2017, Robust model predictive control for autonomous driving
Autonomous driving is becoming popular nowadays. In order for autonomous cars to be fully accepted high demands are placed on the safety. One safety critical issue is the robustness to disturbances. The goal of this thesis work is to develop a robust output feedback model predictive control for such issue.
In this work a robust model predictive controller is designed for an autonomous vehicle. More specifically, robust output feedback model predictive control (ROFMPC) is used, and robustness is guaranteed through the use of robust invariant sets. The vehicle is modeled using a discretized, and linearized, version of a simple kinematic bicycle model, expressed in road-aligned coordinates. It is investigated for how large uncertainties robustness, and stability, can be guaranteed. Both external disturbances and measurement noise are considered. A steady-state Kalman filter is used to estimate the state of the vehicle. Two cases have been studied in simulation; straight line and curved line trajectory following. Results from simulations show that robustness can be ensured if the uncertainties in the system are sufficiently small. Finally, the ROFMPC algorithm is implemented and tested on an F1/10 RC car.
