State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF

Xu, Bo; Mu, Fangqiang; Shi, Guoding; Ji, Wei; Zhu, Huangqiu

doi:10.3390/en9070489

Cited by 13 publications

(10 citation statements)

References 14 publications

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“…, 2n. When the system meets the Gaussian noise assumptions, α, β and κ in the UKF approach are typically set as 10 −3 , 2 and 0 respectively [36].…”

Section: Unscented Kalman Filtermentioning

confidence: 99%

Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter

Zeng

Wang

Zhang

2016

Sci. China Inf. Sci.

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This paper is concerned with the problem of learning structure of the lateral flow immunoassay (LFIA) devices via short but available time series of the experiment measurement. The model for the LFIA is considered as a nonlinear state-space model that includes equations describing both the biochemical reaction process of LFIA system and the observation output. Especially, the time-delays occurring among the biochemical reactions are considered in the established model. Furthermore, we utilize the unscented Kalman filter (UKF) algorithm to simultaneously identify not only the states but also the parameters of the improved state-space model by using short but high-dimensional experiment data in terms of images. It is shown via experiment results that the UKF approach is particularly suitable for modelling the LFIA devices. The identified model with time-delay is of great significance for the quantitative analysis of LFIA in both the accurate prediction of the dynamic process of the concentration distribution of the antigens/antibodies and the performance optimization of the LFIA devices.

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“…, 2n. When the system meets the Gaussian noise assumptions, α, β and κ in the UKF approach are typically set as 10 −3 , 2 and 0 respectively [36].…”

Section: Unscented Kalman Filtermentioning

confidence: 99%

Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter

Zeng

Wang

Zhang

2016

Sci. China Inf. Sci.

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“…One is for the error covariance matrix, which may be negative on Cholesky decomposition. Here are some methods for this problem such as SR decomposition [13], singular value decomposition (SVD) [14], and adaptive noise variance [15]. SVD is more robust than SR decomposition and less complex than the adaptive noise variance method, so it is adopted in this study [16].…”

Section: Introductionmentioning

confidence: 99%

Multistage Estimators for the Distributed Drive Articulated Steering Vehicle

Gao

Wang

et al. 2020

Mathematical Problems in Engineering

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The distributed drive articulated steering vehicle (DDASV) has a broad application prospect in the field of special operations. It is essential to obtain accurate vehicle states for better effect of active control. DDASV dynamic model is presented. To improve robustness, an adaptive strong tracking algorithm is applied to the singular value decomposition unscented Kalman filter (SVDUKF). Divided by yaw rate sensors and the tire models, two multistage estimators are established for DDASVs. Stable steering condition is simulated to investigate the influence on the estimated accuracy about the sensors and tire models. The velocities and tire forces are the key parameters to be estimated. The performance of each estimator regarding the practicability and accuracy is compared. The results show that all estimators are practicable. However, the accuracy of the estimated velocities based on yaw rate sensors is better and the transient tire model can improve the accuracy of estimated lateral forces more effectively for the estimator established with yaw rate sensors.

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“…Eliminating the external rotor excitation removes the rotor losses, which improves the PMSM efficiency dramatically. Moreover, the absence of rotor winding eliminates not only the slip rings but also the brushes, which leads to a considerable reduction of the maintenance costs [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Proportional-Type Performance Recovery DC-Link Voltage Tracking Algorithm for Permanent Magnet Synchronous Generators

Kim

2017

Energies

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This study proposes a disturbance observer-based proportional-type DC-link voltage tracking algorithm for permanent magnet synchronous generators (PMSGs). The proposed technique feedbacks the only proportional term of the tracking errors, and it contains the nominal static and dynamic feed-forward compensators coming from the first-order disturbance observers. It is rigorously proved that the proposed method ensures the performance recovery and offset-free properties without the use of the integrators of the tracking errors. A wind power generation system has been simulated to verify the efficacy of the proposed method using the PSIM (PowerSIM) software with the DLL (Dynamic Link Library) block.

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State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF

Cited by 13 publications

References 14 publications

Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter

Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter

Multistage Estimators for the Distributed Drive Articulated Steering Vehicle

Proportional-Type Performance Recovery DC-Link Voltage Tracking Algorithm for Permanent Magnet Synchronous Generators

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