Path follower for reversing off-axle single-joint semitrailer trucks

Abstract: Semitrailer trucks are widely used for transportation of goods in Sweden and around the world. Given their usefulness, and since they require specialized drivers, there is an increased need to automate the operation of these vehicles. In particular, reversing these vehicles is considered a challenging maneuver, mainly because of the jackknifing effect. To tackle this challenge, this thesis investigates path following for reversing single-joint semitrailer trucks, by comparing two path-following controllers, corresponding to a Linear Quadratic Regulator (LQR) and a Model Predictive Control (MPC), respectively. Both controllers receive kinematically feasible reference trajectories from a path planner (which is part of another thesis work), which makes it possible to avoid jackknifing as long as the reference joint angle between the trailer and the truck is closely followed. Moreover, they use a linearized and discretized 1-trailer kinematic model, defined in terms of the reference tracking errors for the truck as states. To evaluate the performance of the controllers, a Python simulation is implemented using the 1-trailer kinematic model. Using this simulation, the controllers are compared using metrics related to the reference tracking errors along the generated path and the controller execution time. The results show that the LQR and the MPC controllers perform similarly for most cases. Even though there are certain cases where the MPC outperforms the LQR, the execution time of the MPC is at least one order of magnitude slower, which makes the LQR an attractive solution for practical implementations, as long as certain assumptions (small initial deviations, reliable measurements) are ensured. As such, an LQR controller might be preferred by the industry because, while the performance from both controllers is similar, it can be considered a more efficient controller.