Browse > Article
http://dx.doi.org/10.5762/KAIS.2012.13.2.772

Parameter Estimation of Dynamic System Based on UKF  

Seung, Ji-Hoon (Electronics and Information Department, Chonbuk National University)
Chong, Kil-To (Electronics and Information Department, Chonbuk National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.13, no.2, 2012 , pp. 772-778 More about this Journal
Abstract
In this paper, the states and the parameters in the dynamic system are simultaneously estimated by applying the UKF(Unscented Kalman Filter), which is widely used for estimating the state of non-linear systems. Estimating the parameter is very important in various fields, such as system control, modeling, analysis of performance, and prediction. Most of the dynamic systems which are dealt with in engineering have non-linearity as well as some noise. Therefore, the parameter estimation is difficult. This paper estimates the states and the parameters applying to the UKF, which is a non-linear filter and has strong noise. The augmented equation is used by including the addition of the parameter factors to the original state equation of the system. Moreover, it is simulated by applying to a 2-DOF(Degree of Freedom) dynamic system composed of the pendulum and the slide. The measurement noise of the dynamic equation is assumed to be a Gaussian distribution. As the simulation results show, the proposed parameter estimation performs better than the LSM(Least Square Method). Furthermore, the estimation errors and convergence time are within three percent and 0.1 second, respectively. Consequentially, the UKF is able to estimate the system states and the parameters for the system, despite having measurement data with noise.
Keywords
Unscented Kalman Filter; Dynamic System; Parameter Estimation;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Young-Seok Cho, Duk-Sun Shim, Chel-Kwan Yang, Jin-Woo Park, "Performance Investigation of the Unscented Kalman Filter for Ultra-tightly GPS/INS Integration", Journal of Institute of Control, Robotics and Systems, vol. 13, no. 8, 817-823, 2007.   과학기술학회마을   DOI
2 Oh Shin Kwon, "Parameter Estimation of Recurrent Neural Networks Using A Unscented Kalman Filter Training Algorithm and Its Applications to Nonlinear Channel Equalization", Journal of Korean Institute of Intelligent Systems, vol. 15, no. 5, 552-559, 2005.   과학기술학회마을   DOI   ScienceOn
3 K. T. Chong, J. H. Park, A. G. Parlos, "Control-Relevant Discretization of Nonlinear Systems With Time-Delay Using Taylor-Lie Series," Journal of Dynamic Systems, Measurement, and Control, Vol.127, 153-159, 2005.   DOI
4 Andrew K. Stimac, "Standup and Stabilization of the Inverted Pendulum," MIT, master thesis, 1999.
5 Sahar Pirooz Azad, Joseph Euzebe Tate, "Parameter Estimation of Doubly Fed Induction Generator Driven by Wind Turbine", Power Systems Conference and Exposition (PSCE), 2011 IEEE/PE, March 2011.
6 Panuska, V. "A new form of the extended Kalman filter for parameter estimation in linear systems with correlated noise", Automatic Control, IEEE Transactions on, Vol.25, 229-235, Apr 1980.   DOI
7 Emmanuel Blanchard, Adrian Sandu, Corina Sandu, "Parameter Estimation Method using an Extended Kalman Filter", Proceedings of the Joint North America, Asia-Pacific ISTVS Conference and Annual Meeting of Japanese Society for Terramechanics 2007.
8 Jeng-Ming Chen, Bor-Sen Chen, "System Parameter Estimation with Input/Output Noisy Data and Missing Measurements", IEEE Transactions on Signal Processing, Vol. 48, No. 6, June 2000.
9 Wang Wan-ping, Liao Sheng, Xing Ting-wen, "Particle Filter for State and Parameter Estimation in Passive Ranging", Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on, Vol.3, 257-261, Nov. 2009.
10 Jin-Woo Park, Cheol-Kwan Yang, Duk-Sun Shim, "Particle Filter Performance for Ultra-tightly GPS/INS integration", Journal of Institute of Control, Robotics and Systems, Vol. 14, No. 8, August 2008.   과학기술학회마을   DOI   ScienceOn
11 K. V. Fernando and H. Nicholson, "Identification of linear systems with input and output noise: The Koopmans-Levin method," Proc. Inst. Elect. Eng. D, vol.132, pp. 30-36, 1985.
12 S. Julier, J. Uhlmann, "A new extension of the Kalman filter to nonlinear systems", in: Proceedings of the 1997 SPIE AeroSense Symposium, SPIE, 21-24, April 1997.
13 Julier. S. J, "The Scaled Unscented Transformation", Proceednig of the American Control Conference, Anchorage, AK, Vol.6 4555 - 4559, May 2002.
14 Joongsup Yun, Chang-Kyung Ryoo, Taek-Lyul Song, "Guidance Filter Design Based on Strapdown Seeker and MEMS Sensors", Journal of the Korean Society for Aeronautical & Space Sciences, vol .37, no. 10, 1002-1009, 2009.   과학기술학회마을   DOI   ScienceOn
15 Pintelon, R., Schoukens, J., "Identification of stochastic linear systems in the presence of Nonlinear Distortion", Instrumentation and Measurement Technology Conference, 2000. IMTC 2000. Proceedings of the 17th IEEE, Vol.2, 879-884, 2000.
16 Sohns, B., Allison, J., Fathy, H. K., Stein, J. L., "Efficient Parameterization of LargeScale Dynamic Models Through the Use of Activity Analysis", Proceedings of the ASME IMECE 2006, IMECE2006, Nov 5-10, 2006.
17 Aksoy, S., Muhurcu, A., Kizmaz, H., "State and Parameter Estimation in Induction Motor Using the Extended Kalman Filtering Algorithm", Modern Electric Power Systems (MEPS), 2010 Proceedings of the International Symposium, 2010.
18 Hurtig, J., Yurkovich, S., "Parameter set estimation for nonlinear systems", System Theory, 2001. Proceedings of the 33rd Southeastern Symposium on, 275-280, Mar 2001.
19 Nagatsuka, H., "A Study of Estimation for the Three-Parameter Weibull Distribution Based On Doubly Type-II Censored Data Using a Least Squares Method", Secure System Integration and Reliability Improvement, 2008. SSIRI '08. Second International Conference on, 158-165, 2008.