Robust Intelligent Backstepping Longitudinal Control of Vehicle Platoons with H∞ Tracking Performance

This paper proposes a robust intelligent backstepping control (RIBC) scheme for the car-following control of a platoon of automated vehicles using a recurrent cerebellar model articulation controller (RCMAC) via the H^infin control technique, so that the robust tracking performance can be achieved. In the RIBC system, an adaptive RCMAC is used to mimic an ideal backstepping control law and a robust controller is designed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. Moreover, the Taylor linearization technique is employed to derive the linearized model of the RCMAC. The adaptation laws of the RIBC system are derived on the basis of the Lyapunov stability analysis and H^infin control theory so that the stability of the system can be guaranteed. Finally, the simulation results denominate that the proposed RIBC system can achieve favorable tracking performance for a safe car-following control.