Feedback linearization based sensorless direct torque control using stator flux MRAS-sliding mode observer for induction motor drive

Ammar, Abdelkarim; Kheldoun, Aissa; Metidji, Brahim; Ameid, Tarek; Azzoug, Younes

doi:10.1016/j.isatra.2019.08.061

Cited by 48 publications

(35 citation statements)

References 30 publications

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“…The rotor speed estimation and the tracking are shown Figure 9a, where it can be observed that the rotor speed, estimated by the ANN estimation scheme, was well achieved, and the rotor speed was tracking its reference. It can be observed for the speed response in Figure 9a, a zero steady-state error, while a small steady-state error was observed in the speed tracking in reference [24], using the MRAS-sliding mode observer. Furthermore, the speed tracking occurred without an overshoot, compared to the result in reference [15], where a small overshoot was seen at the step transition as the speed estimation depended on the model parameters.…”

Section: Resultsmentioning

confidence: 83%

“…The concern was that these matrices depended on the system parameters, and there was no discussion about the estimation robustness to parametric uncertainties. In reference [24], a sliding mode observer was used in the MRAS scheme for stator flux estimation, whereas the speed was estimated using a PI function of an error defined in terms of the stator flux, stator current and machine parameters. Although the stability was guaranteed using this estimation scheme, there was no discussion about the robustness against parametric variations.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid

Tanvir

Merabet

2020

Energies

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This paper presents an improved estimation strategy for the rotor flux, the rotor speed and the frequency required in the control scheme of a standalone wind energy conversion system based on self-excited three-phase squirrel-cage induction generator with battery storage. At the generator side control, the rotor flux is estimated using an adaptive Kalman filter, and the rotor speed is estimated based on an artificial neural network. This estimation technique enhances the robustness against parametric variations and uncertainties due to the adaptation mechanisms. A vector control scheme is used at the load side converter for controlling the load voltage with respect to amplitude and frequency. The frequency is estimated by a Kalman filter method. The estimation schemes require only voltage and current measurements. A power management system is developed to operate the battery storage in the DC-microgrid based on the wind generation. The control strategy operates under variable wind speed and variable load. The control, estimation and power management schemes are built in the MATLAB/Simulink and RT-LAB platforms and experimentally validated using the OPAL-RT real-time digital controller and a DC-microgrid experimental setup.

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Section: Resultsmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid

Tanvir

Merabet

2020

Energies

View full text Add to dashboard Cite

show abstract

“…Virtually any of the previously mentioned control systems of an induction machine can be implemented as sensorless. Rotor speed estimation systems are built, among others, with the use of flux observers, which are equipped with an adaptive mechanism, creating Model Reference Adaptive Systems (MRAS) [17][18][19]. The adaptation mechanism reconstructs the speed based on the estimated flux signals or the fluxes and the measured stator winding currents.…”

Section: Introductionmentioning

confidence: 99%

Developing Induction Motor State Observers with Increased Robustness

Białoń¹,

Pasko²,

Niestrój³

2020

Energies

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This paper presents the results of recently conducted research on Luenberger observers with non-proportional feedbacks. The observers are applied for the reconstruction of magnetic fluxes of an induction motor. Structures of the observers known from the control theory are presented. These are a proportional observer, a proportional-integral observer, a modified integral observer, and an observer with additional integrators. The practical application of some of these observers requires modifications to their structures. In the paper, the simulation results for all mentioned types of observers are presented. The simulations are performed with a Scilab-Xcos model which is attached to this paper. The problem of gains selection of the observers is discussed. Gains are selected with the described optimization method based on a genetic algorithm. A Scilab file launching the genetic algorithm also is attached to this paper.

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“…In the input-output linearization control, the coordinates of fieldoriented control are changed to a new coordinate system and by applying state feedback it is possible to decouple the speed control from the flux control [17][18][19]. In [20], a feedback linearization control algorithm with a sliding mode observer has been implemented for the direct torque control of the induction motor.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Formulation of Feedback Linearizing Control of Doubly Fed Induction Generator Including Magnetic Saturation Effects

Jose

Chattopadhyay

2020

Mathematical Problems in Engineering

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This paper proposes a control methodology based on feedback linearization for a doubly fed induction generator (DFIG) incorporating the magnetic saturation. The feedback linearization algebraically converts a nonlinear system model into a linear model, allowing the use of linear control techniques. Feedback linearization control depends on the model of the system and is therefore sensitive to parameter variations. The doubly fed induction generator (DFIG) operating under the magnetic saturation conditions results in the nonlinear variation of magnetizing inductance, which affects the performance of the control algorithm. From this stand point, on the basis of the dynamic model of the doubly fed induction generator considering magnetic saturation, the feedback linearizing control technique has been formulated. The mathematical model of the doubly fed induction generator, integrating the magnetic saturation has been formulated in the stator flux-oriented reference frame with rotor current and stator magnetizing current as state variables. Simulation studies demonstrate that the inclusion of magnetic saturation in the feedback linearization control of the doubly fed induction generator model increases its accuracy and results in a more efficient and reliable synthesis of the control algorithm.

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Feedback linearization based sensorless direct torque control using stator flux MRAS-sliding mode observer for induction motor drive

Cited by 48 publications

References 30 publications

Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid

Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid

Developing Induction Motor State Observers with Increased Robustness

Mathematical Formulation of Feedback Linearizing Control of Doubly Fed Induction Generator Including Magnetic Saturation Effects

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