<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abderazak SAIDI1, Farid NACERI1, Lamia YOUB1, Mihai CERNAT2, Luis GUASCH PESQUER3</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Two Types of Fuzzy Logic Controllersfor the Speed Control of theDoubly-Fed Induction Machine.</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Electrical and Computer Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">65-74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The paper presents two fuzzy logic control&lt;br&gt;algorithms: type-1 and type-2. These two nonlinear techniques&lt;br&gt;are used for adjust the speed control with a direct stator flux&lt;br&gt;orientation control of a doubly fed induction motor. The&lt;br&gt;effectiveness of the proposed control strategy is evaluated&lt;br&gt;under different operating conditions such as of reference speed&lt;br&gt;and for load torque step changes at nominal parameters and in&lt;br&gt;the presence of parameter variation (stator resistance, rotor&lt;br&gt;resistance and moment of inertia). The results of the simulation&lt;br&gt;of the doubly fed induction motor velocity control have shown&lt;br&gt;that fuzzy type-2 ensures better dynamic performances with&lt;br&gt;respect to fuzzy type-1 control, even by parametric variations&lt;br&gt;and external disturbances.&lt;br&gt;I</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. ZIDANI, L. YOUB, S. BELKACEM, F. NACERI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DESIGN OF ROBUST CONTROL USING FUZZY LOGIC CONTROLLER FOR DOUBLY-FED INDUCTION MOTOR DRIVES</style></title><secondary-title><style face="normal" font="default" size="100%">U.P.B. Sci. Bull.,</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">81</style></volume><pages><style face="normal" font="default" size="100%">159-170</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a fuzzy logic controller destined to the doubly-fed induction motor (DFIM) speed controlling. It solves the problems associated with the conventional IP (Integral Proportional) controller. This fuzzy logic controller is based on the decoupling control to enhance robustness under different operating conditions such as load torque and in the presence of parameters variation.&lt;br&gt;The simulation results for various scenarios show the high performances of the proposed control in terms of piloting effectiveness, precision, rapidity and stability for the high powers DFIM operating at variable speeds.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. SAIDI, F. NACERI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Speed control of a doubly-fed induction machine based on fuzzy adaptive</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">61-76</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	&lt;span style=&quot;color:#00ffff;&quot;&gt;&lt;span style=&quot;background-color:#00ffff;&quot;&gt;In this paper, we are interested in the adaptive&amp;nbsp;&lt;/span&gt;&lt;br&gt;&lt;span style=&quot;background-color:#00ffff;&quot;&gt;fuzzy control a technique has been studied and&lt;/span&gt;&lt;br&gt;&lt;span style=&quot;background-color:#00ffff;&quot;&gt;&amp;nbsp;applied, namely adaptive fuzzy control based&lt;/span&gt;&lt;br&gt;&lt;span style=&quot;background-color:#00ffff;&quot;&gt;on theory of Lyapunov. The system based on the&amp;nbsp;&lt;/span&gt;&lt;br&gt;&lt;span style=&quot;background-color:#00ffff;&quot;&gt;stability theory is used to approximate the gains&amp;nbsp;&lt;/span&gt;&lt;br&gt;&lt;span style=&quot;background-color:#00ffff;&quot;&gt;ke and kdce to ensure the stability of the control&lt;/span&gt;&lt;br&gt;&lt;span style=&quot;background-color:#00ffff;&quot;&gt;&amp;nbsp;inparticular time, simulations results obtained by&lt;/span&gt;&lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;color:#00ffff;&quot;&gt;&lt;span style=&quot;background-color:#00ffff;&quot;&gt;using MATLAB environment gives that the fuzzy &lt;/span&gt;&lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;color:#00ffff;&quot;&gt;&lt;span style=&quot;background-color:#00ffff;&quot;&gt;adaptive control more robust, also it has superior &lt;/span&gt;&lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;color:#00ffff;&quot;&gt;&lt;span style=&quot;background-color:#00ffff;&quot;&gt;dynamicsperformances. The results and test&lt;/span&gt;&lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;color:#00ffff;&quot;&gt;&lt;span style=&quot;background-color:#00ffff;&quot;&gt;of robustness will be presented.&lt;/span&gt;&lt;/span&gt;
&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. SAIDI, F. NACERI, S. VAIDYANATHAN</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A new robust adaptive fuzzy synergetic control fornonlinear systems with an application to an invertedpendulum</style></title><secondary-title><style face="normal" font="default" size="100%">Int. J. Modelling, Identification and Control</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">89-96</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper deals with a nonlinear adaptive control design based on synergetic control,&lt;br&gt;which also uses fuzzy systems to approximate the dynamics of nonlinear systems. The stability of&lt;br&gt;the closed-loop system is ensured by the Lyapunov synthesis in the sense that all the signals are&lt;br&gt;bounded, and the controller parameters adjusted by adaptation laws. The proposed algorithm is&lt;br&gt;applied to an inverted pendulum to track a sinusoidal reference trajectory. Simulations and&lt;br&gt;discussion are presented to illustrate the new robust adaptive fuzzy synergetic control described&lt;br&gt;in this work.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">L. YOUB, S. BELKACEM1, F.  NACERI1, M. CERNAT2, L.G.  PESQUER3</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">
	Design of an Adaptive Fuzzy Control System



