Artificial Neural Network-Based Gain-Scheduled State Feedback Speed Controller for Synchronous Reluctance Motor
More details
Hide details
Nicolaus Copernicus University, Institute of Engineering and Technology
Warsaw University of Technology, Institute of Control and Industrial Electronics
Corresponding author
Tomasz Tarczewski   

Nicolaus Copernicus University, Institute of Engineering and Technology
Power Electronics and Drives 2021;6 (41)
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 optimization 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 G-S SFC is applied to 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 behavior of SynRM drive and robustness against q-axis inductance, the moment of inertia, and viscous friction fluctuations.
Journals System - logo
Scroll to top