Sensorless Control of Induction-Motor Drive Based on Robust Kalman Filter and Adaptive Speed Estimation

Alonge, F.; D’Ippolito, Filippo; Sferlazza, Antonino

doi:10.1109/tie.2013.2257142

Cited by 130 publications

(71 citation statements)

References 24 publications

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“…All the model based schemes were sensitive to variations or incorrect settings of parameters. During the earlier stages, extended kalman filter (EKF) based estimators [3][4][5] were widely used for speed estimation, but they give accurate results only if the system dynamics are linearized and had an inherent disadvantage of a high sampling frequency and were computationally expensive. Estimators based on model reference adaptive systems (MRAS), extended luenberger observers (ELO) and sliding mode observers (SMO) [6,7] had a wider utility and were more extensively used owing to ease of use and flexibility.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Analysis of a Modified State Observer for Sensorless Induction Motor Drive Used in Electric Vehicle Applications

Krishna¹,

Daya

Padmanaban

et al. 2017

Energies

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Abstract:The purpose of this work is to present an adaptive sliding mode Luenberger state observer with improved disturbance rejection capability and better tracking performance under dynamic conditions. The sliding hyperplane is altered by incorporating the estimated disturbance torque with the stator currents. In addition, the effects of parameter detuning on the speed convergence are observed and compared with the conventional disturbance rejection mechanism. The entire drive system is first built in the Simulink environment. Then, the Simulink model is integrated with real-time (RT)-Lab blocksets and implemented in a relatively new real-time environment using OP4500 real-time simulator. Real-time simulation and testing platforms have succeeded offline simulation and testing tools due to their reduced development time. The real-time results validate the improvement in the proposed state observer and also correspond to the performance of the actual physical model.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Analysis of a Modified State Observer for Sensorless Induction Motor Drive Used in Electric Vehicle Applications

Krishna¹,

Daya

Padmanaban

et al. 2017

Energies

View full text Add to dashboard Cite

show abstract

“…During the earlier stages, Extended Kalman Filter (EKF) based estimators [3][4][5] were widely used for speed estimation, but they give accurate results only if the system dynamics are linearized and had an inherent disadvantage of a high sampling frequency and were computationally expensive. Estimators based on Model Reference Adaptive Systems (MRAS), Extended Luenberger Observers (ELO) and Sliding Mode Observers (SMO) [6][7] had a wider utility and were more extensively used owing to ease of use and flexibility.…”

Section: Introductionmentioning

confidence: 99%

Real-time Analysis of a Modified State Observer for Sensorless Induction Motor Drive used in Electric Vehicle Applications

Srinivasan¹,

L²,

Padmanaban³

et al. 2017

Preprint

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Abstract:The purpose of this work is to present an adaptive sliding mode luenberger state observer with improved disturbance rejection capability and better tracking performance under dynamic conditions. The sliding hyperplane is altered by incorporating the estimated disturbance torque with the stator currents. Also, the effects of parameter detuning on the speed convergence is observed and compared with the conventional disturbance rejection mechanism. The entire drive system is first built in simulink environment. Then, the simulink model is integrated with RT-Lab blocksets and implemented in a relatively new real-time environment using OP4500 real-time simulator. Real-time simulation and testing platforms have succeeded offline simulation and testing tools due to their reduced development time. The real-time results validate the improvement in the proposed state observer and also correspond to the performance of the actual physical model.

show abstract

“…The extended Kalman filter (EKF) has been successfully implemented as a state observer for induction motor (IM) drives in various areas [1][2][3][4][5][6][7][8]. However, EKF needs to calculate the nonlinear equation of the Jacobian matrix, which is sub-optimal and can easily lead to divergence.…”

Section: Introductionmentioning

confidence: 99%

State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF

Shi

et al. 2016

Energies

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This paper focuses on an improved square root unscented Kalman filter (SRUKF) and its application for rotor speed and position estimation of permanent magnet synchronous motor (PMSM). The approach, which combines the SRUKF and strong tracking filter, uses the minimal skew simplex transformation to reduce the number of the sigma points, and utilizes the square root filtering to reduce computational errors. The time-varying fading factor and softening factor are introduced to self-adjust the gain matrices and the state forecast covariance square root matrix, which can realize the residuals orthogonality and force the SRUKF to track the real state rapidly. The theoretical analysis of the improved SRUKF and implementation details for PMSM state estimation are examined. The simulation results show that the improved SRUKF has higher nonlinear approximation accuracy, stronger numerical stability and computational efficiency, and it is an effective and powerful tool for PMSM state estimation under the conditions of step response or load disturbance.

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Sensorless Control of Induction-Motor Drive Based on Robust Kalman Filter and Adaptive Speed Estimation

Cited by 130 publications

References 24 publications

Real-Time Analysis of a Modified State Observer for Sensorless Induction Motor Drive Used in Electric Vehicle Applications

Real-Time Analysis of a Modified State Observer for Sensorless Induction Motor Drive Used in Electric Vehicle Applications

Real-time Analysis of a Modified State Observer for Sensorless Induction Motor Drive used in Electric Vehicle Applications

State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF

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