Adaptive neural dynamics-based speed control strategy for stable retrieval of tethered satellite system

被引:5
|
作者
Ji, Zhixiong [1 ]
Shi, Gefei [1 ,2 ]
机构
[1] Sun Yat Sen Univ, Sch Aeronaut & Astronaut, Guangzhou 510275, Peoples R China
[2] Shenzhen Key Lab Intelligent Microsatellite Conste, Shenzhen 518107, Peoples R China
基金
中国国家自然科学基金;
关键词
Tethered satellite system; Stable retrieval; Adaptive neural dynamic control; PARTIAL SPACE ELEVATOR; SLIDING MODE CONTROL; DEPLOYMENT;
D O I
10.1016/j.asr.2023.01.061
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
This work proposed a novel adaptive neural dynamics (AND)-based speed control strategy for the stable sub satellite's retrieval of the tethered satellite system (TSS). The new control scheme is implemented by adjusting the retrieval speed only. An analytical speed function is used to ensure stable retrieval without libration motion overall. A high-efficiency adaptive neural dynamic control law with the retrieval speed as control input is used to eliminate the libration motion. In the control loop, the tension in the tether is monitored and restrained directly. The Lyapunov stability of the control law is proved analytically. The simulation results show the proposed adaptive neural dynamics-based speed control strategy is very effective in keeping a stable retrieval by adjusting the retrieval speed only. The libration motions can be eliminated promptly in a fast manner with limited control input and tension constrain. (c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:4987 / 4994
页数:8
相关论文
共 50 条
  • [41] Adaptive Trajectory Tracking Control of a Quad-rotor System Based on Fuzzy Monitoring Strategy
    Song, Zhankui
    Wang, Jun
    2018 8TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST 2018), 2018, : 415 - 421
  • [42] Neural-network-based adaptive tracking control for Markovian jump nonlinear systems with unmodeled dynamics
    Chang, Ru
    Fang, Yiming
    Li, Jianxiong
    Liu, Le
    NEUROCOMPUTING, 2016, 179 : 44 - 53
  • [43] Based on RBF Neural Network Adaptive Sliding Mode Shore Power Grid-connected Control Strategy
    Zhang, Cheng
    Gao, Jian
    Su, Zhen
    2019 CHINESE AUTOMATION CONGRESS (CAC2019), 2019, : 1731 - 1735
  • [44] Adaptive neural network-based sliding mode control of rotary inverted pendulum system
    Gupta, Neha
    Dewan, Lillie
    JOURNAL OF CONTROL AND DECISION, 2024,
  • [45] Sliding mode control strategy for PMSM speed ring based on improved exponential convergence law and adaptive Lunberger observer
    Tang, Tengyu
    Deng, Yongting
    Liu, Jing
    Kang, Yuxin
    Xu, Yifan
    Fu, Jia
    Zhang, Zhimin
    Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2024, 32 (19): : 2921 - 2932
  • [46] Adaptive Speed Control of PMSM Drive System Based a New Sliding-Mode Reaching Law
    Junejo, Abdul Khalique
    Xu, Wei
    Mu, Chaoxu
    Ismail, Moustafa Magdi
    Liu, Yi
    IEEE TRANSACTIONS ON POWER ELECTRONICS, 2020, 35 (11) : 12110 - 12121
  • [47] Neural learning-based dual channel event-triggered deployment control of space tethered system with intermittent output
    Huang, Bingxiao
    Zhang, Fan
    Song, Mengshi
    Huang, Panfeng
    ACTA ASTRONAUTICA, 2023, 213 : 537 - 546
  • [48] Observer-based neural adaptive control of a platoon of autonomous tractor-trailer vehicles with uncertain dynamics
    Elhaki, Omid
    Shojaei, Khoshnam
    IET CONTROL THEORY AND APPLICATIONS, 2020, 14 (14) : 1898 - 1911
  • [49] Adaptive position tracking control system based on recurrent fuzzy wavelet neural networks for robot manipulators
    ThangLong Mai
    Wang, YaoNan
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2014, 228 (07) : 500 - 520
  • [50] Neural Network Based Adaptive Backstepping Control for Electro-Hydraulic Servo System Position Tracking
    Wan, Zhenshuai
    Yue, Longwang
    Fu, Yu
    INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING, 2022, 2022