Estimation-aware model predictive path-following control for a general 2-trailer with a car-like tractor

The design of the path-following controller is crucial to enable reliable autonomous vehicle operation. This design problem is especially challenging for a general 2-trailer with a car-like tractor due to the tractor's curvature limitations and the vehicle's structurally unstable joint-angle kinematics in backward motion. Additionally, to make the control system independent of any sensor mounted on the trailer, advanced sensors placed in the rear of the tractor have been proposed to solve the joint-angle estimation problem. Since these sensors typically have a limited field of view, the proposed estimation solution introduces restrictions on the joint-angle configurations that can be estimated with high accuracy. To model and explicitly consider these constraints in the controller, a model predictive path-following control approach is proposed. Two approaches with different computation complexity and performance are presented. In the first approach, the constraint on the joint angles is modeled as a union of convex polytopes, making it necessary to incorporate binary decision variables. The second approach avoids binary variables at the expense of a more restrictive approximation of the joint-angle constraints. In simulations and field experiments, the performance of the proposed path-following control approach in terms of suppressing disturbances and recovering from non-trivial initial states is compared with a previously proposed control strategy where the joint-angle constraints are neglected.