Application of RBF neural network and sliding mode control for a servo mechanical press

被引：0

作者：

Liu, Chen ^{[1
]}

Zhao, Sheng-dun ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Xian 710049, Shaanxi, Peoples R China

来源：

2016 IEEE/CSAA INTERNATIONAL CONFERENCE ON AIRCRAFT UTILITY SYSTEMS (AUS) | 2016年

关键词：

FRICTION;

D O I：

暂无

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Servo mechanical press is a complicated system with several transmission processes. The friction and other nonlinear factors are critical problems of servo press controlling. This paper focuses on the performance improvements of position tracking on servo press. In order to carry out the researches, first, the mathematic model which expresses the mechanical transmission processes is built to analyze the servo screw press system. Then an algorithm which combined neural network and fuzzy sliding mode (RBFFS) was proposed and applied on the position tracking of servo press. Finally, the simulation and experiment results indicate that the RBFFS control algorithm is effective and capable for servo press controlling.

引用

页码：346 / 351

页数：6

共 50 条

[41] RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit
Song, Shengli
Zhang, Xinglong
Tan, Zhitao
[J]. STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING, 2014, 60 (06): : 437 - 446
[42] RBF Neural Network Adaptive Sliding Mode Control Based on Genetic Algorithm Optimization
Zhao Jie
Han Long
Ren Sijing
[J]. PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC), 2016, : 6772 - 6775
[43] Design of an RBF Neural Network Supervisory Controller Based on a Sliding Mode Control Approach
Son, Young Ik
Lim, Seungchul
[J]. Son, Young Ik (sonyi@mju.ac.kr), 1984, Korean Institute of Electrical Engineers (70): : 1984 - 1991
[44] Adaptive backstepping and sliding mode control of fin stabilizer based on RBF neural network
Zhang, Yuantao
Shi, Weiren
Yin, Lingling
Qiu, Mingbai
Zhao, Lin
[J]. 2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 2, 2009, : 302 - +
[45] RBF Neural Network-Based Terminal Sliding Mode Control for Reentry Warhead
Zhao, Hongchao
Chen, Jie
Liu, Huazhang
[J]. ADVANCED RESEARCH ON MECHANICAL ENGINEERING, INDUSTRY AND MANUFACTURING ENGINEERING, PTS 1 AND 2, 2011, 63-64 : 381 - +
[46] Fuzzy Sliding Mode Control Based on RBF Neural Network for AUV Path Tracking
Wang, Xiang
Zhang, Yonglin
Xue, Zhouzhou
[J]. INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2019, PT II, 2019, 11741 : 637 - 648
[47] RBF Neural Network Sliding Mode Control for Doubly Salient Electromagnetic Generator System
Dai, Weili
Zhou, Rushu
Yu, Yanghua
Zhang, Yi
[J]. 2019 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW), 2019,
[48] Chaos control of Lorenz system via RBF neural network sliding mode controller
Guo, Hui-Jun
Liu, Jun-Hua
[J]. Wuli Xuebao/Acta Physica Sinica, 2004, 53 (12): : 4080 - 4086
[49] RBF neural network PID for bilateral servo control system
Jingdong, Zhang
Guang, Wen
Yongqiao, Wei
Guofu, Yin
[J]. Telkomnika - Indonesian Journal of Electrical Engineering, 2013, 11 (09): : 5200 - 5209
[50] A RBF Neural Network Sliding Mode Controller for SMA Actuator
Tai, Nguyen Trong
Ahn, Kyoung Kwan
[J]. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2010, 8 (06) : 1296 - 1305

← 1 2 3 4 5 →