Adaptive Self-Tuning Fuzzy Backstepping Controller for the Control of Electric Vehicle With Two-Motor-Wheel Drive

Laguidi, Ahmed; Hazzab, Abdeldjebar; Boughazi, Othmane; Habbab, Mohamed

doi:10.15546/aeei-2019-0013

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…The PI controller has been combined by an ANN artificial neuron network type controller to increase the performance of the ANN-DTC control strategy in terms of speed reference tracking. The neural network that we utilized is a multilayer network with local connections that learns using the backpropagation technique [28]. The structure of the neural network used is shown in the Figure 4.…”

Section: Design Of Ann-dtc Controllermentioning

confidence: 99%

Improved direct torque control strategy performances of electric vehicles induction motor

Bachiri

Gasbaoui

Ghezouani

et al. 2022

IJPEDS

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<p>A three-wheeled electric scooter (3WES) with two control techniques is modeled and simulated in this study. The conventional direct torque control (C-DTC) and the DTC based on a neural network artificial multi layers (ANN-DTC). The objective is to assess the traction system's response to the control approach. by 3WES taking into account the dynamics of the scooter, the range and the energy consumption of the battery. The 3WES was simulated numerically using the MATLAB/Simulink environment, which is powered 1.5 kW by two induction motors integrated into the rear wheels. Where the reference speeds of the rear wheels detected using a differential electronic. This can possibly cause it to synchronize the wheel speed in any curve. Each wheel's speed was controlled by two types of regulators, PI and ANN, to increase stability and reaction time (in terms of set point tracking, disturbance rejection and rise time). The proposed ANN-DTC control technique reduces torque, stator flux, and current ripple by roughly 35%. While the range of 3WES has increased by approximately 8.062 m, the battery power consumption has decreased by nearly 0.25%.</p>

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Section: Design Of Ann-dtc Controllermentioning

confidence: 99%

Improved direct torque control strategy performances of electric vehicles induction motor

Bachiri

Gasbaoui

Ghezouani

et al. 2022

IJPEDS

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“…The controller is resilient against stator resistance, viscous friction instability and unpredictable load torque disturbance. Nonetheless, this method employs feedback linearization to cancel all undesirable non-linearities [16]- [20].…”

Section: Introductionmentioning

confidence: 99%

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

Khessam¹,

Lousdad

Hazzab³

et al. 2022

IJEECS

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This paper introduces <span>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.</span>

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Adaptive Self-tuning Backstepping - Nonlinear (NLPI) Controller for the Control of Electric Vehicle with Two-Motor-Wheel Drive

Laguidi

Benoudjafer

Boughazi

2020

Artificial Intelligence and Renewables Towards an Energy Transition

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Adaptive Self-Tuning Fuzzy Backstepping Controller for the Control of Electric Vehicle With Two-Motor-Wheel Drive

Cited by 4 publications

References 7 publications

Improved direct torque control strategy performances of electric vehicles induction motor

Improved direct torque control strategy performances of electric vehicles induction motor

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

Adaptive Self-tuning Backstepping - Nonlinear (NLPI) Controller for the Control of Electric Vehicle with Two-Motor-Wheel Drive

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