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<p>This paper focuses on designing a gain-scheduled (G-S) state feedback controller (SFC) for synchronous reluctance motor (SynRM) speed control with non-linear inductance characteristics. The augmented model of the drive with additional state variables is introduced to assure precise control of selected state variables (i.e. angular speed and <italic>d</italic>-axis current). Optimal, non-constant coefficients of the controller are calculated using a linear-quadratic optimisation method. Non-constant coefficients are approximated using an artificial neural network (ANN) to assure superior accuracy and relatively low usage of resources during implementation. To the best of our knowledge, this is the first time when ANN-based gain-scheduled state feedback controller (G-S SFC) is applied for speed control of SynRM. Based on numerous simulation tests, including a comparison with a signum-based SFC, it is shown that the proposed solution assures good dynamical behaviour of SynRM drive and robustness against <italic>q</italic>-axis inductance, the moment of inertia and viscous friction fluctuations.</p>
</abstract>

This paper focuses on designing a gain-scheduled (G-S) state feedback controller (SFC) for synchronous reluctance motor (SynRM) speed control with non-linear inductance characteristics. The augmented model of the drive with additional state variables is introduced to assure precise control of selected state variables (i.e. angular speed and d-axis current). Optimal, non-constant coefficients of the controller are calculated using a linear-quadratic optimisation method. Non-constant coefficients are approximated using an artificial neural network (ANN) to assure superior accuracy and relatively low usage of resources during implementation. To the best of our knowledge, this is the first time when ANN-based gain-scheduled state feedback controller (G-S SFC) is applied for speed control of SynRM. Based on numerous simulation tests, including a comparison with a signum-based SFC, it is shown that the proposed solution assures good dynamical behaviour of SynRM drive and robustness against q-axis inductance, the moment of inertia and viscous friction fluctuations.

Artificial Neural Network-Based Gain-Scheduled State Feedback Speed Controller for Synchronous Reluctance Motor

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper focuses on designing a gain-scheduled (G-S) state feedback controller (SFC) for synchronous reluctance motor (SynRM) speed control with non-linear...

Artificial Neural Network-Based Gain-Scheduled State Feedback Speed Controller for Synchronous Reluctance Motor

Data publikacji: 17 gru 2021

Zakres stron: 276 - 288

Otrzymano: 12 paź 2021

Przyjęty: 10 lis 2021

DOI: https://doi.org/10.2478/pead-2021-0017

Słowa kluczowe
synchronous reluctance motor, state feedback controller, gain-scheduling, artificial neural network, robustness analysis

© 2021 Tomasz Tarczewski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Artificial Neural Network-Based Gain-Scheduled State Feedback Speed Controller for Synchronous Reluctance Motor

Data publikacji: 17 gru 2021

Zakres stron: 276 - 288

Otrzymano: 12 paź 2021

Przyjęty: 10 lis 2021

DOI: https://doi.org/10.2478/pead-2021-0017

Słowa kluczowesynchronous reluctance motor, state feedback controller, gain-scheduling, artificial neural network, robustness analysis

© 2021 Tomasz Tarczewski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
synchronous reluctance motor, state feedback controller, gain-scheduling, artificial neural network, robustness analysis