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

共 50 条

[1] Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking
Rokonuzzaman, Mohammad
Mohajer, Navid
Nahavandi, Saeid
Mohamed, Shady
IEEE ACCESS, 2021, 9 : 128233 - 128249
[2] Recurrent-Neural-Network-Based Predictive Control of Piezo Actuators for Trajectory Tracking
Xie, Shengwen
Ren, Juan
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2019, 24 (06) : 2885 - 2896
[3] Application of Optimal Control Method to Path Tracking Problem of Vehicle
Liu, Yingjie
Wang, Enhao
IEEE ACCESS, 2024, 12 : 192172 - 192183
[4] Differential recurrent neural network based predictive control
Al Seyab, R. K.
Cao, Y.
COMPUTERS & CHEMICAL ENGINEERING, 2008, 32 (07) : 1533 - 1545
[5] Differential Recurrent Neural Network based Predictive Control
Al Seyab, Rihab
Cao, Yi
16TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING AND 9TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING, 2006, 21 : 1239 - 1244
[6] Progressive Barrier Tightened Fast Nonlinear Model Predictive Control for High-Performance Path Tracking
Dong, Shiying
Shi, Wentong
Gao, Bingzhao
Chen, Hong
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, 2025, 11 (02): : 5445 - 5456
[7] Support Vector Machine Based Model Predictive Control for Vehicle Path Tracking Control
Xu, Fang
Qu, Yi
Qu, Ting
Chen, Hong
Liang, Dinan
PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE, 2020, : 2461 - 2466
[8] Model predictive path following control of underwater vehicle based on RBF neural network
Guo L.
Gao J.
Jiao H.
Song Y.
Chen Y.
Pan G.
Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University, 2023, 41 (05): : 871 - 877
[9] Recurrent Fuzzy-Neural Network with Fast Learning Algorithm for Predictive Control
Todorov, Yancho
Terzyiska, Margarita
Petrov, Michail
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2013, 2013, 8131 : 459 - 466
[10] Model Predictive Control Based on Recurrent Neural Network
Liang, Xiao
Cui, Baotong
Lou, Xuyang
2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2014, : 4835 - 4839

← 1 2 3 4 5 →