Eco-cruise NMPC Control for Autonomous Vehicles

Abstract

The purpose of this work is the development of a nonlinear model-based predictive controller for Eco-cruise 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 latitude-longitude information 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 require iterative searches, which greatly reduces the computational burden, allowing a real time implementation. Another advantage of the proposed method is that it only requires publicly available data to obtain the controller parameters. The Eco-cruise NMPC was implemented in simulation and compared with a cruise controller. The obtained results were considered satisfactory and showed the predictive controller's potential to reduce fuel consumption in autonomous vehicles.