Estimation-aware model predictive path-following control for a general 2-trailer with a car-like tractor
This addresses the problem of reliable autonomous vehicle operation for trailers, but it is incremental as it builds on prior control strategies with specific constraints.
The paper tackled the path-following control problem for a general 2-trailer with a car-like tractor, which is challenging due to unstable kinematics and sensor limitations, by proposing a model predictive control approach that explicitly handles joint-angle constraints, resulting in performance comparisons in simulations and field experiments.
The design of the path-following controller is crucial for reliable autonomous vehicle operation. This design problem is especially challenging for a general 2-trailer with a car-like tractor due to the vehicle's unstable joint-angle kinematics in backward motion. Additionally, 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 estimation solution introduces restrictions on the joint-angle configurations that can be estimated with high accuracy. To 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 joint-angle constraints are 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 conservative controller. In simulations and field experiments, the performance of the proposed path-following control approach is compared with a previously proposed control strategy.