Sintonia online de controladores PID adaptativo-ótimo via redes neuronais artificiais

• Ano de defesa: 2017
• Autor(a) principal: Santos, Hilton Seheris da Silva
• Orientador(a): FONSECA NETO, João Viana da
• 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: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Sintonia online; Redes neurais artificiais; Controlador PID; Controle adaptativo; Painel fotovoltaico; Controle preditivo; Photovoltaic panel
• Palavras-chave em Inglês: Online tuning; Artificial neural networks; PID controller; Adaptive control
• Área do conhecimento CNPq: Controle de Processos Eletrônicos, Retroalimentação
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1743

The emergence of new industrial plants with great complexity and the need to improve the operation of existing plants has fostered the development of high performance control systems, these systems must not only meet the design specifications, such as merit figures, but also operate at minimal cost and impacts at environment. Motivated by this demand, it is presented in this dissertation the development of methods for on-line tuning of control system parameters, ie, a methodology is presented for the on-line tuning of adaptive and optimal PID controllers via Artificial Neural Networks(ANNs). The approach developed in this dissertation is based on three PID controllers parameters. [Artificial neural networks with radial base functions and Model Predictive Control (MPC). From the union of these approaches a general formulation of an Adaptive-optimal PID controller via artificial neural networks with on-line tuning was presented. The on-line tuning methodology for the ANN parameters is presented in the context of MPC, predicting plant output. For the PID controller, we proposed a modification of the standard structure in order to adapt the error function. The adjustment of the PID controller parameters and the prediction of the optimally plant output, are performed by the ANN-RBF weights adjustments. In addition, an indoor implementation of the control system were proposed for the positioning of a photovoltaic panel. The performance evaluations of the proposed system were obtained from computational experiments results that were based on mathematical models and hardware experiments, that were obtained from a reduced model of a photovoltaic panel. Finally, a comparison between the proposed methodology with the classical PID controller were performed and the proposed methodology presented to be more flexible to the insertion of new performance metrics and the results achieved from the ANN, were better than the ones obtained by the classical PID tuning, such as: Ziegler-Nichols or trial and error.