Autonomous vehicle trajectory following with flatness-based model predictive control and real-time iteration

被引:0
作者
Wang, Wei [1 ]
Wang, Zejiang [2 ]
Hu, Chuan [3 ]
Chen, Xinbo [4 ]
Raksincharoensak, Pongsathorn [1 ]
机构
[1] Tokyo Univ Agr & Technol, Dept Mech Syst Engn, Tokyo, Japan
[2] Oak Ridge Natl Lab, Oak Ridge, TN USA
[3] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai, Peoples R China
[4] Tongji Univ, Sch Automot Studies, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
Trajectory following; model predictive control; differential flatness; field tests; DIFFERENTIAL FLATNESS; TRACKING CONTROL; FAULT-DIAGNOSIS; SYSTEMS;
D O I
10.1177/09596518251322238
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Model Predictive Control (MPC) is increasingly gaining popularity in a wide range of autonomous vehicle and driving assistant applications owing to its style of optimality and convenience in handling multiple constraints. However, a primary drawback in online applications lies in the high computational burden caused by the complex non-linearity in the vehicle system and driving stability constraints. The differential flatness property of the vehicle motion system offers a solution by treating the error signals as flat outputs for endogenous feedback linearization. Nevertheless, this approach introduces a new challenge of dealing with time-varying implicit constraints. To address this issue, our work combines flatness-based MPC design with real-time iterative handling of lateral stability constraints, leading to a pure linear Quadratic Programming (QP) problem that can be efficiently solved online. Through numerical simulations conducted under two challenging scenarios, we demonstrate that our proposed method outperforms conventional linear-time-varying MPC in terms of linearization error and dynamic performance. Finally, field tests conducted with a four-wheel independent driving and four-wheel independent steering chassis platform validate the feasibility and substantial potential of our presented method in autonomous driving applications.
引用
收藏
页码:984 / 999
页数:16
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