Connected cruise control with delayed feedback and disturbance: An adaptive dynamic programming approach

This paper studies the connected cruise control problem for a platoon of human-operated and autonomous vehicles. The autonomous vehicles can receive motional data, ie, headway and velocity information from other vehicles by wireless vehicle-to-vehicle communication. The use of wireless communications in information exchange between vehicles inevitably causes input delay in the platooning system. Meanwhile, unpredictable behaviors of the leading vehicle constitute exogenous disturbance for the system. An adaptive optimal control problem with input delay and disturbance is formulated, and a novel data-driven control solution is proposed such that each vehicle in the platoon can achieve safe distance and desired velocity. By adopting an adaptive dynamic programming technique with sampled-data system theory, a data-driven adaptive optimal control approach is proposed for autonomous vehicles by the learning strategies of policy iteration without the accurate knowledge of the dynamics of all human drivers and vehicles. The efficacy of the proposed controller is substantiated by rigorous analysis and validated by simulation results in different scenarios.