The Tracking Control of Unmanned Underwater Vehicles Based on QPSO-Model Predictive Control

被引：1
|
作者：
Gan, Wenyang [1 ]
Zhu, Daqi [1 ]
Sun, Bing [1 ]
Luo, Chaomin [2 ]
机构：
[1] Shanghai Maritime Univ, Lab Underwater Vehicles & Intelligent Syst, Shanghai 201306, Peoples R China
[2] Univ Detroit Mercy, Dept Elect & Comp Engn, Detroit, MI 48221 USA
来源：
INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2017, PT I | 2017年 / 10462卷
关键词：
Unmanned Underwater Vehicle; Trajectory tracking; Quantum-behaved particle swarm optimization; Model predictive control; Backstepping control;
D O I：
10.1007/978-3-319-65289-4_66
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
For the trajectory tracking control of Unmanned Underwater Vehicles (UUV), an improved Model Predictive Control (MPC) method based on Quantum-behaved Particle Swarm Optimization (QPSO) is proposed. The concept of trajectory tracking is given firstly in this paper. Then QPSO-MPC is employed to realize the tracking control. The QPSO problem is suggested to optimization problem of minimizing the objective function with the conditions of satisfying the control constraints. The simulation results which is under the two-dimensional situation show that QPSO-MPC can effectively solve the speed jump problem. More effective and feasible for trajectory tracking problem compared with backstepping control method.
引用
收藏
页码：711 / 720
页数：10
相关论文
共 50 条
  • [1] QPSO-model predictive control-based approach to dynamic trajectory tracking control for unmanned underwater vehicles
    Gan, Wenyang
    Zhu, Daqi
    Ji, Daxiong
    [J]. OCEAN ENGINEERING, 2018, 158 : 208 - 220
  • [2] THE TRACKING CONTROL OF UNMANNED UNDERWATER VEHICLES BASED ON MODEL PREDICTIVE CONTROL
    Zhu, Daqi
    Mei, Man
    Sun, Bing
    [J]. INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION, 2017, 32 (04): : 351 - 359
  • [3] Trajectory Tracking of Unmanned Underwater Vehicles based on Model Predictive Control in Two Dimension
    Gan, WenYang
    Zhu, Daqi
    Sun, Bing
    [J]. PROCEEDINGS OF THE 2016 IEEE 11TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), 2016, : 212 - 217
  • [4] Path Tracking of Underwater Vehicles Based on Adaptive Model Predictive Control
    Yan, Jinghao
    Wang, Weiran
    Xu, Meng
    Yang, Guanjun
    Zhu, Zhiyu
    [J]. PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 5786 - 5791
  • [5] Research on Model Predictive Control-based Trajectory Tracking for Unmanned Vehicles
    Yuan, Shoutong
    Zhao, Pengchao
    Zhang, Qingyu
    Hu, Xin
    [J]. 2019 4TH INTERNATIONAL CONFERENCE ON CONTROL AND ROBOTICS ENGINEERING (ICCRE), 2019, : 79 - 86
  • [6] Research on Trajectory Tracking of Unmanned Tracked Vehicles Based on Model Predictive Control
    Hu, Jiaming
    Hu, Yuhui
    Chen, Huiyan
    Liu, Kai
    [J]. Binggong Xuebao/Acta Armamentarii, 2019, 40 (03): : 456 - 463
  • [7] Model Predictive Adaptive Constraint Tracking Control for Underwater Vehicles
    Gan, Wenyang
    Zhu, Daqi
    Hu, Zhen
    Shi, Xianpeng
    Yang, Lei
    Chen, Yunsai
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2020, 67 (09) : 7829 - 7840
  • [8] Nonlinear model predictive tracking control for rotorcraft-based unmanned aerial vehicles
    Kim, HJ
    Shim, DH
    Sastry, S
    [J]. PROCEEDINGS OF THE 2002 AMERICAN CONTROL CONFERENCE, VOLS 1-6, 2002, 1-6 : 3576 - 3581
  • [9] Optimization of the Energy Consumption of Depth Tracking Control Based on Model Predictive Control for Autonomous Underwater Vehicles
    Yao, Feng
    Yang, Chao
    Zhang, Mingjun
    Wang, Yujia
    [J]. SENSORS, 2019, 19 (01)
  • [10] Neurodynamics-Based Model Predictive Control for Trajectory Tracking of Autonomous Underwater Vehicles
    Wang, Xinzhe
    Wang, Jun
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2014, 2014, 8866 : 184 - 191
12345