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)
JournalInternational Journal of Adaptive Control and Signal Processing
DOIs
StatePublished - Jan 1 2019

Fingerprint

Actuators
Sensors
Vehicle to vehicle communications
Kinematics
Controllers

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

Cite this

An adaptive learning and control architecture for mitigating sensor and actuator attacks in connected autonomous vehicle platoons. / Jin, Xu; Haddad, Wassim M.; Jiang, Zhong-Ping; Kanellopoulos, Aris; Vamvoudakis, Kyriakos G.

In: International Journal of Adaptive Control and Signal Processing, 01.01.2019.

Research output: Contribution to journalArticle

@article{566c9c197fbb4d8e8a255fe5bc105533,
title = "An adaptive learning and control architecture for mitigating sensor and actuator attacks in connected autonomous vehicle platoons",
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.",
keywords = "adaptive control, adaptive learning, connected vehicle formations, relaxed excitation conditions, sensor and actuator attacks, uniform boundedness",
author = "Xu Jin and Haddad, {Wassim M.} and Zhong-Ping Jiang and Aris Kanellopoulos and Vamvoudakis, {Kyriakos G.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1002/acs.3032",
language = "English (US)",
journal = "International Journal of Adaptive Control and Signal Processing",
issn = "0890-6327",
publisher = "John Wiley and Sons Ltd",

}

TY - JOUR

T1 - An adaptive learning and control architecture for mitigating sensor and actuator attacks in connected autonomous vehicle platoons

AU - Jin, Xu

AU - Haddad, Wassim M.

AU - Jiang, Zhong-Ping

AU - Kanellopoulos, Aris

AU - Vamvoudakis, Kyriakos G.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - adaptive control

KW - adaptive learning

KW - connected vehicle formations

KW - relaxed excitation conditions

KW - sensor and actuator attacks

KW - uniform boundedness

UR - http://www.scopus.com/inward/record.url?scp=85068058467&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068058467&partnerID=8YFLogxK

U2 - 10.1002/acs.3032

DO - 10.1002/acs.3032

M3 - Article

AN - SCOPUS:85068058467

JO - International Journal of Adaptive Control and Signal Processing

JF - International Journal of Adaptive Control and Signal Processing

SN - 0890-6327

ER -