Neural network control of a four-wheel ABS model

This paper presents simulation studies of neural network controller training carried out on a four-wheel anti-lock brake system (ABS) model. The indirect controller training method utilized here requires prior training of an identification network, which is then used in a sensitivity circuit that em...

Full description

Saved in:
Bibliographic Details
Main Authors: Yuan, F., Puskorius, G.V., Feldkamp, L.A., Davis, L.I.
Format: Conference Proceeding
Language:English
Subjects:
Engines
Friction
Laboratories
Mathematical model
Neural networks
Roads
Stability
Testing
Vehicles
Wheels
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents simulation studies of neural network controller training carried out on a four-wheel anti-lock brake system (ABS) model. The indirect controller training method utilized here requires prior training of an identification network, which is then used in a sensitivity circuit that embeds the identification network and carries out a form of real-time recurrent learning. The training of both identification and controller networks makes use of a decoupled extended Kalman filter (DEKF) update scheme. To the extent that the model represents a real system, the resulting controller may be applicable to an actual vehicle. More likely, in our estimation, is that such studies will be useful in determining appropriate identification and controller architectures for on-vehicle training and in determining how to carry out such training.
DOI:10.1109/MWSCAS.1994.519091