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 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 25, 2019
- Source ID
- 10.1002/acs.3032
Entities
People
- Aris Kanellopoulos
- Kyriakos G Vamvoudakis
- Wassim M. Haddad
- Xu Jin
- Zhong‐ping Jiang
Organizations
- Air Force Office of Scientific Research
- Georgia Tech
- NATO
- National Science Foundation
- New York University
- Office of Naval Research