Combined Path Tracking and Stability Control using Model Predictive Control

Abstract

This thesis presents a new MPC controller which integrates path tracking and stability control into one controller. Previously these tasks were done by separate controllers, where one controller handled the path tracking while another controller ensured the vehicle was kept in the stable operating region. A drawback of this method is that the controllers have opposing objectives. The path tracker could require a higher steering wheel angle to follow the path, while the Vehicle Stability Controller (VSC) might require a lower angle to keep the vehicle stable. By integrating these two controllers into one controller, the new controller is able to take both tasks into account and optimise the control output such that both objectives are satisfied. This is achieved by implementing two extra yaw rates into the MPC model. These are the expected yaw rates based on the steering wheel angle and lateral acceleration of the vehicle. By comparing these two yaw rates to the actual yaw rate, the stability of the vehicle can be determined. The MPC controller is then able to prioritise path tracking or vehicle stability. This is achieved by actively varying the weights in the cost function depending on the vehicle state. To compare the new MPC controller, 8 benchmark controllers have been created. These controllers can be divided into two groups of four controllers. The first group is able to use differential braking in the control output, while the second group can only output an equal brake torque for all wheels. The benchmark controllers use different methods for path tracking and stability control, to get an understanding of the performance benefits of each method.
These different methods include: adding an extra target yaw rate based on path curvature and speed for tracking, adding constraints to ensure vehicle stability and using a separate stability controller to stabilise the vehicle. All controllers are evaluated using the industry standard Moose test as well as a double lane change in simulations. These manoeuvres are used in industry to evaluate stability and can also be used to evaluate path tracking. Furthermore the robustness of the controllers was evaluated by changing various parameters. These variations include: changing vehicle speed, adding extra weight to the vehicle, lowering the road 𝜇 level and performing a lane change where each lane has a different 𝜇 level. The results were evaluated using objective Key Performance Indicators regarding tracking performance and vehicle stability. The results show that the new MPC controller with the combined path tracking and stability control improves performance in both objectives. The new controller improves path tracking by 8% compared to the pure path tracking controller. While the stability is improved by 11% compared to the controller with a separate VSC. Furthermore the new controller was able to keep the vehicle stable at higher speeds and was more robust to varying conditions.