Biomedical Engineering Reference
In-Depth Information
can outperform DEKF by comparing the performance of tracking estimation value,
NRMSE and prediction overshoot analysis. Moreover, HEKF has more discrimi-
nated degree with comparison to DEKF across any group numbers selected, which
means HEKF has less error than DEKF. Even though the provided method needed
more computational time comparing to the previous method, the experiment
results showed that it improved NRMSE around 24.72 % across the overall pre-
diction time horizon.
References
1. J. Tan, N. Kyriakopoulos, Implementation of a tracking Kalman filter on a digital signal
processor. IEEE Trans Ind Electron 35(1), 126-134 (1988)
2. Heui-Wook Kim, Seung-Ki Sul, A new motor speed estimator using Kalman filter in low-
speed range. IEEE Trans Ind Electron 43(4), 498-504 (1996)
3. B. Terzic, M. Jadric, Design and implementation of the extended Kalman filter for the speed
and rotor position estimation of brushless DC motor. IEEE Trans Ind Electron 48(6),
1065-1073 (2001)
4. Murat Barut, Seta Bogosyan, Metin Gokasan, Speed-Sensorless Estimation for Induction
Motors Using Extended Kalman Filters. IEEE Trans Ind Electron 54(1), 272-280 (2007)
5. S.-h.P. Won, W.W. Melek, F. Golnaraghi, A Kalman/particle filter-based position and
orientation estimation method using a position sensor/inertial measurement unit hybrid
system. IEEE Trans. Ind. Electron. 57(5), 1787-1798 (2010)
6. M. Chueh, Y.L.W. Au Yeung, K.-P.C. Lei, S.S. Joshi, Following Controller for autonomous
mobile robots using behavioral cues. IEEE Trans Ind Electron 55(8), 3124-3132 (2008)
7. Y. Motai, A. Kosaka, Hand-Eye calibration applied to viewpoint selection for robotic vision.
IEEE Trans Ind Electron 55(10), 3731-3741 (2008)
8. W.-S. Ra, H.-J. Lee, J.B. Park, T.-S. Yoon, Practical pinch detection algorithm for smart
automotive power window control systems. IEEE Trans. Ind. Electron. 55(3), 1376-1384
(2008)
9. K. Szabat, T. Orlowska-Kowalska, Performance improvement of industrial drives with
mechanical elasticity using nonlinear adaptive Kalman filter. IEEE Trans Ind Electron 55(3),
1075-1084 (2008)
10. A.G. Beccuti, S. Mariethoz, S. Cliquennois, S. Wang, M. Morari, Explicit model predictive
control of DC-DC switched-mode power supplies with extended Kalman filtering. IEEE
Trans. Ind. Electron. 56(6), 1864-1874 (2009)
11. K. Szabat, T. Orlowska-Kowalska, M. Dybkowski, Indirect adaptive control of induction
motor drive system with an elastic coupling. IEEE Trans Ind Electron 56(10), 4038-4042
(2009)
12. C. Mitsantisuk, S. Katsura, K. Ohishi, Kalman-Filter-based sensor integration of variable
power assist control based on human stiffness estimation. IEEE Trans Ind Electron 56(10),
3897-3905 (2009)
13. N. Salvatore, A. Caponio, F. Neri, S. Stasi, G.L. Cascella, Optimization of delayed-state
Kalman-filter-based algorithm via differential evolution for sensorless control of induction
motors. IEEE Trans Ind Electron 57(1), 385-394 (2010)
14. M. Charkhgard, M. Farrokhi, State of charge estimation for lithium-ion batteries using neural
networks and EKF. IEEE Trans Ind Electron 57(12), 4178-4187 (2010)
15. G.V. Puskorius, L.A. Feldkamp, Neurocontrol of nonlinear dynamical systems with Kalman
filter trained recurrent networks. IEEE Trans Neural Netw 5(2), 279-297 (1994)
Search WWH ::




Custom Search