An adaptive learning and control architecture for mitigating sensor and actuator attacks in connected autonomous vehicle platoons

In this paper, we develop an adaptive control algorithm for addressing security for a class of networked vehicles that comprise a formation of (Formula presented.) human-driven vehicles sharing kinematic data and an autonomous vehicle in the aft of the vehicle formation receiving data from the preceding vehicles through wireless vehicle-to-vehicle communication devices. Specifically, we develop an adaptive controller for mitigating time-invariant state-dependent adversarial sensor and actuator attacks while guaranteeing uniform ultimate boundedness of the closed-loop networked system. Furthermore, an adaptive learning framework is presented for identifying the state space model parameters based on input-output data. This learning technique utilizes previously stored data as well as current data to identify the system parameters using a relaxed persistence of excitation condition. The effectiveness of the proposed approach is demonstrated by an illustrative numerical example involving a platoon of connected vehicles.