurements,” Proceedings of the 4th International Conference on Machine Learning and Cybernatics, Guanzhou, 2009.
  11. Myung, H. and Bang, H., “Spacecraft Parameter Estimation by Using Predictive Filter Algorithm,” Proceedings of IFAC World Congress, Seoul, Korea, 2008.
  12. Ejiang, Y. T., Chang, F. R. and Wang, L. S., “Data Fusion of Three Attitude Sensors,” SICE, Nagoya, 2001.
  13. Ziyang, Zh., Zhisheng, W. and Yong, H., “Multi-Sensor Information Fusion for Aircraft Attitude Determination System,” World Congress on Computer Science and Information Engineering, 2009.
  14. Myung, H. S. Yong, K. K. and Bang, H., “Unscented Kalman Filtering for Hybrid Estimation of Spacecraft Attitude Dynamics and Rate Sensor Alignment,” Advances in the Astronautical Sciences, Vol. 118, No. 1, 2009, pp. 217-228.
  15. Yu, Ch. and Huang, Y., “Hybrid Filtering for Satellite Attitude Estimation with Asynchronous Multi-Sensors,” 3rd International Conference on Intelligent Networks and Intelligent Systems, 2010.
  16. Bolandi, H. and Fani-Saberi, F., “Design of an Attitude Estimation Algorithm for a LEO Sattelite Based on Multiple Models Adaptive Method And Comparison With EKF,” Journal of Space Science and Technology, 2, No. 4, 2009, pp. 17-26.