Novel neural network based speed estimator for multilevel inverter fed sensorless field oriented controlled IM drive

  • A. VenkadesanEmail author
  • K. Sedhuraman
Original Paper


In this paper, a new reference model adaptive system based speed estimator is proposed for multilevel inverter fed sensorless field oriented controlled induction motor drive. The proposed scheme employs 2-neural network models which is novel in this paper. The proposed speed estimator is shown to overcome low speed problems. One of the FPGA implementation issues of NN model is the identification of appropriate bit precision. The performance of reference NN estimator is studied for various bit precision and the suitable bit precision is identified compromising the memory size (resource) and the accuracy. Further the suitability of multicarrier pulse width modulation techniques for multilevel inverter is also studied. The performance is compared in terms of magnitude of the output voltage and total harmonics distortion and suitable modulation technique is identified. The encouraging results obtained are presented.


Neural network based speed estimator Multilevel inverter Modulation techniques Induction motor drives Sensorless drives 

List of symbols

\( {\text{v}}^{\text{S}}_{\text{ds}} ,{\text{v}}^{\text{S}}_{\text{qs}} \)

d-Axis Stator voltage, q-axis Stator voltage

\( {\text{i}}^{\text{S}}_{\text{ds}} ,{\text{i}}^{\text{S}}_{\text{qs}} \)

d-Axis Stator current, q-axis stator current

\( {{\varphi }}^{\text{S}}_{\text{dr}} ,{{\varphi }}^{\text{S}}_{\text{qr}} \)

d-Axis Rotor flux, q-axis rotor flux

\( {\text{R}}_{\text{s}} ,{\text{R}}_{\text{r}} \)

Stator resistance, rotor resistance

\( {\text{L}}_{\text{s}} ,{\text{L}}_{\text{r}} \)

Stator inductance, rotor inductance

\( {\text{L}}_{\text{m}} \)

Magnetization inductance

\( {\text{T}}_{\text{s}} \)

Sampling time

\( {{\omega }}_{\text{r}} \)

Rotor speed (rad/s)

Superscript s

Stationary reference frame

Superscript e

Synchronous reference frame


 \( {1 - \text{ (T}}_{\text{s}} / {\text{T}}_{\text{r}} ) \)


\({{\omega }}_{{\text{r}}} {\text{T}}_{{\text{s}}}\) 


\({\text{ (L}}_{{\text{m}}} {\text{T}}_{\text{s}} ) / {\text{T}}_{\text{r}} \) 



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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.EEE DepartmentNIT PuducherryKaraikalIndia
  2. 2.EEE DepartmentMVIT PuducherryKaraikalIndia

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