ITSC 2025 Paper Abstract

Paper FR-LM-T31.1

Dong, Yuchen (Northeastern University), Gao, Weinan (Northeastern University), Li, Zhongmei (East China University of Science and Technology)

Resilient Optimal Output Regulation under Denial-Of-Service Attacks with an Application to Autonomous Vehicles

Scheduled for presentation during the Regular Session "S31a-AI-Driven Motion Prediction and Safe Control for Autonomous Systems" (FR-LM-T31), Friday, November 21, 2025, 10:30−10:50, Southport 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Cybersecurity in Autonomous and Connected Vehicle Systems, Real-time Motion Planning and Control for Autonomous Vehicles in ITS Networks

Abstract

In this paper, a novel resilient optimal control framework is proposed for linear systems under denial-of-service attacks. By integrating adaptive dynamic programming, the gradient descent method, and the output regulation theory, the resilient optimal controller can be learned directly from the real-time state and input data of the attacked system. In addition, a sufficient condition for the stability and resilience of the closed-loop system under denial-of-service attacks is provided. Finally, the effectiveness of the proposed resilient control algorithm is verified by simulation of autonomous vehicles.