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Security and Safety-Critical Learning-Based Collaborative Control for Multiagent Systems

journal contribution
posted on 2024-03-14, 04:28 authored by Bing Yan, Peng Shi, Chee Peng LimChee Peng Lim, Yuan Sun, Ramesh K Agarwal
This article presents a novel learning-based collaborative control framework to ensure communication security and formation safety of nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks, model uncertainties, and barriers in environments. The framework has a distributed and decoupled design at the cyber-layer and the physical layer. A resilient control Lyapunov function-quadratic programming (RCLF-QP)-based observer is first proposed to achieve secure reference state estimation under DoS attacks at the cyber-layer. Based on deep reinforcement learning (RL) and control barrier function (CBF), a safety-critical formation controller is designed at the physical layer to ensure safe collaborations between uncertain agents in dynamic environments. The framework is applied to autonomous vehicles for area scanning formations with barriers in environments. The comparative experimental results demonstrate that the proposed framework can effectively improve the resilience and robustness of the system.

History

Journal

IEEE Transactions on Neural Networks and Learning Systems

Volume

PP

Pagination

1-12

Location

United States

ISSN

2162-237X

eISSN

2162-2388

Language

eng

Issue

99

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

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