<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0035-001X</journal-id>
<journal-title><![CDATA[Revista mexicana de física]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. fis.]]></abbrev-journal-title>
<issn>0035-001X</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Física]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0035-001X2011000100013</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Design of an optimal control for an autonomous mobile robot]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gutiérrez-Arias]]></surname>
<given-names><![CDATA[E.M.]]></given-names>
</name>
<xref ref-type="aff" rid="A01"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Flores-Mena]]></surname>
<given-names><![CDATA[J.E.]]></given-names>
</name>
<xref ref-type="aff" rid="A01"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morin-Castillo]]></surname>
<given-names><![CDATA[M.M.]]></given-names>
</name>
<xref ref-type="aff" rid="A01"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Suárez-Ramírez]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<xref ref-type="aff" rid="A01"/>
</contrib>
</contrib-group>
<aff id="A01">
<institution><![CDATA[,Benemérita Universidad Autónoma de Puebla Facultad de Ciencias de la Electrónica ]]></institution>
<addr-line><![CDATA[Puebla Pue]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>02</month>
<year>2011</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>02</month>
<year>2011</year>
</pub-date>
<volume>57</volume>
<numero>1</numero>
<fpage>75</fpage>
<lpage>83</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0035-001X2011000100013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0035-001X2011000100013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0035-001X2011000100013&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[In this article, we present an autonomous mobile robot that is provided with two active wheels and passive one, as well as two control algorithms for the stabilization of the programmed paths. The dynamic programming constitute the bases for the determination of both control laws. The first law of optimal control is obtained by solving the Ricatti matricial differential equation. The second is deduced taking into account the work done by Kalman, which makes possible the reduction of a matricial differential equation into an algebraic matricial equation. The simulation of both algorithms is made when the programmed path is a straight line and this makes possible to observe the optimal control law, which represents the principal goal of this paper, and which presents an improved quality for the stabilization that the control law obtained following the work of Kalman.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[En este trabajo presentamos un robot movil autónomo provisto de dos ruedas activas y una pasiva, así como dos algoritmos de control para la estabilización de las trayectorias programadas; la programación dinámica es el fundamento para determinar ambas leyes de control. La primera ley de control optimo la obtenemos al solucionar una ecuación diferencial matricial del tipo Riccati, la segunda ley se deduce al aprovechar una disertación hecha por Kalman, la cual permite reducir una ecuación diferencial matricial a una ecuación algebraica matricial. La simulación de ambos algoritmos se realiza cuando la trayectoria programada es una línea recta y permite observar que la ley de control óptimo, objetivo primordial de este artículo, presenta una calidad superior en la estabilización que la ley de control obtenida mediante la disertación de Kalman.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Mobile robot]]></kwd>
<kwd lng="en"><![CDATA[optimal control]]></kwd>
<kwd lng="en"><![CDATA[dynamic programming]]></kwd>
<kwd lng="en"><![CDATA[Ricatti's differential equation]]></kwd>
<kwd lng="es"><![CDATA[Robot móvil]]></kwd>
<kwd lng="es"><![CDATA[control óptimo]]></kwd>
<kwd lng="es"><![CDATA[programación dinámica]]></kwd>
<kwd lng="es"><![CDATA[ecuación diferencial de Riccati]]></kwd>
</kwd-group>
</article-meta>
