Conception and Experimental Validation of a Model Predictive Control (MPC) for Lateral Control of a Truck-Trailer

Abstract

The automation of a truck-trailer offers enormous potential for safe and efficient transportation. The optimal control approaches, e.g., MPC, have significantly improved the tracking accuracy and the smoothness of the lateral control of vehicles. MPC application for a truck-trailer is complex compared to a car as the system behavior is different for forwarding and reversing. In this paper, we propose a lateral MPC algorithm for a truck-trailer, where we linearize the system dynamics around a nominal trajectory computed using a control law. The control law formulated as cascade control computes the nominal trajectory, an initial guess to the optimization process. The nominal trajectory lies in the vicinity of the optimal trajectory. We linearize the system dynamics around the computed nominal trajectory, reducing the linearization errors. The region of validity of the linearized system dynamics is narrow due to the system’s instability during reverse driving. A quadratic optimization problem subjective to the linear dynamics of the truck-trailer and state and input constraints defines the optimal control problem. The nominal trajectory is stable over the prediction horizon while reversing, so is the linear prediction model, improving optimization feasibility. Further, the discretization errors are also reduced using a small discrete step and integrating the model multiple times between two prediction steps. We tested the developed MPC approach on a prototypical full-scale truck-trailer system and discussed results. The developed MPC is considerably fast and accurate for real-time application.