State Estimation of Induction Motor Drives Using the Unscented Kalman Filter

Jafarzadeh, Saeed; Lascu, C.; Fadali, M. Sami

doi:10.1109/tie.2011.2174533

Cited by 152 publications

(76 citation statements)

References 21 publications

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“…In the ESC system with high real-time requirements, the number of sigma sampling points is n+2. The weights equations are shown as the following equations [8]: …”

Section: A Initialization Part Of Improved Ukfmentioning

confidence: 99%

Research on Soft Measurement of Electronic Stability Control for Electric-wheel Vehicle

Wang¹,

Song²

2015

Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

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Abstract-In order to enhance the timeliness and accuracy of the soft measurement method of the ES C for electricwheel vehicle, an improved soft measurement algorithm was designed. The improved algorithm was built based on an improved UKF. The improved UKF sampling strategy used scaled minimal skew sampling method to speed up the system sampling rate. The sigma points of the improved UKF were sampling two times to enhance the accuracy. The three degree of freedom vehicle model was built to establish vehicle dynamic relationship. According to the three degree of freedom vehicle model, the vehicle measurement model was built. The verification experiments of improved soft measurement algorithm were made based on the electricwheel vehicle ES C test platform. The experiments included a step input experiment in 70 kilometers per hour and a step input experiment in 110 kilometers per hour. From the experimental results, it could be seen that the estimated value of yaw velocity and sideslip angle tracked the actual very well. The overshoot of the tracking process was small and the lag time was short. The improved soft measurement algorithm was fit for the ES C of electric-wheel vehicle.

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“…In the ESC system with high real-time requirements, the number of sigma sampling points is n+2. The weights equations are shown as the following equations [8]: …”

Section: A Initialization Part Of Improved Ukfmentioning

confidence: 99%

Research on Soft Measurement of Electronic Stability Control for Electric-wheel Vehicle

Wang¹,

Song²

2015

Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

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“…A previous work summarizes the scaling parameters and explains how to select and perform a simple comparison simulation of basic UT (α = 1, β = 0, κ = 0), general UT (α = 1, β = 0, κ = 0.1-0.9). Several experimental results for various types of UT, such as basic UT and general UT (κ = 1), have been presented [11]. The application of a UKF with basic UT having the parameter β (α = 1, β = 2, κ = 0) was reported [12].…”

Section: Introductionmentioning

confidence: 99%

Reduced-Order Unscented Kalman Filter for Sensorless Control of Permanent-Magnet Synchronous Motor

Moon¹,

Kwon²

2017

Journal of Electrical Engineering and Technology

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-The unscented Kalman filter features a direct transforming process involving unscented transformation for removing the linearization process error that may occur in the extended Kalman filter. This paper proposes a reduced-order unscented Kalman filter for the sensorless control of a permanent magnet synchronous motor. The proposed method can reduce the computational load without degrading the accuracy compared to the conventional Kalman filters. Moreover, the proposed method can directly estimate the electrical rotor position and speed without a back-electromotive force. The proposed Kalman filter for the sensorless control of a permanent magnet synchronous motor is verified through the simulation and experimentation. The performance of the proposed method is evaluated over a wide range of operations, such as forward and reverse rotations in low and high speeds including the detuning parameters.

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“…It is called the unscented Kalman filter (UKF). The linearization process often involves error between the linearized model and the system model [10][11]. The UKF observer has previously been introduced [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…There are two kinds of the UT methods: basic UT (BUT) and general UT (GUT) [11]. The BUT and GUT can be distinguished by scaling parameter value.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Speed Control of Permanent Magnet Synchronous Motor by Unscented Kalman Filter using Various Scaling Parameters

Moon¹,

Kwon²

2016

Journal of Electrical Engineering and Technology

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-This paper investigates the application, design and implementation of unscented Kalman filter observer using the various scaling parameters for the sensorless speed control of a permanent magnet synchronous motor. The principles of unscented transformation and unscented Kalman filter are examined and their applications are explained. Typically the mapping transformation process is divided into two types, namely the basic unscented transformation and the general unscented transformation by virtue of the scaling parameter value. And resultantly, the number of sampling points, weights, code configuration and computation time are different. But there is no little information on the scaling parameter value or how this value influences the system performance. To analyze the unscented transformation with the various scaling parameters in this study, the experimental results under a wide range of operation condition have been demonstrated.

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State Estimation of Induction Motor Drives Using the Unscented Kalman Filter

Cited by 152 publications

References 21 publications

Research on Soft Measurement of Electronic Stability Control for Electric-wheel Vehicle

Research on Soft Measurement of Electronic Stability Control for Electric-wheel Vehicle

Reduced-Order Unscented Kalman Filter for Sensorless Control of Permanent-Magnet Synchronous Motor

Sensorless Speed Control of Permanent Magnet Synchronous Motor by Unscented Kalman Filter using Various Scaling Parameters

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