Model-predictive Trajectory Tracking for Autonomous Vehicles

The thesis (in English) is available online: https://www.researchgate.net/publication/327201197_Trajectory_Tracking_for_Autonomous_Vehicles Trajectory tracking for autonomous driving based on model predictive control (MPC). Author: Jan Filip, Czech Technical University in Prague Control structure: a) Longitudinal velocity MPC based on LTI model, b) Lateral MPC with LPV model based on a single-track vehicle model with linear tire cornering force characteristic, c) The desired path created using spline curves and approximated piecewise-linearly during controller runtime for tracking error calculation. d) Velocity trajectory generated using the forward-backward iterative algorithm and a simplified "single isotropic tire" model. Longitudinal and lateral control of vehicle motion implemented in Simulink. Controller performance verified in simulation in a race track scenario using high-fidelity vehicle model in IPG Carmaker, with simulated OTS RT3002 state estimator. Simulator: IPG Carmaker 6.0.4 Location: Nardo Handling Track Car model: Tesla Model S QP Solver: qpOASES Description of the graphical overlay: * Left-hand side plots indicate: 1) Velocity tracking 2) Longitudinal acceleration 3) Lateral acceleration * Right-hand side plots indicate: 1) Crosstrack error 2) Heading error (orange with side slip compensation) 3) Steering angle (orange = open-loop, blue = closed-loop)