Earticle

초록

영어

Virtual axis machine tool is widely used in the machining process of complicated curved surface, pointing on the uncertain factors that exists in the virtual axis machine tool system will affect the process accuracy of the virtual axis machine tool, also take the problem that the upper bound of the interference of the actual system are unable to be measured into considering, in this paper, a sliding model control scheme with upper bound adaptive learning based on RBF networks, and the proposed scheme is realized in the MATLAB platform. The simulation results revealed that compared with the traditional sliding model control, the proposed control algorithm has the good performance on position tracking, the error upper bound prediction, chattering reducing, fasting convergence and so forth.

목차

Abstract
 1. Introduction
 2. Control System for Virtual Axis Machine Tool
 3. System Description
 4. Design for Controller
  4.1. Control Rule for the Nominal Model Part
  4.2. The design of the Sliding Mode Compensator When Upper Bound is Known
 5. The Sliding Mode Compensator based on RBF Upper Bound Adaptive Learning
 6. Numerical Simulation
 7. Conclusions
 Acknowledgements
 References

키워드

저자

• Yu Zhao [ School of Basic Education, Jiangsu Food & Pharmaceutical Science College, Huaian, 223000, P. R. China ]
• Yongfeng Cui [ College of Network Engineering, Zhoukou Normal University, Zhoukou 466001, P. R. China ]
• Miaochao Chen [ School of Applied of Mathematics, Chaohu University, Hefei 466001, P. R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605