| Summary: | In the last decades, the development of self-driving vehicles has rapidly increased. Improvements in algorithms, as well as sensor and computing hardware have led to self-driving technologies becoming a reality. It is a technology with the potential to radically change how society interacts with transportation. One crucial part of a self-driving vehicle is control schemes that can safely control the vehicle during evasive maneuvers. This work investigates the modeling and lateral control of tractor-trailer vehicles during aggressive maneuvers. Models of various complexity are used, ranging from simple kinematic models to complex dynamic models, which model tire slip and suspension dynamics. The models are evaluated in simulations using TruckMaker, which is a high fidelity vehicle simulator. Several lateral controllers are proposed based on Model predictive control (MPC) and linear-quadratic (LQ) control techniques. The controllers use different complex prediction models and are designed to minimize the path-following error with respect to a geometric reference path. Their performance is evaluated on double lane change maneuvers of various lengths and with different longitudinal speeds. Additionally, the controllers' robustness against changes in trailer mass, weight distribution, and road traction is investigated. Extensive simulations show that dynamic prediction models are necessary to keep the control errors small when performing maneuvers that result in large lateral accelerations. Furthermore, to safely control the tractor-trailer vehicle during high speeds, it is a necessity to include a model of the trailer dynamics. The simulation study also shows that the proposed LQ controllers have trouble to evenly balance tractor and trailer deviation from the path, while the MPC controllers handle it much better. Additionally, a method for approximately weighting the trailer deviation is shown to improve the performance of both the LQ and MPC controllers. Finally, it is concluded that an MPC controller with a dynamic tractor-trailer model is robust against model errors, and can become even more robust by tuning the controller weights conservatively.
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