Cruise Control for a TwoWheeled Mobile Vehicle Using Its Mixed Logical Dynamical System Model This paper proposes a cruise control for a twowheeled mobile vehicle on a twodimensional plane. The cruise control is executed using model predictive control (MPC), such that input and state constraints can be addressed explicitly. For realtime execution of MPC, we model the dynamics of the vehicle as a mixed logical dynamical system rather than an underlying nonlinear model. The cruise control is then reduced to a mixed integer quadratic programming problem, for which efficient solvers using the convexities are available. Simulations and experiments demonstrate the effectiveness of the proposed cruise control through a computation time comparison with the nonlinear MPC method.

Ecocruise NMPC Control for Autonomous Vehicles The purpose of this work is the development of a nonlinear modelbased predictive controller for Ecocruise in autonomous ground vehicles. Ecodriving 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 latitudelongitude information and a GPS module, the predictive controller can calculate a sequence of control input to smooth the vehicleu0027s 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 CGMRES, 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 Ecocruise NMPC was implemented in simulation and compared with a cruise controller. The obtained results were considered satisfactory and showed the predictive controlleru0027s potential to reduce fuel consumption in autonomous vehicles.