Velocity and Steering Control for Automated Personal Mobility Vehicle

Abstract: In this thesis, a Model Predictive Control (MPC) based re-planning and control system is proposed. The MPC re-planner will generate a collision-free path for the controlled vehicle when obstacles are detected, and the controller will make the vehicle move along the reference or re-planned path by adjusting its velocity and steering angle. The MPC re-planner and controller are built based on different vehicle dynamic models, i.e., the bicycle model and point-mass model, respectively. Simulation results show that the trajectory tracking performance when the velocity and steering are controlled simultaneously are better than using steering MPC alone. Then the effects on computational time of two critical parameters, prediction horizon and control horizon, are studied to find reasonable horizons that can enable real-time control. The robustness of the obstacle avoidance function is tested using obstacles with increasing sizes and the results show that the controlled vehicle is able to avoid a 6 m obstacle so that it can overtake other car-like vehicles in the driving process. Finally, a closed-loop one-lane road with some moving vehicles is built as a test scenario for the MPC-based re-planning and control system. According to the results, the controlled vehicle can successfully follow the centerline of the road and overtake other vehicles.