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Implementation of feedback-linearization-modelled induction motor drive through an adaptive simplified neuro-fuzzy approach

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Abstract

A simple modified version of neuro-fuzzy controller (NFC) method based on single-input, reduced membership function in conjunction with an intuitive flux–speed decoupled feedback linearization (FBL) approach of induction motor (IM) model is presented in this paper. The proposed NFC with FBL remarkably suppresses the torque and speed ripple and shows improved performance. Further, the modified NFC is tuned by genetic algorithm (GA) approach for optimal performance of FBL-based IM drive. Moreover, the GA searches the optimal parameters of the simplified NFC in order to ensure the global convergence of error. The proposed simplified NFC integrates the concept of fuzzy logic and neural network structure like a conventional NFC, but it has the advantages of simplicity and improved computational efficiency over the conventional NFC as the single input introduced here is an error (speed and torque) instead of two inputs, error and change in error, as in the conventional NFC. This structure makes the proposed NFC robust and simple as compared with conventional NFC and thus, can be easily applied to real-time industry application. The proposed system incorporated with different control methods is also validated with extensive experimental results using DSP2812. The effectiveness of the proposed method using FBL of IM drive is investigated in simulation as well as in experiment with different working modes. It is evident from the comparative results that the system performance is not deteriorated using the proposed simple NFC as compared to the conventional NFC; rather, it shows superior performance over PI-controller-based drive.

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Acknowledgement

I wish to express my sincere gratitude and thanks to Silicon institute of technology, Bhubaneswar for granting me study-leave and providing constant financial support to carry out this work at National institute of technology Rourkela.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology Rourkela, Rourkela, 769008, India

    RABI NARAYAN MISHRA & KANUNGO BARADA MOHANTY

Authors
  1. RABI NARAYAN MISHRA
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  2. KANUNGO BARADA MOHANTY
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Corresponding author

Correspondence to RABI NARAYAN MISHRA.

Appendix

Appendix

Induction motor drive system parameters

1

Rated power

P or

3.7 kW

2

Rated voltage

V L-L

415 V

3

Rated speed

n r

1445 rpm

4

Rated frequency

f r

50 Hz

5

No. of pole pairs

P

2

6

Stator resistance

R s

7.34 Ω

7

Stator leakage inductance

L ls

0.021 H

8

Rotor resistance

R r

5.64 Ω

9

Rotor leakage inductance

L lr

0.021 H

10

Mutual inductance

L m

0.5 H

11

Friction coefficient

B

0.035 kg m2/s

12

Inertia coefficient

J

0.16 kg m2

Controllers parameters

13

PI-speed control

K p /K i

20/0.02

14

PI-torque control

K p /K i

10/0.01

15

Tuning rate of the weight

\( \eta_{wi} \)

0.05

16

Tuning rate of the MFs

η ai /η bi

0.005

17

Sampling time for proposed NFC-based drive

T s

100 µs

18

Sampling time for conventional NFC-based drive

T s

250 µs

19

For SNFC-GA-based drive

 Population size

40

 Crossover rate

0.8

 Mutation rate

0.18

 Learning rate

0.045

 Coefficient of momentum

0.55

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MISHRA, R.N., MOHANTY, K.B. Implementation of feedback-linearization-modelled induction motor drive through an adaptive simplified neuro-fuzzy approach. Sādhanā 42, 2113–2135 (2017). https://doi.org/10.1007/s12046-017-0741-6

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  • DOI: https://doi.org/10.1007/s12046-017-0741-6

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