Title: | Minimum-energy neural-fuzzy approach for current/voltage-controlled electromagnetic suspension system |
Authors: | Chang, YC Wu, SJ Lee, TT 電控工程研究所 Institute of Electrical and Control Engineering |
Issue Date: | 2003 |
Abstract: | In this paper, the electromagnetic suspension system is modeled as a neural-based linear T-S fuzzy system, and then the optimal fuzzy control design scheme is proposed to control the current and voltage-controlled system with minimum current and voltage consumption, respectively. The proposed linear self-constructing neural fuzzy inference network is a six layer neural network (linear SONFIN) modified from the well-known SONFIN network, which can construct a linear T-S fuzzy model of the system just by the input and output (I/O) information. Based on the linear T-S model, we can construct the optimal fuzzy control scheme to efficiently regulate the highly nonlinear, complex and uncertain electromagnetic suspension system to the equilibrium state. |
URI: | http://hdl.handle.net/11536/18691 |
ISBN: | 0-7803-7866-0 |
Journal: | 2003 IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ROBOTICS AND AUTOMATION, VOLS I-III, PROCEEDINGS |
Begin Page: | 1405 |
End Page: | 1410 |
Appears in Collections: | Conferences Paper |