Data-Driven Adaptive Optimal Control of Connected Vehicles

In this paper, a data-driven non-model-based approach is proposed for the adaptive optimal control of a class of connected vehicles that is composed of $n$ human-driven vehicles only transmitting motional data and an autonomous vehicle in the tail receiving the broadcasted data from preceding vehicles by wireless vehicle-to-vehicle (V2V) communication devices. Considering the cases of range-limited V2V communication and input saturation, several optimal control problems are formulated to minimize the errors of distance and velocity and to optimize the fuel usage. By employing an adaptive dynamic programming technique, the optimal controllers are obtained without relying on the knowledge of system dynamics. The effectiveness of the proposed approaches is demonstrated via the online learning control of the connected vehicles in Paramics' traffic microsimulation.