A New Trajectory Tracking Algorithm for Autonomous Vehicles Based on Model Predictive Control

Abstract

Trajectory tracking is a key technology for precisely controlling autonomous vehicles. In this paper, we propose a trajectory-tracking method based on model predictive control. Instead of using the forward Euler integration method, the backward Euler integration method is used to establish the predictive model. To meet the real-time requirement, a constraint is imposed on the control law and the warm-start technique is employed. The MPC-based controller is proved to be stable. The simulation results demonstrate that, at the cost of no or a little increase in computational time, the tracking performance of the controller is much better than that of controllers using the forward Euler method. The maximum lateral errors are reduced by 69.09%, 47.89% and 78.66%. The real-time performance of the MPC controller is good. The calculation time is below 0.0203 s, which is shorter than the control period.

Advanced Sensing and Control for Connected and Automated Vehicles

“…To meet the real-time requirement, a constraint is imposed on the control law, and the warm-start technique is employed. The authors of [7] concluded that the proposed the tracking performance of the proposed controller is much better than that of controllers using the forward Euler method.…”

“…This Special Issue of Sensors aims at reporting on some of the recent research efforts on this increasingly important topic. The 12 accepted papers in this Issue cover vehicle position estimation [1], vehicle dynamic parameters estimation [2], cooperative collision warning systems [3], small object detection [4], impact identification of the driver's driving performance on executive control function [5], hybrid path planning for autonomous driving [6], trajectory tracking for autonomous driving [7], vehicle stability control [8], vehicle stability and ride comfort control [9], urban platooning protocol design for platoon [10], path planning algorithm for platooning [11], and self-driving architecture design for CAV platoon [12].…”

“…Trajectory tracking is a key technology for precisely controlling autonomous vehicles. A trajectory-tracking method based on model predictive control is proposed in [7]. Instead of using the forward Euler integration method, the backward Euler integration method is used to establish the predictive model.…”

“…In summary, there is a huge potential for CAV in collision avoidance, safety improvement and driving stability improvement. The papers gathered in this Special Issue contributed by proposing solutions to the general problem of state and/or parameter estimation [1][2][3], obstacle detection [4], driver behavior estimation [5], and/or classical academic problems such as path planning [6], path tracking [7], and stability control for autonomous driving [8,9] and/or by suggesting applications of the combined use of sensors and advanced algorithms to platooning [10][11][12], thus showing the theoretical challenges and practical interest of this research topic. Finally, we wish to thank the authors, reviewers, and journal staff for their commitment and effort, which made it possible to complete this Special Issue on time.…”

Performance Improvement of H8 Transformerless Grid-Tied Inverter Using Model Predictive Control Considering a Weak Grid

“…Hence, the continuous state-space model of Equation ( 5) can be transformed into the discrete-time state-space model using the backward Euler technique [34]. Although the forward Euler method requires an extra computation at each iteration, the backward Euler method has great stability properties, and its local truncation error is much smaller than using the forward Euler method [35]. Therefore:…”