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標題: 應用卡曼濾波器於壓電振動陀螺儀的狀態估測
Design of Kalman Filters for State Estimation of Piezoelectric Vibration Gyroscopes
作者: 林泰瑋
Lin, Tai-Wei
關鍵字: piezoelectric vibration gyroscope;壓電振動陀螺儀;Kalman filter;double population genetic algorithm;extended Kalman filter;卡曼濾波器;雙族群基因演算法;延展型卡曼濾波器
出版社: 電機工程學系所
引用: [1] Batur, C., Sreeramreddy, T. and Khasawneh, Q., Sliding mode control of a simulated mems gyroscope, Proceedings of the American Control Conference, Vol. 6, 2005, pp. 4160-4165. [2] Mochida, Y., Tamura, M. and Ohwada, K., A micromachined vibrating rate gyroscope with independent beams for the drive and detection modes, IEEE International Conference on Micro Electro Mechanical Systems, 1999, pp. 618-623. [3] Piyabongkarn, D., Rajamani, R. and Greminger, M., The development of a MEMS gyroscope for absolute angle measurement, IEEE Transactions on Control Systems Technology, Vol. 13, 2005, pp. 185-195. [4] Pryputniewicz, R.J., Xiangguang, Tan and Przekwas, A.J., Modeling and measurements of MEMS gyroscopes, Position Location and Navigation Symposium, 2004, pp. 111-119. [5] Rahbari, R., Leach, B.W., Dillon, J. and de Silva, C.W., Adaptive tuning of a Kalman filter using the fuzzy integral for an intelligent navigation system, Proceedings of the IEEE International Symposium Conference on Intelligent Control, 2002, pp. 252-257. [6] Saab, S.S., Discrete-time Kalman filter under incorrect noise covariances, Proceedings of the American Control Conference, Vol. 2, 1995, pp. 1152-1156. [7] Brown, R. G. and Patrick, Y. C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, John Wiley & Sons, Inc., 1997, pp. 335-336. [8] Shi, K.L., Chan, T.F., Wong, Y.K. and Ho, S.L., Speed estimation of an induction motor drive using an optimized extended Kalman filter, IEEE Transactions on Industrial Electronics, Vol. 49, 2002, pp. 124-133. [9] Lee, B.J., Park, J.B., Joo, Y.H. and Jin, S.H., Intelligent Kalman filter for tracking a maneuvering target, IEE Proceeding on Solid State Sensors and Actuators, Vol. 151, 2004, pp. 344-350. [10] Saren, H. and Parviainen, A. Real Time Simulation and Measurement Environment of DC Converters for Educational Purposes, Proceeding of the SIMS, 2002, pp.133-138. [11] Qi Lin and Stern, P.E. Analysis of a correlation filter for thermal noise reduction in a MEMS gyroscope. Proceedings of the Thirty-Fourth Southeastern Symposium on System Theory, 2002, pp. 197-203. [12] Lin, C. L., Jan, H. Y. and Shieh, N. C. GA-based multiobjective PID control for a linear brushless DC motor. IEEE/ASME Transactions on Mechatronics, Vol. 8, 2003, pp. 56-65. [13] Goldberg, D. E. Algorithms in optimization and Machine Learning. Addison Wesley Publishing Inc., 1989. [14] Kristinsson, K. and Dumont, G. A. System identification and control using genetic algorithm, IEEE Transactions on Systems, Man and Cybernetics, Vol. 22, 1992, pp. 1033-1046. [15] Vlachos, C., Williams, D. and Gomm, J.B. Genetic approach to decentralised PI controller tuning for multivariable processes. IEE Proceedings on Control Theory and Applications, Vol. 146, 1999, pp. 58-64. [16] Michalewicz, Z. and Krawezyk, J. B. A modified genetic algorithm for optimal control problems, Computer Mathematics Application, Vol. 23, 1992, pp. 83-94. [17] Gelb, A., Applied Optimal Estimation, The Analytic Sciences Corporation, 1974. [18] Grewal, M. S. and Andrews, A. P., Kalman Filtering: Theory and Practice Using MATLAB, 2 ed. John Wiley & Sons, Inc. 2001.

Standard Kalman filters (KF) and extended Kalman filters (EKF) have been commonly applied in the state estimation of maneuvering targets. Standard KFs minimize the estimated error variance between clean signal and its estimation. EKFs solve the weighted least-squares predictor-corrector feature for nonlinear stochastic systems. However, their performances might not be expected because the exact process noise covariance and measurement noise covariance are usually hard to achieve. We propose here a method for design of standard KFs and EKFs to estimate the angular velocity from a piezoelectric vibration gyroscope. In the proposed estimating scheme, a conventional KF combined with a double population genetic algorithm (DPGA) is first used to improve the estimation performance. Then, an EKF combines with DPGA is considered. The DPGA is used to determine the optimal process noise covariance during state estimation. Based on it a more accurate state estimation is shown to be achievable. Simulation results presented show that the gyroscope can accurately measure angular rate. Results of simulation and experimental verification are presented.
其他識別: U0005-2508200612402200
Appears in Collections:電機工程學系所

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