Machine Learning Structure for Controlling the Speed of Variable Reluctance Motor via Transitioning Policy Iteration Algorithm

This paper investigated a new speed regulator using an adaptive transitioning policy iteration learning technique for the variable reluctance motor (VRM) drive. A transitioning strategy is used in this unique scheme to handle the nonlinear behavior of the VRM by using a series of learning centers, e...

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Vydané v:	World electric vehicle journal Ročník 15; číslo 9; s. 421
Hlavný autor:	Alharkan, Hamad
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.09.2024
Predmet:	Adaptive algorithms adaptive control systems Algorithms Design optimization Dynamic programming Electric motors Iterative algorithms Machine learning machine learning method Methods Nonlinear control optimization problems Speed regulators Variable reluctance variable reluctance motor
ISSN:	2032-6653, 2032-6653
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