A new application for fast prediction and protection of electrical drive wheel speed using machine learning methodology

Medjdoub Khessam, Abdelkader Lousdad, Abdeldjebar Hazzab, Miloud Rezkallah, Ambrish Chandra

Abstract


This paper introduces a non-linear implementation of the speed control technique of permanent magnetic synchronous motors (PMSM) using electronic differential (ED) command. Artificial neural network (ANN) coupled with particles swarm optimization (ANN-PSO) are implemented to control wheel speed and steering angle. The main purpose of the PMSM system and its application is the command of electric vehicles (EV). In the controller design, three-phase currents and rotor speed shall be measurable and eligible for feedback. Our propulsion platform consists of two PMSM in the back. The study with implemented ANN-PSO is performed after collecting the data from the ED to manage the control of speed EV, Left and right of steering angle and steering ahead. Based on this strategy, a new application can be provided in the GPS application to give the information as input (curved path angle) to ANN-PSO. Next, the application of ANN-PSO can estimate the parameters of ED to avoid the slip, as well as improves better performance and dynamic stability of electric vehicle drive systems.

Keywords


Artificial neural network; Electric vehicle; Electronic differential; Non-linear control; Particles swarm optimization; Permanent magnet sunchronous generator

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DOI: http://doi.org/10.11591/ijeecs.v26.i3.pp1290-1298

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