A Model Predictive Control Based Path Tracker in Mixed-Domain

被引:7
|
作者
Hu, Jia [1 ]
Feng, Yongwei [1 ]
Li, Xin [2 ]
Wang, Haoran [1 ]
机构
[1] Tongji Univ, Key Lab Rd & Traff Engn, Minist Educ, Shanghai 201804, Peoples R China
[2] Dalian Maritime Univ, Coll Transportat Engn, Dalian, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
MOBILE ROBOT; TRAJECTORY TRACKING; AUTONOMOUS VEHICLES; NEURAL-NETWORK; DESIGN; SYSTEM;
D O I
10.1109/IV48863.2021.9575934
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This research proposes a Model Predictive Control (MPC) based path tracker controller. It is designed for maneuvering an autonomous driving vehicle to follow its desired trajectory smoothly and accurately. The proposed path tracker has the following features: i) formulated in the time and space mixed-domain to improved control accuracy ii) with consideration of vehicle dynamics; iii) with consideration of vehicle control delay. Simulation and field test results demonstrate that the maximum longitudinal speed error is 2.3km/h and the maximum lateral position error is 11cm. It is 27% smaller than that of the conventional path-trackers. Moreover, the average computation time of the proposed path-tracker is 12 milliseconds on a laptop equipped with an Intel i7-4710MQ CPU. It indicates that the proposed path tracker is ready for real-time implementation.
引用
收藏
页码:1255 / 1260
页数:6
相关论文
共 50 条
  • [41] Echidna: Mixed-domain Computational Implementation via Decision Trees
    Merrill, Devon J.
    Garza, Jorge
    Swanson, Steven
    ACM SYMPOSIUM ON COMPUTATIONAL FABRICATION (SCF 2019), 2019,
  • [42] A novel design methodology for the mixed-domain optimization of a MEMS accelerometer
    Pak, Murat
    Fernandez, Francisco, V
    Dundar, Gunhan
    INTEGRATION-THE VLSI JOURNAL, 2018, 62 : 314 - 321
  • [43] Mixed-Signal and Mixed-Domain Instrumentation for Emerging Technology Device Characterization
    Ribeiro, Diogo
    Boaventura, Alirio
    Cruz, Pedro
    Carvalho, Nuno Borges
    2014 XXXITH URSI GENERAL ASSEMBLY AND SCIENTIFIC SYMPOSIUM (URSI GASS), 2014,
  • [44] Obstacle Avoidance Path Planning Algorithm Based on Model Predictive Control
    Kim, Ji Chang
    Pae, Dong Sung
    Lim, Myo Taeg
    2018 18TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS), 2018, : 141 - 143
  • [45] Learning Based Model Predictive Path Tracking Control for Autonomous Buses
    Han, Mo
    He, Hongwen
    Cao, Jianfei
    Wu, Jingda
    Liu, Wei
    Shi, Man
    2024 35TH IEEE INTELLIGENT VEHICLES SYMPOSIUM, IEEE IV 2024, 2024, : 2680 - 2687
  • [46] Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control
    Bai, Guoxing
    Liu, Li
    Meng, Yu
    Luo, Weidong
    Gu, Qing
    Ma, Baoquan
    APPLIED SCIENCES-BASEL, 2019, 9 (07):
  • [47] Model Predictive Interaction Control based on a Path-Following Formulation
    Goller, Tim
    Gold, Tobias
    Voelz, Andreas
    Graichen, Knut
    PROCEEDINGS OF 2022 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (IEEE ICMA 2022), 2022, : 551 - 556
  • [48] Path Planning Scheme for AUV Based on Improved Model Predictive Control
    Deng, Si-Yi
    Hao, Li-Ying
    Wu, Zhi-Jie
    2023 IEEE 2ND INDUSTRIAL ELECTRONICS SOCIETY ANNUAL ON-LINE CONFERENCE, ONCON, 2023,
  • [49] Path tracking of automatic parking based on nonlinear model predictive control
    Gu Q.
    Bai G.-X.
    Meng Y.
    Liu L.
    Luo W.-D.
    Gan X.
    Gongcheng Kexue Xuebao/Chinese Journal of Engineering, 2019, 41 (07): : 947 - 954
  • [50] Path Tracking and Obstacle Avoidance Algorithm Based on Model Predictive Control
    Wang Xiaohua
    Han Mingyu
    Gao Zhiyuan
    Li Li
    Miao Zhonghua
    2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2022, : 2402 - 2407