Robust Adaptive Control of Transverse Flux Permanent Magnet Machines Using Neural Networks

Babazadeh, A.; Karimi, Hamid Reza; Parspour, Nejila

doi:10.3182/20050703-6-cz-1902.01240

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The goal is to design a fuzzy PID controller Figure 7 in order to maintain the speed of the variable reluctance motor at the desired set points, then introduce a step where the motor is loaded and observed, at each instant adapt for different variations of velocity [24][25][26][27]. The conventional controller is replaced by a PID -fuzzy controller to ensure a 10% better operation by maintaining the set point speed even when charging the motor.…”

Section: Variable Reluctance Machine Fuzzy Control the Speedmentioning

confidence: 99%

Control of variable reluctance machine (8/6) by artificiel intelligence techniques

Mama¹,

Benouzza²,

Bekouche³

2020

IJECE

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The non-linearity of variable-Reluctance Machine (8/6) and the dependence of machine inductance on rotor position and applied current complicate the development of the control strategies of drives using variable-Reluctance Machine variable-Reluctance Machine (VRM). The classical-control algorithms for example of derived full proportional action may prove sufficient if the requirements on the accuracy and performance of systems are not too strict. In the opposite case and particularly when the controlled part is submitted to strong nonlinearity and to temporal variations, control techniques must be designed which ensure the robustness of the process with respect to the uncertainties on the parameters and their variations. These techniques include artificial-intelligence-based techniques constituted of neural networks and fuzzy logic. This technique has the ability to replace PID regulators by nonlinear ones using the human brain’s reasoning and functioning and is simulated by using MATLAB/Simulink software. Finally, by using obtained waveforms, these results will be compared.

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Section: Variable Reluctance Machine Fuzzy Control the Speedmentioning

confidence: 99%

Control of variable reluctance machine (8/6) by artificiel intelligence techniques

Mama¹,

Benouzza²,

Bekouche³

2020

IJECE

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Transverse Flux Machine—A Review

Kaiser

Parspour

2022

IEEE Access

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For applications with high torque demand, gearboxes are commonly used to convert torque and speed in order to receive higher specific values for torque and power. This causes additional losses, cost, inaccuracies, effort, and noise. Eliminating the need of a mechanical gear and the associated disadvantages, Transverse Flux Machines with their high torque density are a very promising alternative. Despite a high torque density and a good efficiency, these types of machines are not commonly used. Due to the complex structure, challenges with mechanical design, and modeling of the machine behavior arise. Additionally, there are high requirements for the inverter due to the low power factor. This paper provides an overview of the state of the art including the potentials and advantages but also the problems and hindrances of these types of machines. Relating to linear and rotary machines from research and industry, the machine is introduced with its history, application and classification. Further, the general technical aspects, the influence of materials for flux guidance, the methods of modeling, methods for a minimization of torque ripples, as well as methods for power factor improvement are presented.

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Robust Adaptive Control of Transverse Flux Permanent Magnet Machines Using Neural Networks

Cited by 2 publications

References 11 publications

Control of variable reluctance machine (8/6) by artificiel intelligence techniques

Control of variable reluctance machine (8/6) by artificiel intelligence techniques

Transverse Flux Machine—A Review

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