Sensorless control of salient PMSM with adaptive integrator and resistance online identification using strong tracking filter

Ma, Shaohua; Peijun, Wu; Ji, Jinhu; Xu-chun, LI

doi:10.1080/00207217.2015.1036317

Cited by 12 publications

(2 citation statements)

References 31 publications

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“…By comprehensively considering the electrical and physical characteristics, this paper proposes to use the nonlinear filter for noise reduction and continuity of the native data, which can eliminate error and obtain the universal impedance characteristic variation law [18]. There are more types of nonlinear filters, more representative of the Kalman Filter, Trackless Kalman Filter, and Strong Tracking Filter (STF), among others.…”

Section: Introductionmentioning

confidence: 99%

Novel method for identifying the stages of discharge underwater based on impedance change characteristic

GAO 高,

KANG 康,

GONG 龚

et al. 2024

Plasma Sci. Technol.

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It is difficult to determine the discharge stages in a fixed time of repetitive discharge underwater due to the arc formation process being susceptible to external environmental influences. This paper proposes a novel underwater discharge stage identification method based on the Strong Tracking Filter (STF) and impedance change characteristics. The time-varying equivalent circuit model of the discharge underwater is established based on the plasma theory analysis of the impedance change characteristics and mechanism of the discharge process. The STF is used to reduce the randomness of the impedance of repeated discharges underwater, and then the universal identification resistance data is obtained. Based on the resistance variation characteristics of the discriminating resistance of the pre-breakdown, main, and oscillatory discharge stages, the threshold values for determining the discharge stage are obtained. These include the threshold values for the resistance variation rate (K) and the moment (t). Experimental and error analysis results demonstrate the efficacy of this innovative method in discharge stage determination, with a maximum mean square deviation of Scr less than 1.761.

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Section: Introductionmentioning

confidence: 99%

Novel method for identifying the stages of discharge underwater based on impedance change characteristic

GAO 高,

KANG 康,

GONG 龚

et al. 2024

Plasma Sci. Technol.

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“…Jia and Xin [28], [31] from Columbia University -Missouri State University analyzed the volume rule that can obtain the accuracy of arbitrary order estimation, and proposed a high-order Cuban Kalman Filter (HCKF) method with more volume points. Meanwhile, They focus on the fifth-order volume Kalman filter and show the filter accuracy is similar to the Gaussian Hermititian filter (GHQF) [32]. Although the calculation is much smaller that GHQF, but it is much larger that CKF, so it is not conducive to tracking high-speed target [28].…”

Section: Introductionmentioning

confidence: 99%

An Improved IMM Algorithm Based on STSRCKF for Maneuvering Target Tracking

Han

Huang

Lei³

et al. 2019

IEEE Access

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To overcome the IMM algorithm is easy divergence and low tracking accuracy when dealing with complex maneuvering situations, this paper proposes an improved interactive multiple model strong tracking square room cubature Kalman filter (IIMM-STSRCKF) algorithm under the idea of real-time dynamic adjustment of gain matrix and transition probability matrix. The algorithm has been improved in two aspects: on the one hand, the algorithm uses the idea of a strong tracking filter to deduce a new method for time-varying fading factor and introduce it into the square root of the state error covariance matrix of the SRCKF, which improves the tracking accuracy for strong maneuver; on the other hand, the probability difference between two consecutive time points in the IMM submodel is used to adjust the Markov probability transfer matrix to adaptively improve the switching speed of the submodel and the rationality of the allocation. By comparing with IMM-CKF algorithm by maneuvering target tracking case and results show that the IIMM-STSRCKF algorithm has better tracking performance in nonmaneuvering, weak maneuvering, and strong maneuvering cases.INDEX TERMS Complex maneuvering, IMM, SRCKF, strong tracking filter.

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Performance Analysis of Flux-Switching Stator Permanent Magnet Motor Based on Linear Active Disturbance Rejection Control

Wang

Chen

Sun

et al. 2018

Proceedings of 2018 Chinese Intelligent Systems Conference

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Sensorless control of salient PMSM with adaptive integrator and resistance online identification using strong tracking filter

Cited by 12 publications

References 31 publications

Novel method for identifying the stages of discharge underwater based on impedance change characteristic

Novel method for identifying the stages of discharge underwater based on impedance change characteristic

An Improved IMM Algorithm Based on STSRCKF for Maneuvering Target Tracking

Performance Analysis of Flux-Switching Stator Permanent Magnet Motor Based on Linear Active Disturbance Rejection Control

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