Journal of System Simulation
Abstract
Abstract: UsingextendedKalmanfilter (EKF) to estimate the position of permanent magnet linear synchronous motor(PMLSM), the model is not accurate, the noise properties areuncertain,and may lead to the problem of filtering divergence.Adouble forgetting Kalman filter (DFKF) method was proposed. Adaptive fading factor on the basisof EKF was introduced to achieve the first forgetting,andthe Sage-Husa adaptive filter algorithm was introduced to realize the second forgetting. The experiments show that DFKF diminishesaccording to the law of sineregardless synchronous speed change or load mutation;the stable error is 0.469% or 0.943% before or after the load mutation; the final error stabilizes near 0.167%;the effects will be better with the longer time of the simulation.
Recommended Citation
Jun, Zhu; Li, Xiangjun; Fu, Rongbing; Wu, Yuhang; and Miao, Tian
(2019)
"PMLSM without Position Sensing Control of Double ForgettingKalman Filter,"
Journal of System Simulation: Vol. 30:
Iss.
2, Article 37.
DOI: 10.16182/j.issn1004731x.joss.201802037
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol30/iss2/37
First Page
672
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.201802037
Last Page
678
CLC
TP391.9
Recommended Citation
Zhu Jun, Li Xiangjun, Fu Rongbing, Wu Yuhang, Tian Miao. PMLSM without Position Sensing Control of Double ForgettingKalman Filter[J]. Journal of System Simulation, 2018, 30(2): 672-678.
DOI
10.16182/j.issn1004731x.joss.201802037
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons