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Minimum-energy neural-fuzzy approach for current/voltage-controlled electromagnetic suspension system

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3 Author(s)
Yen-Chen Chang ; Dept. of Electr. Eng., Da-Yeh Univ., Chang-Hwa, Taiwan ; Shinq-Jen Wu ; Tsu-Tian Lee

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-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 form 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.

Published in:

Computational Intelligence in Robotics and Automation, 2003. Proceedings. 2003 IEEE International Symposium on  (Volume:3 )

Date of Conference:

16-20 July 2003