High Performance of Self Scheduled Linear Parameter Varying Control with Flux Observer of Induction Motor

In this paper, a novel rotor speed estimation method using model reference adaptive system (MRAS) is proposed to improve the performance of a sensorless vector control in the very low and zero speed regions. In the classical MRAS method, the rotor flux of the adaptive model is compared with that of the reference model. The rotor speed is estimated from the fluxes difference of the two models using adequate adaptive mechanism. However, the performance of this technique at low speed remains uncertain and the MRAS loses its efficiency, but in the new MRAS method, two differences are used at the same time. The first is between rotor fluxes and the second between electromagnetic torques. The adaptive mechanism used in this new structure contains two parallel loops having Proportional-integral controller and low-pass filter. The first and the second loops are used to adjust the rotor flux and electromagnetic torque. To ensure good performance, a robust vector control using sliding mode control is proposed. The controllers are designed using the Lyapunov approach. Simulation and experimental results show the effectiveness of the proposed speed estimation method at low and zero speed regions, and good robustness with respect to parameter variations, measurement errors, and noise is obtained. terests include linear and nonlinear control theory, including sliding mode control, adaptive control, and robust control, with applications to electric drive and mechatronics systems.Mohammed Ouriagli received the Ph.D. degree in electrical engineering from the Institut National Polytechnique de Lorraine, Lorraine, France, in 1995.Since 2003, he has held a teaching position in automatic control in the Polydisciplinary Faculty of Taza, Université Sidi Mohamed Ben-Abdellah (USMBA), Taza, Morroco. His research interests mainly include linear and nonlinear control theory, including sliding mode control, adaptive control, robust control, electric drive applications, and mechatronics systems.

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“…The dynamic equation of the estimation errorē φ = (φ r −φ r ) is obtained by subtracting (23) and (24) …”

Section: A Classical Mras Speed Observermentioning

confidence: 99%

“…whereφ r is given by (24). It is well established that the motion equation (2) governs the mechanical dynamics part of the machine, then a variation of the load results in a variation of the speed until the electromagnetic torque becomes equal to the load torque.…”

Section: B New Mras Speed Observermentioning

confidence: 99%

Implementation of a New MRAS Speed Sensorless Vector Control of Induction Machine

Benlaloui

Drid

Chrifi‐Alaoui

et al. 2015

IEEE Trans. Energy Convers.

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161

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“…The temporal evolution of φ rd is directly linked to the variation of the operating point of the slow subsystem defined by the torque amplitude (C m ). In this case, the dynamic controllers based on the LPV system theory taking into account the variation of the rotor flux, rotor speed and rotor resistance, respectively, for torque control and flux estimation [16][17][18] is an alternative to robust control with fixed controller parameters in induction motor control [20].…”

Section: Introductionmentioning

confidence: 99%

Linear parameter varying sensorless torque control for singularly perturbed induction motor with torque and flux observers

Khamari¹,

Benlaloui²,

Ouchen³

et al. 2020

Electr Eng

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In this paper, a new approach being different from the concept of DTC and IFOC for a robust torque control design for induction motor is addressed. The design is based on the framework of singularly perturbed system theory and linear varying parameter systems. In these systems, the rotor flux is considered to be a time-varying parameter in order to guarantee a robust torque control with LPV flux observer with respect to the speed and resistance variations. In fact, this observer is designed to estimate the rotor flux as well as an MRAS observer is introduced to estimate the mechanical speed and rotor resistance. The main feature of this proposed structure is the enhancement of robustness with flux, speed and rotor resistance variation. This improvement leads to a considerable decrease of the torque ripples and ensures the stability for the entire operating range. The obtained simulations and experimental results are used to validate the effectiveness of the proposed control strategy. Keywords H ∞ LPV controller • Singularly perturbed system • LMI • MRAS observer • Induction motor Abbreviations Rotor speed (rd/s) ω s Stator current frequency (rd/s) ωr Induced rotor current frequency (rd/s) ωc Injected rotor current frequency (rd/s) J In Inertia f Coefficient of viscous C m Maximal electromagnetic torque ∼ Symbol indicating measured valuê Symbol indicating the estimated value B Sabir Ouchen

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“…Since the flux observer is basically described by the motor equations, variations in parameters inevitably lead to flux estimation error. These limitations provide an ideal scenario for the design of different observers such as: LPV flux observer [3][4], [6] kalman filter (KF), and Model Reference Adaptive System (MRAS) .... etc. In this paper a comparison of performances and characteristics of the LPV flux observer and observation algorithms based on Kalman Filter is presented, using both tension and current corrupted by noise measurements.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of rotor flux estimation in induction motor with a linear parameter varying observer and Kalman Filter

Benlalaoui

Drid

Chrifi‐Alaoui

et al. 2014

2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

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The paper presents a comparison of performances and characteristics of a Self Scheduled linear parameter varying (LPV) Flux Observer and observation algorithms based on Kalman Filter for induction motor rotor flux estimation, inserted in a field oriented control scheme. The construction of the considered algorithms is described in detail, and the different design issues are explained. For true comparison the same measurements are assumed available to both deterministic and stochastic estimators, and the same controller parameters are used in simulation. The performances of the estimators are compared either in terms of observation errors during transient and steady state operations either in terms of robustness face to variations of motor parameters.

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High Performance of Self Scheduled Linear Parameter Varying Control with Flux Observer of Induction Motor

Cited by 10 publications

References 19 publications

Implementation of a New MRAS Speed Sensorless Vector Control of Induction Machine

Implementation of a New MRAS Speed Sensorless Vector Control of Induction Machine

Linear parameter varying sensorless torque control for singularly perturbed induction motor with torque and flux observers

A comparative study of rotor flux estimation in induction motor with a linear parameter varying observer and Kalman Filter

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