Modelado y control Neuroborroso de sistemas complejos. Aplicación a procesos de mecanizado de alto rendimiento
type: CreativeWork
description: [EN] This work presents a methodology for the design and implementation of an intelligent control system and an intelligent monitoring system. This methodology is successfully applied to highly complex processes. To that end, it proposes a procedure based on the neurofuzzy modeling of the process in question. The models are obtained through an identification process which uses representative input-output data of the system to be studied. Once models which describe the dynamic process have been obtained, these are used as the basis of the monitoring and control systems. In the case of design and implementation of neurofuzzy control systems, it proposes a procedure for obtaining a neurofuzzy model of the process dynamics (direct dynamic) and a neurofuzzy model of its inverse dynamics. These models are used according to the internal model control paradigm to control the complex process. Thus, it designs and implements a neurofuzzy control system based on the internal model control paradigm to control the cutting force in a drilling process (complex electrochemical process) by modifying the feed rate of the tool. Moreover, in the case of neurofuzzy monitoring of complex systems, it proposes a procedure to obtain a neurofuzzy model which relates directly measured process parameters through sensors with a range of phenomena difficult to measure on-line. Thus, the monitoring system is implemented on the basis of the obtained neurofuzzy model. The proposed procedure for developing a neurofuzzy monitoring system, it has been applied to monitoring cutting tool wear in a turning process (complex physical-mechanical process). The information obtained from cutting forces sensors, acceleration (vibration) sensors, acoustic emission sensors and using the operating time, it has developed a neurofuzzy model to estimate the flank wear of the cutting tool. Both in the monitoring system design and in the design and implementation of the control system, it has used different neurofuzzy …
abstract: [EN] This work presents a methodology for the design and implementation of an intelligent control system and an intelligent monitoring system. This methodology is successfully applied to highly complex processes. To that end, it proposes a procedure based on the neurofuzzy modeling of the process in question. The models are obtained through an identification process which uses representative input-output data of the system to be studied. Once models which describe the dynamic process have been obtained, these are used as the basis of the monitoring and control systems. In the case of design and implementation of neurofuzzy control systems, it proposes a procedure for obtaining a neurofuzzy model of the process dynamics (direct dynamic) and a neurofuzzy model of its inverse dynamics. These models are used according to the internal model control paradigm to control the complex process. Thus, it designs and implements a neurofuzzy control system based on the internal model control paradigm to control the cutting force in a drilling process (complex electrochemical process) by modifying the feed rate of the tool. Moreover, in the case of neurofuzzy monitoring of complex systems, it proposes a procedure to obtain a neurofuzzy model which relates directly measured process parameters through sensors with a range of phenomena difficult to measure on-line. Thus, the monitoring system is implemented on the basis of the obtained neurofuzzy model. The proposed procedure for developing a neurofuzzy monitoring system, it has been applied to monitoring cutting tool wear in a turning process (complex physical-mechanical process). The information obtained from cutting forces sensors, acceleration (vibration) sensors, acoustic emission sensors and using the operating time, it has developed a neurofuzzy model to estimate the flank wear of the cutting tool. Both in the monitoring system design and in the design and implementation of the control system, it has used different neurofuzzy …
author: Gajate Martín, Agustín
keywords: Tesis y disertaciones académicas, Universidad de Salamanca (España), Academic dissertations, Inteligencia artificial, Artificial Intelligence
date published: 2010-06-03
date modified: 2026-07-27T20:53:56Z
provider: Universidad de Salamanca
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Universidad de Salamanca

PropertyValue
property ID: DOI
value: 10.14201/gredos.76447