  The design of reliable path-following controllers is a key ingredient for
successful deployment of self-driving vehicles. This controller-design problem
is especially challenging for a general 2-trailer with a car-like tractor due
to the vehicle's structurally unstable joint-angle kinematics in backward
motion and the car-like tractor's curvature limitations which can cause the
vehicle segments to fold and enter a jackknife state. Furthermore, advanced
sensors with a limited field of view have been proposed to solve the
joint-angle estimation problem online, which introduce additional restrictions
on which vehicle states that can be reliably estimated. To incorporate these
restrictions at the level of control, a model predictive path-following
controller is proposed. By taking the vehicle's physical and sensing
limitations into account, it is shown in real-world experiments that the
performance of the proposed path-following controller in terms of suppressing
disturbances and recovering from non-trivial initial states is significantly
improved compared to a previously proposed solution where the constraints have
been neglected.
