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

Mama, Chouitek; Benouzza, Noureddine; Bekouche, Benaissa

doi:10.11591/ijece.v10i2.pp1893-1904

Cited by 3 publications

(6 citation statements)

References 17 publications

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“…In the literature, several articles have studied the magnetic saturation in induction machines (IM) [3], [21]− [26]. The control laws based on the saturation of the IM machine are: linear controls with update of one or more parameters, or a nonlinear control with constant or variable parameters [27]. The dynamic model of the IM that takes into account saturation of the iron core in [24] will be used to develop a nonlinear control law for speed and flux.…”

Section: Mathematical Model Of Im Taking Into Account Saturationmentioning

confidence: 99%

“…Where 𝑘 1 and 𝑑 1 are any positive real design constants. By identifying (25) to (26), we obtain the (27).…”

Section: Design and Analysis Of Speed And Flux Backstepping Controlle...mentioning

confidence: 99%

“…According to(27) and (36) the control voltages 𝑢 𝑠𝑥 and𝑢 𝑠𝑦 satisfy as in (36) and (37). ISSN: 2088-8694 Int J Pow Elec & Dri Syst, Vol. 14, No.…”

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confidence: 99%

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Nonlinear control of an induction motor taking into account saturation and rotor resistance variation effects

Es-Semyhy

Ba-Razzouk

Haroussi

et al. 2023

IJPEDS

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<span lang="EN-US">Nowadays, electrical drives require more and more precision and reliability. Induction machine is one of the most robust actuators in terms of maintenance and reliability. Their control requires a good knowledge of the physical phenomena governing its operation. Saturation and heating are two main phenomena that must be taken into account to achieve the desired performance. In this paper, we will synthesize a nonlinear control law based on the “Backstepping” method, to regulate rotor speed and flux. This law takes into account saturation and rotor resistance variation effects. The immunity of the control to temperature rise will be tested. This control strategy is studied by simulation in the MATLAB/Simulink environment.</span>

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Section: Mathematical Model Of Im Taking Into Account Saturationmentioning

confidence: 99%

“…Where 𝑘 1 and 𝑑 1 are any positive real design constants. By identifying (25) to (26), we obtain the (27).…”

Section: Design and Analysis Of Speed And Flux Backstepping Controlle...mentioning

confidence: 99%

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Nonlinear control of an induction motor taking into account saturation and rotor resistance variation effects

Es-Semyhy

Ba-Razzouk

Haroussi

et al. 2023

IJPEDS

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show abstract

“…The power Pa in the gap becomes (9) He powers in the air gap is equal to the product of the electromagnetic torque Te and the speed of the rotor wm (10) By identification, we determine the equation of the electromagnetic torque knowing that the current i is constant , (11) . (12) This variation of the inductance developed in eq.…”

Section: ( ) [ ]mentioning

confidence: 99%

Comparison of the Various Controls of the Switched Reluctance Motor 12/8

CHOUITEK,

CHAOUCH,

BEKKOUCHE

2023

RRST-EE

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The control of the variable reluctance machine raises a certain number of constraints, among which we can cite: the effect of the external disturbances, the nature of the nonlinearities, the ripple of its torque, and the modeling errors. The different ordering approaches proposed in this article dealt with all these constraints. 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 flexible. In the opposite case, particularly when the controlled part is submitted to strong nonlinearity and temporal variations, control techniques must be designed to ensure the process's robustness concerning the uncertainties on the parameters and their variations. These techniques include artificial intelligence-based techniques constituted of neural networks and fuzzy logic. This technique can replace PID regulators with nonlinear ones using the human brain’s reasoning and functioning and is simulated using MATLAB/Simulink software. Finally, by using obtained waveforms, these results will be compared.

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“…Thanks to the awesome overextend of video compression, the viability of VP8 comparing to with H.264/AVC needs more thorough analysis. It is noted that a few works only compared the viability of both encoders such as [5][6][7][8][9][10][11][12][13][14]. Study [9] has compared Ì ISSN: 2088-8708 H.264/AVC and VP8 encoders for only three sequences.The evaluation measurements were only one metric; which is the perceived video quality.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based energy consumption modeling and comparison of H.264/AVC and google VP8 encoders

Sharrab

Alsmirat

Hawashin

et al. 2021

IJECE

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Advancement of the prediction models used in a variety of fields is a result of the contribution of machine learning approaches. Utilizing such modeling in feature engineering is exceptionally imperative and required. In this research, we show how to utilize machine learning to save time in research experiments, where we save more than five thousand hours of measuring the energy consumption of encoding recordings. Since measuring the energy consumption has got to be done by humans and since we require more than eleven thousand experiments to cover all the combinations of video sequences, video bit_rate, and video encoding settings, we utilize machine learning to model the energy consumption utilizing linear regression. VP8 codec has been offered by Google as an open video encoder in an effort to replace the popular MPEG-4 Part 10, known as H.264/AVC video encoder standard. This research model energy consumption and describes the major differences between H.264/AVC and VP8 encoders in terms of energy consumption and performance through experiments that are based on machine learning modeling. Twenty-nine raw video sequences are used, offering a wide range of resolutions and contents, with the frame sizes ranging from QCIF(176x144) to 2160p(3840x2160). For fairness in comparison analysis, we use seven settings in VP8 encoder and fifteen types of tuning in H.264/AVC. The settings cover various video qualities. The performance metrics include video qualities, encoding time, and encoding energy consumption.

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

Cited by 3 publications

References 17 publications

Nonlinear control of an induction motor taking into account saturation and rotor resistance variation effects

Nonlinear control of an induction motor taking into account saturation and rotor resistance variation effects

Comparison of the Various Controls of the Switched Reluctance Motor 12/8

Machine learning-based energy consumption modeling and comparison of H.264/AVC and google VP8 encoders

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