Speed sensorless vector control of induction machine with Luenberger observer and Kalman filter

Yousfi, Laatra; Houam, Lotfi; Boukrouche, Abdelhani

doi:10.1109/codit.2017.8102679

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…In electric drives with induction motors based on dynamic control methods such as field-oriented control (FOC) [1][2][3][4][5], it is necessary to reconstruct motor state variables such as components of magnetic fluxes coupled with the stator and rotor windings. The Luenberger observer can be used for this task [1,[6][7][8]. Additionally, the angular speed of the motor rotor can be estimated from the state variables reconstructed in the observer [6,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the angular speed of the motor rotor can be estimated from the state variables reconstructed in the observer [6,8,9]. This makes it possible to obtain a speed-sensorless control system where the angular speed measurement is not required [1,10,11]. Removal of the mechanical angular speed sensor, located on the motor shaft, simplifies the drive system and increases its reliability [12].…”

Section: Introductionmentioning

confidence: 99%

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Induction Motor PI Observer with Reduced-Order Integrating Unit

et al. 2021

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This article presents an innovative induction motor state observer designed to reconstruct magnetic fluxes and the angular speed of an induction motor for speed sensorless control system applications such as field-oriented control (FOC). This observer is an intermediate solution between the proportional observer and the classical proportional-integral (PI) observer with respect to which the order of the integrating unit is reduced. Additional modifications of the observer’s structure have been implemented to ensure stability and to improve its functional properties. As a result, two versions of the observer structure were produced and experimentally tested using a sensorless FOC control system. Both structures resulted in correct control system operation for a wide range of angular speeds, including low speed ranges.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Induction Motor PI Observer with Reduced-Order Integrating Unit

et al. 2021

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“…There are numerous techniques of speed estimation of induction machine electric drives, including adaptive flux observers (AFOs), model reference adaptive systems (MRASs) observers, and backstepping observes [7][8][9][10][11][12][13][14][15][16][17][18][19]. The estimation quality highly depends on the proper gains selection of the observer.…”

Section: Introductionmentioning

confidence: 99%

A Universal Gains Selection Method for Speed Observers of Induction Machine

Wachowiak

2021

Energies

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Properties of state observers depend on proper gains selection. Each method of state estimation may require the implementation of specific techniques of finding those gains. The aim of this study is to propose a universal method of automatic gains selection and perform its verification on an induction machine speed observer. The method utilizes a genetic algorithm with fitness function which is directly based on the impulse response of the observer. System identification using least-squares estimation is implemented to determine the dynamic properties of the observer based on the estimation error signal. The influence of sampling time as well as signal length on the system identification has been studied. The results of gains selection using the proposed method have been compared with results obtained using the approach based on the placement of the poles of linearized estimation error equations. The introduced method delivers results comparable with analytical methods and does not require prior preparation specific to the implemented speed observer, such as linearization.

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“…Among the most common induction machine speed estimation techniques, the following main groups can be distinguished: Model Reference Adaptive Systems (MRAS) [7,8], Adaptive Flux Observers (AFO) [9][10][11], Sliding Mode observers [12,13], Artificial Intelligence estimators [14][15][16], Kalman filters [17][18][19], backstepping observers [20,21]. The subject of studies presented in this paper is an observer proposed by Krzemiński in [22] which finally evolved into [23].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Approach for Gains Selection of Induction Machine Extended Speed Observer

Wachowiak

2020

Energies

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The subject of this paper is gains selection of an extended induction machine speed observer. A high number of gains makes manual gains selection difficult and due to nonlinear equations of the observer, well-known methods of gains selection for linear systems cannot be applied. A method based on genetic algorithms has been proposed instead. Such an approach requires multiple fitness function calls; therefore, using a quality index based on simulations makes gains selection a time-consuming process. To find a fitness function that evaluates, in a short time, quality indices based on poles placement have been proposed. As the observer is nonlinear, equations describing the observer dynamics have been linearized. The relationship between poles placement and real dynamic properties has been shown. A series of studies has been performed to investigate the influence of the operating point of the machine on the dynamics of the observer. It has been proven that rotor speed has a significant impact on the placement of the poles and the observer may lose stability after a rotation direction change. A method of gains modification to maintain symmetrical properties of the observer for both directions has been presented. Experimental studies of the observer during machine reverse in the open and closed-loop control system have been performed. The results show that the observer can be implemented in a sensorless drive, using the proposed gains selection method.

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Speed sensorless vector control of induction machine with Luenberger observer and Kalman filter

Cited by 5 publications

References 4 publications

Induction Motor PI Observer with Reduced-Order Integrating Unit

Induction Motor PI Observer with Reduced-Order Integrating Unit

A Universal Gains Selection Method for Speed Observers of Induction Machine

Genetic Algorithm Approach for Gains Selection of Induction Machine Extended Speed Observer

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