Adaptive Field-Oriented Control Using Supervisory Type-2 Fuzzy Control for Dual Star Induction Machine

Rahali, Hilal; Zeghlache, Samir; Benalia, Leila

doi:10.22266/ijies2017.0831.04

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…DFIM comprises three stator coils and three rotor coils offset by an identical distribution angle. Vectors equations of voltages, currents and total stator and rotor fluxes are given as follow [6]:…”

Section: Mathematical Machine Modelmentioning

confidence: 99%

Speed estimation of a Doubly Fed Induction Machine controlled by a Field Oriented Control Strategy

Djellouli

Boucherit

2022

ENPESJ

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This paper studies the estimation problem of speed in a Doubly Fed Induction Machine (DFIM) controlled by a Field Oriented Control (FOC) Strategy. The DFIM is the most responsive in variable speed. The chosen configuration uses two voltage inverters connected to the stator and rotor windings, to adopt the power distribution between them through the pulses distribution of the stator and the rotor in motor operating mode. It is necessary to model the DFIM in three-phase equations and then in two-phase equations faithfully representing the characteristics of the machine. From this model, we can design and simulate the control. The control by oriented rotor flux can be realized by using the speed provided by sensors or estimators. In this paper, we have used the Extended Kalman Filter (EKF) in order to avoid problems caused by the motor speed sensor and improve the robustness of the control and its performance without using any speed sensor.

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Section: Mathematical Machine Modelmentioning

confidence: 99%

Speed estimation of a Doubly Fed Induction Machine controlled by a Field Oriented Control Strategy

Djellouli

Boucherit

2022

ENPESJ

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“…To solve the problem of 𝜑 1 (x 1 , 𝑘) and 𝜑 2 (x 1 , x 2 , 𝑘) , we approximate them by a two-interval type-2 fuzzy adaptive systems. A fuzzy system that uses fuzzy type 2 sets and inference is a fuzzy type 2 system [10][11][12][13][14][15][16]. Type-1 fuzzy set has a crisp membership degree, while type-2 fuzzy set (T2FS) has a fuzzy membership degree.…”

Section: Interval Type-2 Fuzzy Adaptive Controlmentioning

confidence: 99%

Novel Hybrid Interval Type-2 Fuzzy Adaptive Backstepping Control for a Class of Uncertain Discrete-Time Nonlinear Systems

Boukhalfa¹,

Khettab²,

Essounbouli³

2021

JESA

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A Novel hybrid backstepping interval type-2fuzzy adaptive control (HBT2AC) for uncertain discrete-time nonlinear systems is presented in this paper. The systems are assumed to be defined with the aid of discrete equations with nonlinear uncertainties which are considered as modeling errors and external unknown disturbances, and that the observed states are considered disturbed. The adaptive fuzzy type-2 controller is designed, where the fuzzy inference approach based on extended single-input rule modules (SIRMs) approximate the modeling errors, non-measurable states and adjustable parameters are estimated using derived weighted simplified least squares estimators (WSLS). We can prove that the states are bounded and the estimation errors stand in the neighborhood of zero. The efficiency of the approach is proved by simulation for which the root mean squares criteria are used which improves control performance.

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“…The fuzzy logic control based on Type 2 Fuzzy Sets (T2-FS) present a suitable solution for the control of non-linear systems [29]. T2-FS were first proposed in [33][34][35] as an extension to Type 1 Fuzzy Sets (T1-FS), to increase the fuzziness of the relations. Several studies have shown the high performance of T2-FLC, which uses the T2-FS interval instead of Type 1 FLC (T1-FLC) in terms of handling the uncertainties [17,28,34].…”

Section: Type 2 Fuzzy Logic Controllermentioning

confidence: 99%

Evaluation of the High Performance Indirect Field Oriented Controlled Dual Induction Motor Drive Fed by a Single Inverter using Type-2 Fuzzy Logic Control

Bounab

Chaiba

Belkacem

2020

Eng. Technol. Appl. Sci. Res.

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In this paper, a high-performance indirect field-oriented controlled dual Induction Motor (IM) drive fed by a single inverter using type-2 fuzzy logic control will be presented. At first, the mathematical model of the IM is implemented in the d-q reference frame. Then, the speed control of the Dual Induction Motor (DIM) operating in parallel configuration with Indirect Field Oriented Control (IFOC) using PI and type-2 Fuzzy Logic Controller (T2-FLC) will be presented. For the control of this system, a DC supply and a Space Vector Pulse Width Modulation (SVPWM) voltage source inverter are introduced with constant switching frequency. Also, the performance of T2-FLC, which is based on the IFOC, is tested and compared to those achieved using the PI controller. The simulation results demonstrate that the T2-FLC is more robust, efficient, and has superior dynamic performance for traction system applications.

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Adaptive Field-Oriented Control Using Supervisory Type-2 Fuzzy Control for Dual Star Induction Machine

Cited by 10 publications

References 24 publications

Speed estimation of a Doubly Fed Induction Machine controlled by a Field Oriented Control Strategy

Speed estimation of a Doubly Fed Induction Machine controlled by a Field Oriented Control Strategy

Novel Hybrid Interval Type-2 Fuzzy Adaptive Backstepping Control for a Class of Uncertain Discrete-Time Nonlinear Systems

Evaluation of the High Performance Indirect Field Oriented Controlled Dual Induction Motor Drive Fed by a Single Inverter using Type-2 Fuzzy Logic Control

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