Optimization of Delayed-State Kalman-Filter-Based Algorithm via Differential Evolution for Sensorless Control of Induction Motors

Salvatore, N.; Caponio, Andrea; Neri, Ferrante; Stasi, S.; Cascella, Giuseppe Leonardo

doi:10.1109/tie.2009.2033489

Cited by 112 publications

(32 citation statements)

References 24 publications

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“…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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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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“…In the observer based system [16,17,22,23,25] error quantity worked as input to the adaptation mechanism of the stator resistance determination using difference between the measured and observed current signal. In this field extended kalman filter is mostly used because of its robustness and also it requires less number of PI controllers [26,27]. In model reference adaptive system (MRAS) [14,15,18,21,24,28] error quantity selection is more diverse.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Experimental Verification of ANN Based Online Stator Resistance Estimation in DTC-IM Drive

Reza¹,

Islam²,

Mekhilef³

2014

Journal of Electrical Engineering and Technology

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-Direct Torque controlled induction motor (DTC-IM) drives use stator resistance of the motor for stator flux estimation. So, stator resistance estimation properly is very important for a stable and effective operation of the induction motor. Stator resistance variations because of changing in temperature make DTC operation difficult mainly at low speed. A method based on artificial neural network (ANN) to estimate the stator resistance online of IM for DTC drive is modeled and verified in this paper. To train the neural network a back propagation algorithm is used. Weight adjustment of neural network is done by back propagating the error signal between measured and estimated stator current. An extensive simulation has been carried out in MATLAB/SIMULINK to prove the efficacy of the proposed stator resistance estimator. The simulation & experimental result reveals that proposed method is able to obtain precise torque and flux control at low speed.

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“…For this reason, DE has been used in many real-world applications ranging from control engineering, see [3], to image processing, see [4]. One of the first application domains for DE has been signal processing and more specifically the design of a digital filter, see [5].…”

Section: Introductionmentioning

confidence: 99%

Differential evolution schemes for speech segmentation: A comparative study

Iliya

Neri

Menzies

et al. 2014

2014 IEEE Symposium on Differential Evolution (SDE)

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Abstract-This paper presents a signal processing technique for segmenting short speech utterances into unvoiced and voiced sections and identifying points where the spectrum becomes steady. The segmentation process is part of a system for deriving musculoskeletal articulation data from disordered utterances, in order to provide training feedback. The functioning of the signal processing technique has been optimized by selecting the parameters of the model. The optimization has been carried out by testing and comparing multiple Differential Evolution implementations, including a standard one, a memetic one, and a controlled randomized one. Numerical results have also been compared with a famous and efficient swarm intelligence algorithm. For the given problem, Differential Evolution schemes appear to display a very good performance as they can quickly reach a high quality solution. The binomial crossover appears, for the given problem, beneficial with respect to the exponential one. The controlled randomization appears to be the best choice in this case. The overall optimized system proved to segment well the speech utterances and efficiently detect its uninteresting parts.

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Optimization of Delayed-State Kalman-Filter-Based Algorithm via Differential Evolution for Sensorless Control of Induction Motors

Cited by 112 publications

References 24 publications

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

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

Modeling and Experimental Verification of ANN Based Online Stator Resistance Estimation in DTC-IM Drive

Differential evolution schemes for speech segmentation: A comparative study

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