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 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