On sensing-aware model predictive path-following control for a reversing general 2-trailer with a car-like tractor
This addresses the challenge of autonomous vehicle control for complex articulated trailers, but it is incremental as it builds on existing model predictive control methods with added constraints.
The paper tackled the problem of designing a reliable path-following controller for a reversing general 2-trailer with a car-like tractor, which is prone to instability and jackknifing, by proposing a model predictive controller that incorporates physical and sensing limitations; real-world experiments showed significant improvement in disturbance suppression and recovery from non-trivial initial states compared to a prior solution that neglected constraints.
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.