Model Predictive Trajectory Tracking Based on Extended Kalman Filter

被引：0

作者：

Jiang, Yue ^{[1
]}

Peng, Kai ^{[2
]}

Ma, Zeguo ^{[1
]}

Wang, Hongxia ^{[1
]}

机构：

[1] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China

[2] Northwestern Polytech Univ, Sch Power & Energy, Xian 710072, Peoples R China

来源：

2023 2ND CONFERENCE ON FULLY ACTUATED SYSTEM THEORY AND APPLICATIONS, CFASTA | 2023年

基金：

中国国家自然科学基金;

关键词：

Vehicle trajectory tracking; extended Kalman filter; model predictive control; multivariate analysis; CONTROLLER;

D O I：

10.1109/CFASTA57821.2023.10243377

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Vehicle trajectory tracking is one of the core technologies in the field of automatic driving, various control algorithms are currently used in trajectory tracking. Among existing algorithms, model predictive control (MPC) outperforms other algorithms because it consists of model prediction, receding optimization, and feedback correction. Previous studies have designed trajectory-tracking algorithms under stable road conditions. However, the actual driving scenario is no longer stable, meaning those algorithms may not be applicable. Therefore, we establish a kinematic model for the vehicle with additive noises and propose a trajectory-tracking algorithm based on model predictive control and the extended Kalman filter (EKF). The algorithm can still guarantee real-time calculation and high tracking accuracy under unstable road conditions.

引用

页码：906 / 912

页数：7

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