A hierarchical anticipatory neural controller with fuzzy spectral filter diagnostics

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.