Input Constraints Sliding Mode Control Based on RBF Network Compensation

DOI

10.2991/icismme-15.2015.374How to use a DOI?

Keywords

synovial control; RBF network; limited inputs

Abstract

RBF network is an efficient feed-forward neural network with the best performance and the global optimal approximation properties. Synovial variable structure control has many advantages, such as corresponding fast algorithm, the system parameters and external disturbance invariant, and its algorithm is simple and easy to implement. Thus, it has been becoming a hot spot in recent years to solve the problem of complex nonlinear systems research. This article mainly discusses how to combine the synovial variable structure with the RBF network to produce more superior performance and neural network variable structure control. The algorithm focuses on how to improve the convergence of the network.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Mengya Hou
AU  - Huanqiang Chen
PY  - 2015/07
DA  - 2015/07
TI  - Input Constraints Sliding Mode Control Based on RBF Network Compensation
BT  - Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
PB  - Atlantis Press
SP  - 1808
EP  - 1812
SN  - 1951-6851
UR  - https://doi.org/10.2991/icismme-15.2015.374
DO  - 10.2991/icismme-15.2015.374
ID  - Hou2015/07
ER  -