MODELOS BASEADOS EM REDES NEURAIS ARTIFICIAIS COM APLICAÇÃO EM CONTROLE INDIRETO DE TEMPERATURA

• Ano de defesa: 2015
• Autor(a) principal: Sá, Denis Fabrício Sousa de
• Orientador(a): FONSECA NETO, João Viana da
• Banca de defesa: Catunda, Sebastian Yuri Cavalcanti , Serra, Ginalber Luiz de Oliveira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: Engenharia
• País: BR
• Palavras-chave em Português: controle adaptativo; controle proporcional-integral-derivativo (PID); redes neurais artificiais; treinamento on line de RNAs, Identicação de sistemas; sistema térmico; mediação indireta; célula peltier; regulador linear quadrático discreto (DLQR); sistemas embarcados
• Palavras-chave em Inglês: adaptive control; proportional-integral-derivative control (PID); artificial neural networks; systems identification; thermal system; indirect measurement; discrete linear quadratic regulator (DLQR); peltier cell and embedded systems
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/295

The representation of dynamic systems or plants via mathematical models occupies an important position in control system design that allow the performance evaluation of the controller during his development stage. These models are also used as an alternative to solve the problem of the hardness or impracticability to install sensors that measure the controlled variables, the dynamic systems representations enable non-invasive measurement of these variables. As consequence the designer has an alternative way to perform adaptive and optimal sensorless control for a given process. In this dissertation is presented a proposal for control systems schemas and algorithms, based on recurrent neural networks (ANN) and Box-Jenkins models, that are dedicated to sensorless or indirect control of dynamic systems. The proposed models and algorithms are associated with the systems identification and recurrent ANN approaches. The algorithms developed for the AAN training are Backpropagation Accelerated and RLS types that are compared with classical methods and strategies to obtain it online parameters of indirect control of system for a thermal plant, where the actuator is Peltier cell. The performance the parametric models of the plant and adaptive PID digital controllers and linear quadratic regulator (DLQR) that are the main elements of the sensorless temperature control system, are evaluated by means of hybrid simulations, where the algorithms implemented in micro controllers and the plant represented by mathematical models. The performance results of the proposed sensorless control algorithms are promissory, not only, in terms of the control system performance, but also due to the reexibility to deploy it in other dynamic systems.