A hybrid neuro-fuzzy system for adaptive vehicle separation control

The primary purpose of this paper is to develop a robust adaptive vehicle s eparation control in the increasingly important roles of intelligent transp ortation system (ITS). A hybrid neuro-fuzzy system (HNFS) is proposed for d eveloping the adaptive vehicle separation control to minimize the distance (headway) between successive cars. This hybrid system consists of two modul es: a headway identification (prediction) module and a control decision mod ule. Each of these modules is realized with a distinct neuro-fuzzy network that upgrades hierarchical granularity and reduces the complexity in the co ntrol system. Given the current headway and relative velocity between the t wo consecutive cars, the headway identification module predicts the headway of the next time instant. This identified headway, together with the desir ed velocity are input to the control decision module whose output is the ac tual acceleration/deceleration control output. The HNFS encapsulates the ad aptive learning capabilities of a neural network into a fuzzy logic control framework to fine-tune the fuzzy control rules. On the other hand, rules d erived initially from well-defined fuzzy phase plane accelerate the trainin g of the neural network. Simulation results are very encouraging. It is obs erved that the headway decreases significantly without sacrificing speed. F urthermore, both stability and robustness of HNFS are demonstrated.