Model predictive control of heavy haul trains

• Ano de defesa: 2018
• Autor(a) principal: Neves, Carolina Calvo Pose Santos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal do Rio de Janeiro; Brasil; Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia; Programa de Pós-Graduação em Engenharia Elétrica; UFRJ
• Palavras-chave em Português: Engenharia elétrica; Transporte ferroviário; Controle preditivo baseado em modelo; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://hdl.handle.net/11422/9488

In railroad operations, locomotive engineers nowadays use a personalized driving pattern for each track/train combination. This plan serves as a guide reference for punctuality and energetically efficient travels. However, many safety issues related to the high forces experimented by the trains couplers persist, provoking accidents and derailments, which delay the logistic chain and raise operational costs. This Masters Dissertation describes a modeling, simulation and control methodology for real freight trains operation dealing with the described challenge. In fact, this work intends to propose a Model Predictive Control automatic driving procedure taking into account a weighted multi-objective minimization that can reduce forces in the couplings without increasing significantly the trip time or fuel consumption. A moving horizon technique is adopted to predict the train handling effects of the terrain forces interacting with train tractive and braking forces. A heavy haul train dynamic simulator is developed based on the described nonlinear model and numerical simulations illustrate the effectiveness of the considered scheme to reduce coupler forces. The methodology is applied to the "Ferrovia do Aço" railroad that passes through the States of Rio de Janeiro, São Paulo and Minas Gerais in Brazil with real train configuration.