Stability Improvement Methods of the Adaptive Full-Order Observer for Sensorless Induction Motor Drive—Comparative Study

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

doi:10.1109/tii.2019.2930465

Cited by 34 publications

(15 citation statements)

References 21 publications

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“…The speed estimation method based on Adaptive Full-order Observer (AFO) from the currents and/or voltages errors, is well recognized in many studies of the speed sensor-less IM drives [14][15][16]. However, the drawback of this technique is its instability when rotor speed is close to zero in the regenerative region, even when the measurement noise and parameter errors are not considered [15]. This method also results in an error between the actual and estimated speeds [14].…”

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

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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

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“…For an ordinary AC motor, speed sensorless technology has been widely studied. The adopted methods include the direct calculation method [29], model reference adaptive system (MRAS) method [30], extended Kalman filter (EKF) method [31], adaptive full order observer [32], the high frequency signal injection (HFSI) method [33], and the sliding mode observation (SMO) method [34]. Regarding the speed/position sensorless technology of a BLM system, some studies have been carried out, most of which refer to the research ideas of an ordinary AC motor.…”

Section: Introductionmentioning

confidence: 99%

Research on the Speed Sliding Mode Observation Method of a Bearingless Induction Motor

Chen

Qiao

2021

Energies

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In order to achieve the speed sensorless control of a bearingless induction motor (BL-IM), a novel sliding mode observation (SMO) method of motor speed is researched. First of all, according to the mathematical model of a BL-IM system, the observation model of stator current and that of rotor flux-linkage are derived. In order to overcome the chattering problem of a sliding mode observer, a continuous saturation function is adopted to replace the traditional sign function. Then, the SMO model of motor speed is derived, and the stability of the proposed motor speed SMO method is validated by the Lyapunov stability theory. At the end, the observed motor speed and rotor flux-linkage are applied to a BL-IM inverse “dynamic decoupling control” (DDC) system. Simulation results show that the real-time observation or dynamic tracking of motor speed and rotor flux-linkage are achieved in a more timely manner and more accurately, and higher steady-state observation accuracy is obtained; the proposed SMO method can be used in the BL-IM’s inverse DDC system to realize reliable magnetic suspension operation control without a speed sensor.

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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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Stability Improvement Methods of the Adaptive Full-Order Observer for Sensorless Induction Motor Drive—Comparative Study

Cited by 34 publications

References 21 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

Research on the Speed Sliding Mode Observation Method of a Bearingless Induction Motor

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

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