Speed sensorless vector control of an induction motor using an extended Kalman filter

Kim, Y.-R.; Sul, Seung-Ki; Park, M.-H.

doi:10.1109/ias.1992.244341

Cited by 124 publications

(112 citation statements)

References 13 publications

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“…Open loop scalar control is broadly used in induction motors drives (Finch, 1998), (Luo et al, 2007), (Srilad et al, 2007), however, its importance is diminishing because of the superior performance of vector controlled (or field oriented controlled) drives (Khambadkone & Holtz, 1991), (Kim et al, 1986), (Rowan et al, 1982), (Xu et al, 1988). Scalar-and vector-controlled drives have already been discussed in Section 3.1.4.…”

Section: Induction Machines Controlmentioning

confidence: 99%

Electric Machine Topologies in Energy Storage Systems

De¹,

Oliveir²

2010

Energy Storage

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Section: Induction Machines Controlmentioning

confidence: 99%

Electric Machine Topologies in Energy Storage Systems

De¹,

Oliveir²

2010

Energy Storage

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“…And the rotor speed is calculated with the adaption mechanism [11]. Mora and al [11], compared between adaptive speed estimation based on linear KF, straightforward EKF [12], and speed adaptive flux observer [6]. The results show in a real application that the sample time of EKF is greater 5 times than the adaptive one and a good performance in low speed compared with adaptive flux observer.…”

Section: Introductionmentioning

confidence: 99%

Neural Adaptive Kalman Filter for Sensorless Vector Control of Induction Motor

Ghlib¹,

Messlem²,

Gouichiche³

et al. 2017

IJPEDS

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This paper presents a novel neural adaptive Kalman filter for speed sensorless field oriented vector control of induction motor. The adaptive observer proposed here is based on MRAS (model reference adaptive system) technique, where the linear Kalman filter calculate the stationary components of stator current and the rotor flux and the rotor speed is calculated with an adaptive mechanism. Moreover, to improve the performance of the PI classical controller under different conditions, a novel adaptation scheme based on ADALINE (ADAptive LInear NEuron) neural network is used. It offers a solution to the PI parameters to stabilize automatically about their optimum values and speed estimation to converge quicker to the real. The proposed adaptive Kalman filter represents a good comprise between estimation accuracy and computationally intensive. The simulation results showed the robustness, efficiency, and superiority of the proposed scheme compared to the classical method even in low speed region.

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“…Although this approach is a good way to clear the noise in estimation, the computational cost might be problematic for cases where fast response in estimation is desired. Another study, which relies on the use of Extended Kalman Filter, implements the velocity estimation Paper template for Automatika Eray A. Baran, Edin Golubovic, Asif Sabanovic with current and DC voltage inputs of an induction motor [4]. A more recent example of Extended Kalman Filtering on velocity estimation can be found in [15].…”

Section: Introductionmentioning

confidence: 99%

Functional Observers for Motion Control Systems

2013

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Original scientific paperThis paper presents a novel functional observer for motion control systems to provide higher accuracy and less noise in comparison to existing observers. The observer uses the input current and position information along with the nominal parameters of the plant and can observe the velocity, acceleration and disturbance information of the system. The novelty of the observer is based on its functional structure that can intrinsically estimate and compensate the un-measured inputs (like disturbance acting on the system) using the measured input current. The experimental results of the proposed estimator verifies its success in estimating the velocity, acceleration and disturbance with better precision than other second order observers.Key words: Motion Control, Disturbance Observer, Estimation, Acceleration Observer Funkcijski obzerver za sustave upravljanja gibanjem. U radu je predložen funkcijski obzerver za sustave upravljanja gibanjem koji ostvaruje veću preciznost i manji šum estimacije u usporedbi s postojećim obzerverima. Obzerver estimira brzinu, akceleraciju i poremećaj korištenjem mjerenja ulazne struje, položaja te poznatih nominalnih parametara sustava. Znanstveni doprinos predloženog obzervera je funkcijska struktura koja omogućava intrinzičnu estimaciju i kompenzaciju nemjerljivih ulaznih veličina (kao što je poremećaj koji djeluje na sustav) korištenjem mjerenja ulazne struje. Eksperimentalni rezultati potvrduju kako predloženi obzerver ostvaruje točniju estimaciju brzine, akceleracije i poremećaja u usporedbi s ostalim obzerverima drugog reda.

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Speed sensorless vector control of an induction motor using an extended Kalman filter

Cited by 124 publications

References 13 publications

Electric Machine Topologies in Energy Storage Systems

Electric Machine Topologies in Energy Storage Systems

Neural Adaptive Kalman Filter for Sensorless Vector Control of Induction Motor

Functional Observers for Motion Control Systems

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