A hierarchical anticipatory neural controller with fuzzy spectral filter diagnostics

Author

Tascillo, Anya L.

Author_Institution

Allen Park Test Lab., Ford Motor Co., Allen Park, MI, USA

Volume

1

fYear

1995

fDate

Nov/Dec 1995

Firstpage

334

Abstract

A full state feedback recurrent (FSFER) neural network architecture is developed as a best representation in both the time and frequency domains for engine and chassis dynamometer modelling and control. In order to reduce the lag experienced by current robotic driver controllers, a fuzzy spectral filter is combined with radial basis function neural networks to suggest a best time to apply a throttle or brake input before velocity error feedback is available

Keywords

automobiles; feedforward neural nets; fuzzy control; fuzzy neural nets; hierarchical systems; internal combustion engines; modelling; neural net architecture; neurocontrollers; recurrent neural nets; robots; state feedback; automotive engine; brake input; chassis; dynamometer modelling; frequency domains; fuzzy spectral filter; fuzzy spectral filter diagnostics; hierarchical anticipatory neural controller; neural network architecture; radial basis function neural networks; robotic drive; state feedback recurrent neural network; throttle input; time domains; Engines; Filters; Frequency domain analysis; Fuzzy control; Fuzzy neural networks; Neural networks; Recurrent neural networks; Robot control; State feedback; Velocity control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1995. Proceedings., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2768-3

Type

conf

DOI

10.1109/ICNN.1995.488120

Filename

488120