Model predictive eco-driving control of autonomous vehicle

• Ano de defesa: 2018
• Autor(a) principal: Caldas, Kenny Anderson Queiroz
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Autonomous vehicle; Controle preditivo; Direção ecológica; Ecological driving; Economia de combustível; Fuel economy; Predictive control; Veiculo autônomo
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/18/18153/tde-22032021-144058/

The purpose of this masters thesis is the implementation of a model-based predictive controller for eco-driving in autonomous ground vehicles. Eco-driving consists of a group of strategies adopted by a driver aiming to reduce fuel consumption and improvement of safety and comfort levels during a trip. Through the use of digital maps and a GPS module, the predictive controller can calculate a sequence of control input to smooth the vehicle\'s acceleration and braking along the route in critical parts, such as uphills, downhills and curves, following the speed limits of each road. This is accomplished by predictions based on the mathematical model of the vehicle and estimation of gasoline expenditure. The chosen optimizer algorithm is called C/GMRES, where its main advantage from the traditional methods is that the solution of the optimal problem does not required iterative searches, which greatly reduces the computational burden, allowing a real time implementation. The proposed control strategy was implemented in two routes, in a city and highway scenarios, in a simulated environment. The obtained results were considered satisfactory and showed the predictive controller potential to deal with the fuel consumption problem in autonomous vehicles.