Robust Model Predictive Control for Autonomous Driving
Abstract: Autonomous driving is becoming popular nowadays. In order for autonomouscars to be fully accepted, high demands are placed on the safety side. Onesafety critical issue is the robustness to disturbances. In this work, a robustmodel predictive controller is designed for an autonomous vehicle. More specifically,robust output feedback model predictive control (ROFMPC) is used, androbustness is guaranteed through the use of robust invariant sets. The vehicleis modeled using a discretized, and linearized, version of a simple kinematicbicycle model, expressed in road-aligned coordinates. It is investigated for howlarge uncertainties robustness, and stability, can be guaranteed. Both externaldisturbances and measurement noise are considered. A steady-state Kalmanfilter is used to estimate the state of the vehicle. Two cases have been studiedin simulation; straight line and curved line trajectory following. Results fromsimulations show that robustness can be ensured if the uncertainties in the systemare sufficiently small. Finally, the ROFMPC algorithm is implemented onan F1/10 RC car.
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