Tube MPC for Robust Lateral Control of a Class 8 Tractor-Trailer with Parameter Uncertainty

Abstract

This thesis presents a Model Predictive Control (MPC) design for motion planning and control of a five axle tractor-trailer vehicle. The targeted use case for this design is for Society of Automotive Engineers (SAE) Level 3-4 features which may include automated highway driving and lane change or obstacle avoidance maneuvers. Autonomous control of commercial tractor trailer vehicles, specifically class 8 trucks, presents unique challenges due to the need for specific safety guarantees and lack of accurate knowledge of all of the model parameters, such as the mass and yaw inertia of the payload in the trailer. These challenges can be handled in part by MPC due to its ability to enforce constraints and find optimal trajectories with respect to an objective. Additionally, many techniques for ensuring constraints are satisfied under uncertainty exist which can prove useful for this application. In this thesis, a commonly used dynamic model for tractortrailers is first presented. Next, a full prediction model for use in the MPC is developed, which combines the equations for propagating position with respect to the road and a model for the steering actuator with the lateral dynamic model of the vehicle. An MPC design is then introduced by defining the optimal control problem and solving it as a Quadratic Program (QP). The MPC is able to plan and execute a trajectory that ensures constraints related to the vehicle’s position and trailer states such as the hitch angle can be met. A higher update rate feedback controller is used to aid the tracking of the latest solution between MPC updates. A constraint tightening technique is also applied which constructs an error tube around the planned trajectory based on the uncertainty of model parameters. An analysis of the total accuracy and performance in different scenarios is presented. The combined online planning and control scheme is validated in simulation, and the MPC performance with and without constraint tightening is compared for several relevant scenarios, and improvements in the number of scenarios that satisfy the lateral position and hitch angle constraints is demonstrated. Finally, the real-world capability of the design is demonstrated on an autonomous Peterbilt 579 with a trailer attached. The experimental testing demonstrates the feasibility of the concept for real-time control through lane keeping tests, resulting in absolute tracking errors with at most a mean of 18.9 cm and a standard deviation of 11.9 cm.