Application Study of ILC with Fuzzy Neural Network in Shaking Table Control System
Authors
Jianqiu Chen1, Xinzheng Zhang, Ping Tan, Fulin Zhou
1Department of Automation, University of Guangdong Technology
Corresponding Author
Jianqiu Chen
Available Online October 2007.
- DOI
- 10.2991/iske.2007.99How to use a DOI?
- Keywords
- FNN, ILC, system identification, shaking table.
- Abstract
This paper proposes a new approach to improve the control precision of shaking table control system, in which the fuzzy neural network (FNN) technique and iterative learn control (ILC) are combined and developed a new control technique. A FNN inverse model is built and is identified through a white noise with appropriate peak values and frequency range. Then better control effect is obtained by ILC than Remote Parameter Control (RPC). This technique is capable of improving the system precision and adaptability, and reducing the effect of structural load’s dynamic characteristic.
- Copyright
- © 2007, 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/).
Cite this article
TY - CONF AU - Jianqiu Chen AU - Xinzheng Zhang AU - Ping Tan AU - Fulin Zhou PY - 2007/10 DA - 2007/10 TI - Application Study of ILC with Fuzzy Neural Network in Shaking Table Control System BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 580 EP - 585 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.99 DO - 10.2991/iske.2007.99 ID - Chen2007/10 ER -