Title :
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
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;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.488120
