MPC-Based Trajectory Tracking Via Active Front Steering and External Yaw Moment
In this paper, we propose a trajectory tracking method based Model Predictive Control (MPC) by utilizing steering and yaw moment. The main control objective is to track the desired trajectory with the front steering angle and yaw moment. And an external logic computes the torques at the four wheels such that the yaw moment is achieved, at the same time the logic controls the tire slip ratio around 0.15. Finally, the effectiveness of the proposed approach is demonstrated through the simulations at low and high speed. INTRODUCTION Automatic vehicle has broad development prospect in the future intelligent transportation system. Its research has gradually entered the stage of rapid development which forms research fields including perception and navigation, autonomous decision-making, path planning and trajectory tracking. And trajectory tracking is closely related to vehicle dynamics, and is one of the key technologies to realize unmanned driving [1]. At first, the trajectory tracking research focuses on using the front steering angle to follow the desired trajectory. Researchers adopt a variety of control methods to compute the front steering angle. For instance, in [2] the authors presents controllers design procedure for dynamic trajectory tracking of a highly automated vehicle. Longitudinal and lateral controller are decoupled. The lateral controller is based adaptive backstepping approach to design a steering control law for purpose of trajectory tracking, at the same time considering the model nonlinearities and parameter uncertainties. In [3-4] the authors adopt H∞, MPC control method to control the front steering angle to achievement trajectory tracking. There are also some examples [5-8] in the “Grand Challenge” race using the front steering angle to following the trajectory. Current research efforts in trajectory tracking focus on combining the front steering angle and braking to control the vehicle to achieve trajectory tracking more effectively and steadily. For instance, in [9] the added control variable is braking torques in one of the four wheels. The authors use direct Lyapunov method to compute the front steering angle and braking torques, then log the calculated data on vehicle dynamic model to achieve a double lane change maneuver. In [10] the authors use the Nonlinear MPC formulation to track a desired path for obstacle avoidance maneuver, by a combined use of braking and steering. More examples are shown in [11-13]. _________________________________________ Xiao Wang, Jibin Hu, Jun Ni,Beijing Institute of Technology, Beijing, China