Sliding Mode Force Control of an Electrohydraulic Servo System with RBF Neural Network Compensation

被引：4

作者：

Lu, Xinliang ^{[1
]}

Du, Fengpo ^{[2
]}

Jia, Qian ^{[3
]}

Ren, Bin ^{[1
]}

Wang, Xingsong ^{[2
]}

机构：

[1] Shijiazhuang Tiedao Univ, Sch Mech Engn, Shijiazhuang 050043, Hebei, Peoples R China

[2] Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R China

[3] Nanjing Inst Technol, Ind Ctr, Nanjing 211167, Jiangsu, Peoples R China

来源：

MECHANIKA | 2019年 / 25卷 / 01期

关键词：

electrohydraulic; RBF; neural network; sliding mode; compensation control; trajectory tracking;

D O I：

10.5755/j01.mech.25.1.21279

中图分类号：

O3 [力学];

学科分类号：

08 ; 0801 ;

摘要：

In this paper, the dynamics of an electrohydraulic servo system is analyzed. It is difficult to achieve the precise force tracking control due to its high nonlinearities and parameter uncertainties For the accurate force tracking control, a sliding mode control algorithm with radial basis function (RBF) neural network compensation was proposed. The theory verifies that the algorithm is globally asymptotically stable. The experimental results show that the proposed algorithm is not only effective and better than the PID control in the force tracking control, but also robust to external uncertain disturbances.

引用

页码：32 / 37

页数：6

共 50 条

[41] New Sliding Mode Control Based on Tracking Differentiator and RBF Neural Network
Qu, Chunyu
Hu, Yongzhuang
Guo, Ziqi
Han, Fangxu
Wang, Xiuping
[J]. ELECTRONICS, 2022, 11 (19)
[42] Adaptive RBF neural network sliding mode control for a DEAP linear actuator
Qiu, Dehui
Chen, Yu
Li, Yuan
[J]. International Journal of Performability Engineering, 2017, 13 (04) : 400 - 408
[43] Sliding mode control based on RBF neural network for parallel machine tool
School of Basic Education, Jiangsu Food and Pharmaceutical Science College, Huaian, China
不详
不详
[J]. Open Autom. Control Syst. J., 1 (575-582):
[44] Complementary Sliding Mode Control via Elman Neural Network for Permanent Magnet Linear Servo System
Jin, Hongyan
Zhao, Ximei
[J]. IEEE ACCESS, 2019, 7 : 82183 - 82193
[45] Design of ROV Adaptive Sliding Mode Control System for Underwater Vehicle Based on RBF Neural Network
Chen, Wei
Hu, Shilin
Wei, Qingyu
[J]. PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 2976 - 2981
[46] Chaos Control of Fractional Order Lu System via RBF Neural Network Sliding Mode Controller
Yan, Xiaomei
Liu, Ding
[J]. 2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2010, : 5099 - 5104
[47] A sliding mode variable structure control approach for a pneumatic force servo system
Li, Ruihua
Meng, Guoxiang
Feng, Zhengjin
Li, Yijie
Shi, Weixiang
[J]. WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 8173 - +
[48] Hybrid Adaptive Integral Sliding Mode Speed Control of PMSM System Using RBF Neural Network
Zhang, Bin
Gao, Xinyan
[J]. 2020 INTERNATIONAL SYMPOSIUM ON POWER ELECTRONICS, ELECTRICAL DRIVES, AUTOMATION AND MOTION (SPEEDAM 2020), 2020, : 17 - 22
[49] RBF neural network based backstepping terminal sliding mode MPPT control technique for PV system
Khan, Zain Ahmad
Khan, Laiq
Ahmad, Saghir
Mumtaz, Sidra
Jafar, Muhammad
Khan, Qudrat
[J]. PLOS ONE, 2021, 16 (04):
[50] Adaptive RBF Neural Network Control Method for Pneumatic Position Servo System
Ren, Hai-Peng
Jiao, Shan-Shan
Wang, Xuan
Li, Jie
[J]. IFAC PAPERSONLINE, 2020, 53 (02): : 8826 - 8831

← 1 2 3 4 5 →