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http://hdl.handle.net/10397/94642
Title: | A new trajectory tracking algorithm for autonomous vehicles based on model predictive control | Authors: | Huang, Z Li, H Li, W Liu, J Huang, C Yang, Z Fang, W |
Issue Date: | Nov-2021 | Source: | Sensors, Nov. 2021, v. 21, no. 21, 7165 | 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 es-tablish 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. | Keywords: | Autonomous driving Model predictive control Real-time control Trajectory tracking |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Sensors | EISSN: | 1424-8220 | DOI: | 10.3390/s21217165 | Rights: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attributio+n (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The following publication Huang, Z.; Li, H.; Li, W.; Liu, J.; Huang, C.; Yang, Z.; Fang, W. A New Trajectory Tracking Algorithm for Autonomous Vehicles Based on Model Predictive Control. Sensors 2021, 21, 7165 is available at https://doi.org/10.3390/s21217165. |
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