Indirect vector control of induction motor using an extended Kalman observer and fuzzy logic controllers

Zahraoui, Yassine; Bennassar, Abderrahim; Akherraz, Mohamed; Essalmi, Adil

doi:10.1109/irsec.2015.7455046

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…The first prediction technique is used in simulations in this paper. The noises are assumed white, Gaussian, and uncorrelated with the estimated states [28]. Considering the process noise w and the measurement noise v, the dynamic behaviour of the induction motor can be given by the following system:…”

Section: Extended Kalman Filter Algorithm For Induction Motor State Observationmentioning

confidence: 99%

A Comparative Study of Nonlinear Control Schemes for Induction Motor Operation Improvement

Zahraoui

Akherraz

Ma’arif

2021

IJRCS

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In the objective of improving the performance of induction motor operation and ensuring a robust control against different uncertainties and external disturbances, especially at low-speed regions, this research highlights the main features of two nonlinear control techniques. First, the control design is based on the backstepping approach (BSA) with integral action, and then the sliding mode control (SMC) theory. The BSA principle is to define successive causal relations in order to construct the control law in a recursive and systematic way. This allows overcoming the obstacle of the higher-order system's dimension. SMC is designed to drive and then constrain the system state to lie within a neighborhood of the switching surface, this provides very strong and inherent robustness to the resulting controllers. The main reason behind developing the nonlinear control techniques is to ensure a decoupled control of the machine. Besides, it guarantees the stability of the overall system by tracking the speed reference with the fewest static error. Moreover, as the sensorless control increases the reliability and decreases the cost of the control system, an extended Kalman filter is implemented to improve speed and flux observation. The simulations of all the discussed results have been obtained by MATLAB/Simulink.

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Section: Extended Kalman Filter Algorithm For Induction Motor State Observationmentioning

confidence: 99%

A Comparative Study of Nonlinear Control Schemes for Induction Motor Operation Improvement

Zahraoui

Akherraz

Ma’arif

2021

IJRCS

Self Cite

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“…For a DC machine, the field inductor and the induced current are naturally decoupled and orthogonal. In the case of an IM, it is difficult to distinguish the torque-producing current and the flux-producing current, which are strongly coupled [13,14].The idea of the oriented field is to choose a two-phase rotating axis system (d, q) mounted on the rotor, stator or air gap, allowing decoupling the torque and the flux. is the most widely used because it eliminates the influence of rotor and stator leakage reactance and gives better results than methods based on stator flux or gap orientation [11].…”

Section: Direct Field Oriented Control Based Backstepping Approach 31 Main Objective Of Vector Controlmentioning

confidence: 99%

Induction motor harmonic reduction using space vector modulation algorithm

et al. 2020

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The vector control was proposed as an alternative to the scalar control for AC machines control. Vector control provide high operation performance in steady state and transient operation. However, the variable switching frequency of vector control causes high flux and torque ripples which lead to an acoustical noise and degrade the performance of the control scheme. The insertion of the space vector modulation was a very useful solution to reduce the high ripples level inspite of its complexity. Numerical simulation results obtained in MATLAB/Simulink show the good dynamic performance of the proposed vector control technique and the effectiveness of the proposed sensorless strategy in the presence of the sudden load torque basing on the integral backstepping approach capabilities on instant perturbation rejection.

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“…The previous model of the induction machine must be discretized for the implementation of the extended Kalman filter. If quasi-constant control voltages are assumed over a sampling period T s as in reference [18], the discrete augmented state model can be approximated by:…”

Section: Discretization Of the Continuous Modelmentioning

confidence: 99%

Improvement of Induction Motor State Observation: Extended Kalman Filter Versus Adaptive Luenberger Observer

Zahraoui

Akherraz

Elbadaoui

2020

Wseas Transactions on Systems and Control

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the sensor-less control is a major issue in control domain. The use of sensors has several downsides like high cost, fragility and low reliability. Furthermore, the physical environment sometimes, does not allow to use sensors. Due to the multiple variables and non-linearity of induction motor dynamics, the estimation of the rotor speed and flux without the measurement is still a very challenging subject. The main objective of this paper is to present a comparative study between two observer’s structures, a full order adaptive observer based on Lyapunov theory and inherently extended Kalman filter which did not take the speed as adaptive quantity. Both of them are linked to a vector controlled induction motor drive. The effectiveness and estimation accuracy is investigated in detail by simulation results.

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Indirect vector control of induction motor using an extended Kalman observer and fuzzy logic controllers

Cited by 12 publications

References 9 publications

A Comparative Study of Nonlinear Control Schemes for Induction Motor Operation Improvement

A Comparative Study of Nonlinear Control Schemes for Induction Motor Operation Improvement

Induction motor harmonic reduction using space vector modulation algorithm

Improvement of Induction Motor State Observation: Extended Kalman Filter Versus Adaptive Luenberger Observer

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