An adaptive learning and control architecture for mitigating sensor and actuator attacks in connected autonomous vehicle platoons. (25th June 2019)

• Record Type: Journal Article
• Authors: Jin, Xu; Haddad, Wassim M.; Jiang, Zhong‐Ping; Kanellopoulos, Aris; Vamvoudakis, Kyriakos G.
• Other Names: Pan Yongping guestEditor.; Bobtsov Alexey guestEditor.; Darouach Mohamed guestEditor.; Joo Young‐Hoon guestEditor.
• Is Part Of: International journal of adaptive control and signal processing. Volume 33:Number 12(2019)
• Page Start: 1788
• Page End: 1802
• Publication Date: 2019-06-25
• Subjects: adaptive control -- adaptive learning -- connected vehicle formations -- relaxed excitation conditions -- sensor and actuator attacks -- uniform boundedness; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3032
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: In this paper, we develop an adaptive control algorithm for addressing security for a class of networked vehicles that comprise a formation of n ^ 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.