Adaptive intelligent backstepping longitudinal control of vehicleplatoons using output recurrent cerebellar model articulation controller

Abstract

Automatic vehicle-following on traffic safety has been an active area of research. This paper is concerned with the adaptive intelligent backstepping longitudinal control (AIBLC) system for the vehicle-following control of a platoon of automated vehicles. In the proposed control system, an adaptive output recurrent cerebellar model articulation controller (ORCMAC) is used to mimic an ideal backstepping control and a robust controller is designed to attenuate the effects caused by lumped uncertainty term (such as unmodeled dynamics, external disturbances and approximate errors), so that the H ∞ tracking performance can be achieved. Moreover, the Taylor linearization technique is employed to derive the linearized model of the ORCMAC. The adaptation laws of the AIBLC system are derived on the basis of the Lyapunov stability analysis and H ∞ control theory so that the stability of the closed-loop system can be guaranteed. Finally, the simulation results denominate that the proposed AIBLC system can achieve favorable tracking performance for a safe vehicle-following control.