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

Xu Jin, Wassim M. Haddad, Zhong Ping Jiang, Aris Kanellopoulos, Kyriakos G. Vamvoudakis

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1788-1802
Number of pages15
JournalInternational Journal of Adaptive Control and Signal Processing
Volume33
Issue number12
DOIs
StatePublished - Dec 1 2019

Keywords

  • adaptive control
  • adaptive learning
  • connected vehicle formations
  • relaxed excitation conditions
  • sensor and actuator attacks
  • uniform boundedness

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Electrical and Electronic Engineering

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