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A Review of nonlinear kalman filter Appling to Sensorless Control for AC Motor Drives

CES Transactions on Electrical Machines and Systems 2019年第4期3卷 351-362页

作者： Zhonggang Yin Fengtao Gao Yanqing Zhang Chao Du Guoyin Li Xiangdong Sun the Department of Electrical Engineering Xi'an University of TechnologyXi'an710048China

Sensorless control of AC motor drives,which takes the advantages of cost saving,higher reliability,and less hardware,has been developed for several *** the existing speed sensorless control methods,nonlinear kalman filter-based one has attached widespread attention due to its superb estimation accuracy and inherent resistibility to ***,the determination of noise covariance matrix and robustness of model uncertainties are still open issues in practice.A great number of studies try to solve these problems in resent *** paper reviews the application of extended kalman filter(EKF),unscented kalman filter(UKF),and cubature kalman filter(CKF)in speed sensorless control for AC motor *** an iterative algorithm,EKF has advantages in processor ***,EKF suffers from the linearization error and model uncertainties when applying to sensorless control *** paper presents the predominant improvements of EKF which is also applicative in UKF and CKF mostly.

关键词： AC motor drive nonlinear kalman filter robustness sensorless control.

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Variational Bayesian kalman filter using natural gradient

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Chinese Journal of Aeronautics 2022年第5期35卷 1-10页

作者： Yumei HU Xuezhi WANG Quan PAN Zhentao HU Bill MORAN School of Automation Northwestern Polytechnical UniversityXi’an 710072China The Key Laboratory of Information Fusion Technology Ministry of EducationXi’an 710072China Department of Electrical and Electronic Engineering The University of MelbourneMelbourneVIC 3010Australia School of Engineering RMIT UniversityMelbourneVIC 3000Australia School of Computer and Information Engineering Henan UniversityKaifeng 475001China

We propose a technique based on the natural gradient method for variational lower bound maximization for a variational Bayesian kalman *** natural gradient approach is applied to the Kullback-Leibler divergence between the parameterized variational distribution and the posterior density of *** a Gaussian assumption for the parametrized variational distribution,we obtain a closed-form iterative procedure for the Kullback-Leibler divergence minimization,producing estimates of the variational hyper-parameters of state estimation and the associated error *** results in both a Doppler radar tracking scenario and a bearing-only tracking scenario are presented,showing that the proposed natural gradient method outperforms existing methods which are based on other linearization techniques in terms of tracking accuracy.

关键词： Kullback-Leibler divergence Natural gradient nonlinear kalman filter Target tracking Variational Bayesian optimization

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维普期刊数据库

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Sufficient and Necessary Condition for Stabilization of Jump Linear Systems with Noise

Sufficient and Necessary Condition for Stabilization of Jump...

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2010 Chinese Control and Decision Conference

作者： Liu Feng~1,Z.Liu~2,H.B.Dong~3 1.School of Electronics and Information Engineering Beijing Jiaotong University,Beijing 100044,China 2.School of Electronics and Information Engineering Beijing Jiaotong University,Beijing 100044,China 3.Central University of Finance and Economic,Beijing 100044,China

＜正＞The adaptive stabilization problem of linear systems with unknown parameters and noise models is studied in this paper,we investigate continuous time jump linear systems with a finite-state hidden Markov jump form process.A sufficient and necessary condition characterizing the adaptive stabilizability of the system is *** is worth mentioning that a constructive method for designing stabilizing feedback law is provided in this paper.

关键词： Stabilization nonlinear kalman filter jump linear systems.

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nonlinear Recursive Update Strategy for Generalized-Maximum-Likelihood Type kalman filters

Nonlinear Recursive Update Strategy for Generalized-Maximum-...

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第43届中国控制会议

作者： Shoupeng Li Shihui Xu Jiawei Ren Boyuan Yang College of Electronic Information and Optical Engineering Nankai University College of Artificial Intelligence Nankai University

Large initial errors and heavy-tailed observation noise can induce degraded accuracy in state *** the combination of iterative strategy and generalized M-estimation can cope with these problems,the iterative update fails to follow the nonlinearity of observation models,which further induces degradation in filtering *** address the problem,the iterative strategy in generalized M-estimation is replaced with a recursive strategy,which results in the gradual release of observation *** statistical linear regression,the recursive update is applied to nonlinear systems;considering the correlation between state and observation noise induced by recursive update,a novel modification strategy is adopted to build a robust nonlinear system;by analogy with recursive strategy,robust nonlinear recursive strategy is proposed,and a propagation strategy for associated covariances is given to ensure the consistency of observation information statistical *** studies show that the recursive strategy can facilitate the estimator to achieve higher accuracy,stronger robustness,and better consistency.

关键词： Generalized M-estimation iterative update recursive update nonlinear kalman filter robust estimation

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