A review on MRAS-type speed estimators for reliable and efficient induction motor drives

Korzonek, Mateusz; Tarchała, Grzegorz; Orłowska-Kowalska, Teresa

doi:10.1016/j.isatra.2019.03.022

Cited by 84 publications

(40 citation statements)

References 57 publications

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“…It is also more sensitive to variations in IM parameters, which can significantly lead to inaccuracy of speed estimation under unobservable conditions at zero or low speed region. Another important challenge of this technique is its instability under regenerating conditions [17]. Furthermore, it fails to offer satisfactory compromise between the robustness to measurement noise and estimation bandwidth [20].…”

Section: Introductionmentioning

confidence: 99%

“…Model Reference Adaptive System (MRAS) [21] based speed estimation method is one of many promising techniques used for sensor-less IM drives, due to its easy implementation, robustness and low computational complexity [17,19,21]. However, this technique has some drawbacks, such as complexity in the design of the adaptation mechanism block and sensitivity to uncertainty in the reference model [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive non-linear high gain observer based sensorless speed estimation of an induction motor

Kadrine

Tir

Malik

et al. 2020

Journal of the Franklin Institute

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive non-linear high gain observer based sensorless speed estimation of an induction motor

Kadrine

Tir

Malik

et al. 2020

Journal of the Franklin Institute

View full text Add to dashboard Cite

show abstract

“…Although the stability was guaranteed using this estimation scheme, there was no discussion about the robustness against parametric variations. Similar comments on the study in reference [25], where the errors in the adaptation mechanism were based on the machine models that relied on the system parameters. In reference [26], the adaptive model was based on the estimated speed, found from the PI function, using the error between the reference model and the adaptive model.…”

Section: Introductionmentioning

confidence: 72%

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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“…On the other hand, eliminating the speed-sensor in electric drive systems, namely speed-sensorless control, reduce cost, hardware complexity, and maintenance requirements while increasing the reliability of the electric drive system. For this purpose, many model-based estimators/observers have been proposed in the literature, such as model reference adaptive systems [19,20], full-order observers [21,22], extended Luenberger observers [23,24], extended and unscented Kalman filters (EKF and UKF) [25][26][27], and sliding mode observers [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications

Zerdali¹,

Demir²

2021

Turk J Elec Eng & Comp Sci

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Nowadays, the global trend is towards reducing CO2 emissions and one solution is to replace internal combustion vehicles with electric vehicles. To this end, electric drive system, the most crucial part of an electric vehicle, has gained importance and has become a major research field. The induction motor (IM) is one of the best candidates for electric vehicle applications due to its advantages such as simple and robust design, low cost and maintenance requirements, the ability to operate in harsh environments. However, it has a highly nonlinear model with time-varying electrical and mechanical parameters making them difficult to control. Finite control set-predictive torque control (FCS-PTC) is an inherently suitable and a promising control method for the IM because FCS-PTC is easy to implement and has the ability to handle nonlinearities with the inclusion of constraints. In addition, the elimination of speed sensors increases the reliability of electric motor drives while reducing cost and hardware complexity. In this paper, a speedsensorless FCS-PTC based IM drive system is designed in order to combine the aforementioned advantages. Unlike the current literature, to improve the torque response of conventional FCS-PTC, the load torque is also estimated by an adaptive fading extended Kalman filter and is fed back into the torque control loop. The results show that improved control performance is achieved.

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A review on MRAS-type speed estimators for reliable and efficient induction motor drives

Cited by 84 publications

References 57 publications

Adaptive non-linear high gain observer based sensorless speed estimation of an induction motor

Adaptive non-linear high gain observer based sensorless speed estimation of an induction motor

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

Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications

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