Model Predictive Control for Autonomous Driving of a Truck

Abstract: Platooning and cooperative driving can decrease the emissions of greenhouse gases and increase the traffic capacity of the roads. The Grand Cooperative Driving Challenge, GCDC, is a competition that will be held in May 2016 focusing on cooperative driving. A cornerstone in the cooperative driving is autonomous driving. The main objective of this thesis is to design a Model Predictive Control for a truck so it autonomously can perform the following tasks: follow a straight road, make a lane change and make a turn. Constraints are added to the vehicle states and the control signals. Additionally, a constraint is added to make the vehicle keep a safe distance to preceding vehicles. A Linear Time-Varying (LTV) MPC for reference tracking is derived. To use the MPC for reference tracking references for all the states and control signals are derived. For the lane change and turn scenario Bezier curves are used to obtain the position references. The controller is implemented in MATLAB and validated through simulations. For the simulations both a bicycle model of the vehicle and a more complicated four wheel model are used. The latter is implemented in Simulink. The bicycle model is on-line linearised around the references in order to be used as the prediction model for the LTV-MPC. The simulations show that the controller can make the vehicle perform the above mentioned tasks. With a horizon of 18 the time in average to perform one iteration of the control loop is 0.02 s. The maximum deviation in the lateral direction is 0.10 m and occurs for the turn scenario when the four wheel model is used for the simulations. Simulations are also done with a preceding vehicle. The controller is able to make the vehicle keep a safe distance to the preceding vehicle. If the preceding vehicle is driving slower than the controlled vehicle the controller is able to decrease the velocity of the controlled vehicle. In addition to the above mentioned, simulations are also done where disturbances and noise, separately, are added. As a disturbance an error in the start position is used. The vehicle can start 1.3 m from the real start position, in the lateral direction, and still find its way back to the trajectory. The noise is added as white noise to the position updates. The controller can deal with noise with a standard deviation up to 0.3 m.