Integrated Torque Vectoring and Path Following using Nonlinear Model Predictive Control
K. Bani (TU Delft - Mechanical Engineering)
B. Shyrokau – Mentor (TU Delft - Intelligent Vehicles)
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Abstract
This thesis introduces a novel model predictive controller (MPC) that integrates both torque vectoring and path following into one controller. Due to a need to improve vehicle safety, systems are being developed in order to improve vehicle handling. One system that is able to improve the vehicle handling is torque vectoring (TV). With torque vectoring, the magnitude and the direction of torque can be controlled by either applying the motor or brake torques. Additionally, in order to eliminate human error as a cause of accident, automated drive (AD) vehicles are being developed. A key task for AD vehicles is to perform path following (PF), where the vehicle follows a predetermined reference path generated by path planning.
Beforehand, these tasks were performed by separate controllers, where one controller performed path following and the other controller focused on torque vectoring. The disadvantage of this method is that it leads to sub-optimal results as both controllers have opposing objectives. The TV controller is able to decrease the steering angle in order to improve the vehicle handling, whereas the PF controller could require a higher steering angle in order to follow the path. By integrating both tasks, the novel
controller is able to optimise the control output such that both objectives are achieved.
The use of model predictive control strategies with TV have been studied and its ability to deal with hard constraints, while decreasing the state errors and control input, makes it a suitable choice to use it for TV. When the MPC strategy is compared to more common control strategies it is clear that the MPC TV algorithm provides better results in terms of responsiveness, lateral acceleration and vehicle handling. Furthermore, due to its ability to integrate multiple applications and its ability to handle a greater level of complexity, a nonlinear model predictive control (NMPC) formulation will be used to perform both torque vectoring and path following.
In order to test the new MPC controller, benchmark controllers have been created for comparison. The benchmark controllers are two controllers that are able to perform torque vectoring by generating a corrective yaw moment to follow the yaw rate reference. The torques are then allocated based on the size and direction of the corrective yaw moment. Then, the NMPC controller that performs path following by using both the steering angle and torques as an input will be compared to a controller that is able to perform path following by using the steering angle as an input.
The controllers are compared by using the sine with dwell test and the double lane change manoeuvre. These manoeuvres are used to test the lateral performance, vehicle handling, responsiveness and tracking performance of the vehicles. Key performance indicators (KPI) are used in order to evaluate the results regarding tracking performance and the vehicle handling.
The results show that the NMPC controller has an increase in performance regarding both path following and vehicle handling. When compared to the benchmark torque vectoring controller, the vehicle handling is increased by 5% and the lateral performance is increased by 6 %. Additionally, compared to the path following controller, by adding torque vectoring, the NMPC controller has improved the path
following by 4 %, the vehicle handling has been improved by 5 % and the responsiveness has been improved by 11 %.