Research on Anti-Error Performance of Speed and Flux Estimator for Induction Motor Using Robust Reduced-Order EKF

Chang, Zhao; Liu, Jing; Zhong, Yanru

doi:10.1109/tii.2012.2222422

Cited by 36 publications

(12 citation statements)

References 34 publications

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“…The covariance matrix Q represents the noises on the system due to the modeling errors. From (17) and (18), the matrix K is proportional to the matrix Q. Then, it has the opposite effect of that of R. Under the premise to ensure convergence, the choice of every values for these matrices is done according to the dynamics of the state variables and the performance index.…”

Section: Resultsmentioning

confidence: 99%

“…Because of the massive calculation with a five-order matrix, most of the research results are only verified by simulations. In order to apply it to practice, a reduced-order EKF algorithm for flux and speed estimation is proposed in [17] and [18], and the speed and flux linkage are estimated easily. In [19], an EKF has been used to estimate the rotor speed, rotor flux, stator flux and stator currents accurately in the vector control systems of the induction motors, and a small stator current THD is confirmed.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors

Li¹,

Du²,

Sun³

et al. 2017

Journal of Power Electronics

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To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors

Li¹,

Du²,

Sun³

et al. 2017

Journal of Power Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…But there is unstable area for motor parameters estimation at low speed range. In literature [34], the robust Kalman filter and adaptive speed estimation are proposed. Although the sensitivity to external and internal interrupt is decreased at middle and high speed range, the performance at low speed range is not well.…”

Section: Design Methods Of Adaptive Full Order Observer With or Withoumentioning

confidence: 99%

“…So (34) can be satisfied as long as g 2 is negative. However this feedback gains only can be applied in fundamental speed range (within ±50Hz).…”

Section: B Robust Speed Estimation With Corresponding Robust Feedbacmentioning

confidence: 99%

Design Method of Adaptive Full Order Observer With or Without Estimated Flux Error in Speed Estimation Algorithm

Sun

Wang

et al. 2016

IEEE Trans. Power Electron.

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“…A maioria dos estimadores de velocidade tem origem no modelo matemático do motor de indução, onde é necessário o conhecimento preciso dos parâmetros do motor (VASIC; VU-KOSAVIC; LEVI, 2003; AMEZQUITA-BROOKS; LICEAGA-CASTRO; LICEAGA-CASTRO, 2014a; KAN; ZHANG; WANG, 2015; SMITH; GADOUE; FINCH, 2016). Muitas estratégias para a estimação de velocidade têm sido propostas; tais como o uso do Sistema Modelo de Referência Adaptativo (SMRA) (YANG et al, 2016; ORLOWSKA-KOWALSKA; DYBKOWSKI, 2010), injeção de sinais de alta frequência (CARUANA; ASHER; SUMNER, 2005), Observador de Modos Deslizantes (OMD) (ZHANG, 2013), Filtro Extendido de Kalman (FEK) (YIN et al, 2013;ANDRIAMALALA et al, 2011) Em Yang et al (2016) é apresentado o uso de um OMD aplicado para estimar a velocidade de um MI, onde é observada a sensibilidade do sistema frente a precisão paramétrica. O trabalho de Orlowska-Kowalska e Dybkowski (2010) desenvolveu um novo tipo de SMRA para estimar a velocidade do rotor com controle vetorial do MI.…”

Section: Introductionunclassified

Uma proposta de estimador neural da velocidade para controle vetorial do motor de indução

Santos¹

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Primeiramente, eu agradeço a Deus por sempre se fazer presente. Agradeço à minha esposa Grazielli Bueno, por seu apoio, incentivo e, principalmente pelo seu companheirismo e dedicação. À minha filha Maria Luiza dos Santos, pelos momentos de alegria, aprendizagem como pai e pela sua compreensão ao longo dos momentinhos em que fiquei estudando e não pude dar toda atenção que ela merecia. À minha mãe, Salete Regina Lugle, que sempre me apoiou em todos os momentos da minha vida, orando para que tudo pudesse acontecer com tranquilidade. Ao meu orientador, Prof. Dr. Ivan Nunes da Silva, pela sua orientação, ensinamentos, e por suas contribuições para mais essa etapa de minha formação acadêmica e profissional. Agradeço ainda pelos preciosos ensinamentos na elaboração e construção desta pesquisa. Agradeço ao meu co-orientador, Prof. Dr. Alessandro Goedtel, por ter acreditado e trabalhado incansavelmente ao meu lado. Ainda o agradeço pelos ensinamentos na elaboração e correção dos materiais científicos produzidos ao longo de minha jornada desde o mestrado e também por seus valorosos conselhos. Aos amigos e parceiros de doutorado, Prof. Msc. Wylliam Salviano Gongora, Profs. Dr. Wagner Fontes Godoy e Dr. Profs. Rodrigo Henrique Cunha Palácios, pelo companherismo, disposição em sempre ajudar, pelos grandes momentos de conversas e principalmente pelo incentivo na pesquisa, minha eterna gratidão. Aos amigos e parceiros de laboratório, Msc.

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Research on Anti-Error Performance of Speed and Flux Estimator for Induction Motor Using Robust Reduced-Order EKF

Cited by 36 publications

References 34 publications

An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors

An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors

Design Method of Adaptive Full Order Observer With or Without Estimated Flux Error in Speed Estimation Algorithm

Uma proposta de estimador neural da velocidade para controle vetorial do motor de indução

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