Adaptive neural network-based sliding mode control for a hydraulic rotary drive joint

被引：10
|
作者：
Yang, Mingxing [1 ,2 ,3 ]
Zhang, Xing [1 ,2 ]
Xia, Yulei [1 ,2 ]
Liu, Qingyun [1 ,2 ]
Zhu, Qing [1 ,2 ]
机构：
[1] Anhui Univ Technol, Anhui Prov Key Lab Special Heavy Load Robot, Maanshan 243032, Peoples R China
[2] Anhui Univ Technol, Sch Mech Engn, Maanshan 243032, Peoples R China
[3] Haisida Robot Co Anhui Prov, Maanshan 243000, Peoples R China
来源：
COMPUTERS & ELECTRICAL ENGINEERING | 2022年 / 102卷
关键词：
Adaptive sliding mode controller; Higher-order neural network; Parameter uncertainties; Position tracking; SERVO SYSTEM; ACTUATORS;
D O I：
10.1016/j.compeleceng.2022.108189
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Hydraulic system has specific nonlinear and unknown modeling characteristics, realizing precise tracking control is a very challenging task. In this study, a sliding mode controller based on an adaptive higher-order neural network is proposed for realizing precise position-tracking control of a hydraulic rotary drive joint. First, the structural design and working principle of the target joint are introduced, and the mathematical model of the corresponding valve-controlled hydraulic position servo system is developed. Then, the adaptive neural network algorithm and sliding mode control are effectively combined. Based on the measurement information obtained from the control process, the feedback error is used to adaptively approximate the control parameters and realize online adjustment of the controller output. Finally, the parameter identification and position tracking of the system are validated, and the results indicate that the proposed strategy exhibits a 20% improvement in position-tracking control compared to the conventional sliding mode control methods.
引用
收藏
页数：12
相关论文
共 50 条
  • [1] Adaptive neural network-based sliding mode control of rotary inverted pendulum system
    Gupta, Neha
    Dewan, Lillie
    JOURNAL OF CONTROL AND DECISION, 2024,
  • [2] Neural Network-Based Adaptive Sliding Mode Control for Gyroelastic Body
    Jia, Shiyuan
    Shan, Jinjun
    IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2019, 55 (03) : 1519 - 1527
  • [3] Neural network-based sliding mode adaptive control for robot manipulators
    Sun, Tairen
    Pei, Hailong
    Pan, Yongping
    Zhou, Hongbo
    Zhang, Caihong
    NEUROCOMPUTING, 2011, 74 (14-15) : 2377 - 2384
  • [4] Neural Network-Based Adaptive Sliding Mode Control Strategy for Underactuated RTAC
    Tan Panlong
    Qin Huayang
    Sun Mingwei
    Sun Qinglin
    Chen Zengqiang
    Wang Yongshuai
    2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 6244 - 6249
  • [5] Neural network-based adaptive fractional-order terminal sliding mode control
    Hou, Shixi
    Wang, Cheng
    Chu, Yundi
    Fei, Juntao
    TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2022, 44 (16) : 3107 - 3117
  • [6] Adaptive Neural Network-based Visual Servoing with Integral Sliding Mode Control for Manipulator
    Zeng, Haibin
    Lu, Zhihui
    Lv, Yueyong
    Qi, Jiaming
    2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 3567 - 3572
  • [7] RBF Neural Network Adaptive Sliding Mode Control of Rotary Stewart Platform
    Tan Van Nguyen
    Ha, Cheolkeun
    INTELLIGENT COMPUTING METHODOLOGIES, ICIC 2018, PT III, 2018, 10956 : 149 - 162
  • [8] Neural network-based sliding mode control of electronic throttle
    Baric, M
    Petrovic, I
    Peric, N
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2005, 18 (08) : 951 - 961
  • [9] Neural network-based adaptive sliding mode control design for position and attitude control of a quadrotor UAV
    Razmi, Hadi
    Afshinfar, Sima
    AEROSPACE SCIENCE AND TECHNOLOGY, 2019, 91 : 12 - 27
  • [10] Chebyshev Neural Network-Based Adaptive Nonsingular Terminal Sliding Mode Control for Hypersonic Vehicles
    Zhang, Ruimin
    Chen, Qiaoyu
    Guo, Haigang
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2020, 2020 (2020)
12345