Supervisory recurrent fuzzy neural network control for vehicle collision avoidance system design

This paper develops an intelligent method called supervisory recurrent fuzzy neural network (SRFNN) control to deal with the vehicle collision avoidance system (VCAS), which is an uncertain nonlinear model-free system. This SRFNN control system is composed of a recurrent fuzzy neural network (RFNN) controller and a supervisory controller. The RFNN controller is investigated to mimic an ideal controller, and the supervisory controller is designed to compensate for the approximation error between the RFNN controller and the ideal controller. This SRFNN control is employed to keep the VCAS within a safety range to avoid traffic accidences. The simulation results show the performance and effectiveness of the proposed control system are better than that obtained by formal formula-based control.