网联车辆协同巡航系统快速滚动时域状态估计

Fast receding horizon state estimation of cooperative cruise systems of connected vehicles

考虑云平台监控下的网联车辆协同自动巡航控制(CACC)系统,提出一种快速滚动时域估计方法.采用网联车队纵向动力学模型描述网联车辆CACC系统,降低网联车辆CACC系统的状态能观性要求.再应用块概念设计滚动时域估计算法的噪声块结构,压缩滚动时域估计问题的优化变量个数,从而减少其在线计算量.进一步,应用李雅普诺夫稳定性定理证明估计误差系统的渐近稳定性.最后以5车网联车队系统仿真验证所提算法的有效性.

This paper proposes a fast receding horizon estimation method for cooperative automated cruise control(CACC)systems of connected vehicles monitored in cloud platform.The CACC system of connected vehicles is represented by the longitudinal dynamic models of connected vehicle platoons,which reduces the requirements of state observability of the CACC system of connected vehicles.Then the concept of“move blocking”is applied to design the noise block structure of the receding horizon estimation algorithm,where the number of optimization variables of the optimization problem is compressed to reduce the on-line computation.Furthermore,the Lyapunov stability theorem is used to prove asymptotic stability of the estimation error system.Finally,the effectiveness of the proposed algorithm is verified by the simulation of a five-connected vehicle fleet system.