A Fast Predictive Control Method for Vehicle Path Tracking Based on a Recurrent Neural Network

被引：1

作者：

Wu, Xialai ^{[1
]}

Lin, Ling ^{[1
]}

Chen, Junghui ^{[2
]}

Du, Shuxin ^{[1
]}

机构：

[1] Huzhou Univ, Huzhou Key Lab Intelligent Sensing & Optimal Contr, Huzhou 313000, Peoples R China

[2] Chung Yuan Christian Univ, Dept Chem Engn, Taoyuan 32023, Taiwan

来源：

IEEE ACCESS | 2024年 / 12卷

关键词：

Vehicle dynamics; Adaptation models; Accuracy; Heuristic algorithms; Data models; Computational modeling; Mixed integer linear programming; Predictive control; Recurrent neural networks; Path planning; model predictive control; recurrent neural network; symmetric saturating linear transfer functions; vehicle path tracking; OPTIMIZATION;

D O I：

10.1109/ACCESS.2024.3466971

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The paper introduces a Fast Model Predictive Control (FMPC) approach for vehicle path tracking, addressing the challenge of real-time performance in highly nonlinear systems. Utilizing a recurrent neural network with symmetric saturating linear transfer functions (SSL-RNN), our method efficiently constructs an SSL-RNN model for the vehicle. By transforming the MPC optimal control problem into a mixed integer linear programming problem, a swift online solution is achieved. Through simulations on a CarSim/Simulink platform, our FMPC outperforms RNN-based nonlinear MPC and long-short-term memory network-based MPC, demonstrating superior accuracy in vehicle path tracking and enhanced controller solution efficiency.

引用

页码：141104 / 141115

页数：12

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