</front><body><![CDATA[ <p align="justify"><font face="verdana" size="4">Instrumentaci&oacute;n</font></p> 	    <p align="center"><font face="verdana" size="2">&nbsp;</font></p> 	    <p align="center"><font face="verdana" size="4"><b>Design of an optimal control for an autonomous mobile robot</b></font></p> 	    <p align="center"><font face="verdana" size="2">&nbsp;</font></p> 	    <p align="center"><font face="verdana" size="2"><b>E.M. Guti&eacute;rrez&#150;Arias, J.E. Flores&#150;Mena, M.M. Morin&#150;Castillo, and H. Su&aacute;rez&#150;Ram&iacute;rez</b></font></p> 	    <p align="justify"><font face="verdana" size="2">&nbsp;</font></p> 	    <p align="justify"><font face="verdana" size="2"><i>Facultad de Ciencias de la Electr&oacute;nica, Benem&eacute;rita Universidad Aut&oacute;noma de Puebla, Av. San Claudio y 18 Sur, Ciudad Universitaria, Colonia Jardines de San Manuel, Puebla, Pue., 72570, M&eacute;xico, e&#150;mails: </i><a href="mailto:jmgutierrez@ece.buap.mx">jmgutierrez@ece.buap.mx</a>, <a href="mailto:eflores@ece.buap.mx">eflores@ece.buap.mx</a>, <a href="mailto:mmorin@ece.buap.mx">mmorin@ece.buap.mx</a>, <a href="mailto:hsuarez@hotmail.com">hsuarez@hotmail.com</a>.</font></p> 	    <p align="justify"><font face="verdana" size="2">&nbsp;</font></p> 	    <p align="justify"><font face="verdana" size="2">Recibido el 18 de enero de 2010    <br>     Aceptado el 11 de noviembre de 2010</font></p> 	    ]]></body>
<body><![CDATA[<p align="justify"><font face="verdana" size="2">&nbsp;</font></p> 	    <p align="justify"><font face="verdana" size="2"><b>Abstract</b></font></p> 	    <p align="justify"><font face="verdana" size="2">In this article, we present an autonomous mobile robot that is provided with two active wheels and passive one, as well as two control algorithms for the stabilization of the programmed paths. The dynamic programming constitute the bases for the determination of both control laws. The first law of optimal control is obtained by solving the Ricatti matricial differential equation. The second is deduced taking into account the work done by Kalman, which makes possible the reduction of a matricial differential equation into an algebraic matricial equation. The simulation of both algorithms is made when the programmed path is a straight line and this makes possible to observe the optimal control law, which represents the principal goal of this paper, and which presents an improved quality for the stabilization that the control law obtained following the work of Kalman.</font></p> 	    <p align="justify"><font face="verdana" size="2"><b>Keywords:</b> Mobile robot; optimal control; dynamic programming; Ricatti's differential equation.</font></p> 	    <p align="justify"><font face="verdana" size="2">&nbsp;</font></p> 	    <p align="justify"><font face="verdana" size="2"><b>Resumen</b></font></p> 	    <p align="justify"><font face="verdana" size="2">En este trabajo presentamos un robot movil aut&oacute;nomo provisto de dos ruedas activas y una pasiva, as&iacute; como dos algoritmos de control para la estabilizaci&oacute;n de las trayectorias programadas; la programaci&oacute;n din&aacute;mica es el fundamento para determinar ambas leyes de control. La primera ley de control optimo la obtenemos al solucionar una ecuaci&oacute;n diferencial matricial del tipo Riccati, la segunda ley se deduce al aprovechar una disertaci&oacute;n hecha por Kalman, la cual permite reducir una ecuaci&oacute;n diferencial matricial a una ecuaci&oacute;n algebraica matricial. La simulaci&oacute;n de ambos algoritmos se realiza cuando la trayectoria programada es una l&iacute;nea recta y permite observar que la ley de control &oacute;ptimo, objetivo primordial de este art&iacute;culo, presenta una calidad superior en la estabilizaci&oacute;n que la ley de control obtenida mediante la disertaci&oacute;n de Kalman.</font></p> 	    <p align="justify"><font face="verdana" size="2"><b>Descriptores:</b> Robot m&oacute;vil; control &oacute;ptimo; programaci&oacute;n din&aacute;mica; ecuaci&oacute;n diferencial de Riccati.</font></p> 	    <p align="justify"><font face="verdana" size="2">&nbsp;</font></p> 	    <p align="justify"><font face="verdana" size="2">PACS: 45.40.&#150;f; 45.80.+r; 46.15.Cc; 02.30.Yy.</font></p> 	    ]]></body>
<body><![CDATA[<p align="justify"><font face="verdana" size="2">&nbsp;</font></p> 	    <p align="justify"><font face="verdana" size="2"><a href="/pdf/rmf/v57n1//v57n1a13.pdf" target="_blank">DESCARGAR ART&Iacute;CULO EN FORMATO PDF</a></font></p> 	    <p align="justify"><font face="verdana" size="2">&nbsp;</font></p> 	    <p align="justify"><font face="verdana" size="2"><b>Acknowledgments</b></font></p> 	    <p align="justify"><font face="verdana" size="2">The authors are grateful for the financial support given by CONACyT of M&eacute;xico. JEFM is grateful for the support given by VIEP&#150;BUAP (project 7&#150;ING&#150;2009)</font></p> 	    <p align="justify"><font face="verdana" size="2">&nbsp;</font></p> 	    <p align="justify"><font face="verdana" size="2"><b>References</b></font></p> 	    <!-- ref --><p align="justify"><font face="verdana" size="2">1. A. 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