	for Dual Star Induction Motor Drives

</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Electrical and Computer Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">37-44</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	&lt;span style=&quot;background-color:#0000ff;&quot;&gt;In this paper, a new control strategy is developed;&lt;/span&gt;&lt;br&gt;&lt;span style=&quot;background-color:#0000ff;&quot;&gt;an adaptive fuzzy controller based on Lyapunov’s&lt;/span&gt;&lt;br&gt;&lt;span style=&quot;background-color:#0000ff;&quot;&gt;stability theory (AFLC) recalculates the real-time&lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;background-color:#0000ff;&quot;&gt;PI-fuzzygains and combines the advantages of &lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;background-color:#0000ff;&quot;&gt;two robust techniquesi.e. the fuzzy logic control&lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;background-color:#0000ff;&quot;&gt;and the adaptive one. For the newadaptive fuzzy &lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;background-color:#0000ff;&quot;&gt;control, we followed two steps: in the first one, a&lt;/span&gt;&lt;br&gt;&lt;span style=&quot;background-color:#0000ff;&quot;&gt;PI-fuzzy controller is designed, in the second step,&lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;background-color:#0000ff;&quot;&gt;the gains ofa fuzzy regulator are determined. &lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;background-color:#0000ff;&quot;&gt;Extensive simulation resultsare presented to &lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;background-color:#0000ff;&quot;&gt;validate the proposed technique. The system is&lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;background-color:#0000ff;&quot;&gt;tested at different speeds and a very satisfactory&lt;/span&gt;
&lt;/p&gt;

&lt;p&gt;
	&lt;span style=&quot;background-color:#0000ff;&quot;&gt;performance has been achieved.&lt;/span&gt;
&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lamia YOUB1, Sebti BELKACEM1, Farid NACERI1, Mihai CERNAT2, Luis Guasch PESQUER3</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design of an Adaptive Fuzzy Control Systemfor Dual Star Induction Motor Drives</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Electrical and Computer Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">www.aece.ro</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">37-44</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, a new control strategy is developed;&lt;br&gt;an adaptive fuzzy controller based on Lyapunov’s&lt;br&gt;stability theory (AFLC) recalculates the real-time PI-fuzzy&lt;br&gt;gains and combines the advantages of two robust techniques&lt;br&gt;i.e. the fuzzy logic control and the adaptive one. For the new&lt;br&gt;adaptive fuzzy control, we followed two steps: in the first one, a&lt;br&gt;PI-fuzzy controller is designed, in the second step, the gains of&lt;br&gt;a fuzzy regulator are determined. Extensive simulation results&lt;br&gt;are presented to validate the proposed technique. The system is&lt;br&gt;tested at different speeds and a very satisfactory performance&lt;br&gt;has been achieved.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record></records></xml>