Non-Linear State Estimation Using Derivative-Free Filters for a Three-Phase Induction Motor Model

Ravikumar, J.; Subramanian, S.; Prakash, J.

doi:10.1080/1448837x.2011.11464301

Cited by 1 publication

(1 citation statement)

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“…Therefore, the UKF is a nonlinear filter with high precision, strong stability and small computational burden, which is suitable for estimating parameters and states of high-speed objects. In recent years, the UKF has been widely used in estimation of states and parameters [12][13][14], control [15], target tracking [16,17], navigation [18,19], map building [20,21] and fault diagnosis [22]. At the same time, many scholars have tried to improve the UKF in terms of its nu-merical stability and computational speed, e.g., a decrease sigma points UKF [23] and square root decomposition of the UKF [24,25].…”

Section: Introductionmentioning

confidence: 99%

Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

Deng

Chen

2013

J. of Syst. Eng. Electron.

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An adaptive unscented Kalman filter (AUKF) and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter (UKF) when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.

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