Optimal control methods in intelligent vehicles

被引：0
作者：
Chen H. [1 ,2 ]
Guo L. [1 ,2 ]
Qu T. [1 ]
Gao B. [1 ]
Wang F. [1 ]
机构：
[1] State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun
[2] Department of Control Science and Engineering, Jilin University, Changchun
来源：
Chen, Hong (chenh@jlu.edu.cn) | 1600年 / Taylor and Francis Ltd.卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Control technology; eco-driving; intelligent vehicles; MPC-based driver modelling;
D O I：
10.1080/23307706.2016.1254072
中图分类号：
学科分类号：
摘要：
In recent years, intelligent vehicles (IVs) have become a hot spot in automotive industry. Key technologies of IVs range over the field of sensing, decision-making and control. Among them, control technology provides an enabling support for improving autonomous driving safety, reducing energy consumption and carbon emission. This paper focuses on some aspects of applying advanced control methodologies in IVs through several selected examples including eco-driving and MPC-based driver modelling. © 2016, © 2016 Northeastern University, China.
引用
收藏
页码：32 / 56
页数：24
相关论文
共 50 条
  • [21] Optimized Longitudinal and Lateral Control Strategy of Intelligent Vehicles Based on Adaptive Sliding Mode Control
    Wang, Yun
    Wang, Zhanpeng
    Shi, Dapai
    Chu, Fulin
    Guo, Junjie
    Wang, Jiaheng
    WORLD ELECTRIC VEHICLE JOURNAL, 2024, 15 (09):
  • [22] Longitudinal and Lateral Comprehensive Trajectory Tracking Control of Intelligent Vehicles Based on NMPC
    Chen L.
    Zou K.
    Cai Y.
    Teng C.
    Sun X.
    Wang H.
    Qiche Gongcheng/Automotive Engineering, 2021, 43 (02): : 153 - 161
  • [23] Intelligent Vehicles Formation Control Based on Artificial Potential Field and Virtual Leader
    Wang S.
    Zhang J.
    Zhang J.
    Zhang, Junyou (308181387@qq.com), 1600, Shanghai Jiaotong University (54): : 305 - 311
  • [24] Sensors and Sensing for Intelligent Vehicles
    Llorca, David Fernandez
    Daza, Ivan Garcia
    Parra, Noelia Hernandez
    Alonso, Ignacio Parra
    SENSORS, 2020, 20 (18) : 1 - 23
  • [25] Vision technologies for intelligent vehicles
    Bertozzi, Massimo
    Broggi, Alberto
    Bombini, L.
    Caraffi, C.
    Cattani, S.
    Cerri, Pietro
    Fascioli, Alessandra
    Felisa, M.
    Fedriga, R. I.
    Ghidoni, S.
    Grisleri, Paolo
    Medici, P.
    Paterlini, M.
    Porta, P. P.
    Posterli, M.
    Zani, P.
    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS: KES 2007 - WIRN 2007, PT I, PROCEEDINGS, 2007, 4692 : 704 - +
  • [26] Speed supervisor for Intelligent Vehicles
    Carrasco, J. P.
    de la Escalera, A.
    Armingol, J. M.
    COMPUTER AIDED SYSTEMS THEORY- EUROCAST 2007, 2007, 4739 : 1175 - 1182
  • [27] Endowing Intelligent Vehicles with the Ability to Learn User's Habits and Preferences with Machine Learning Methods
    Barbosa, Paulo
    Ferreira, Flora
    Fernandes, Carlos
    Erlhagen, Wolfram
    Guimaraes, Pedro
    Wojtak, Weronika
    Monteiro, Sergio
    Bicho, Estela
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2022, 2022, 13756 : 157 - 169
  • [28] Model Predictive Control for Intelligent Vehicles Fusing Feed-forward and State Feedback
    Chen, Qiping
    Cao, Tianheng
    Huang, Shaotang
    Jiang, Huihua
    Jiang, Zhiqiang
    Shi, Lequan
    Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences, 2024, 51 (08): : 165 - 175
  • [29] Multi-Kernel Online Reinforcement Learning for Path Tracking Control of Intelligent Vehicles
    Liu, Jiahang
    Huang, Zhenhua
    Xu, Xin
    Zhang, Xinglong
    Sun, Shiliang
    Li, Dazi
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 51 (11): : 6962 - 6975
  • [30] Model Predictive Path Following Control of Intelligent Vehicles Considering System Complex Disturbances
    Guan L.
    Gu Z.
    Zhang C.
    Wang A.
    Peng C.
    Jiang H.
    Wu X.
    Qiche Gongcheng/Automotive Engineering, 2022, 44 (12): : 1844 - 1855+1876
12345