Zero-Sum-Game-Based Fixed-Time Event-Triggered Optimal Consensus Control of Multi-Agent Systems Under FDI Attacks DOI Creative Commons
Jing Yang, Ruihong Li, Qintao Gan

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(3), P. 543 - 543

Published: Feb. 6, 2025

This paper concentrates on the fixed-time optimal consensus issue of multi-agent systems (MASs) under false data injection (FDI) attacks. To mitigate FDI attacks sensors and actuators that may cause to deviate from reference trajectory, a zero-sum game framework is established, where secure control protocol aims at better system performance, yet attacker plays contrary role. By solving Hamilton–Jacobi–Isaacs (HJI) equation related game, an tracking controller based event-triggered mechanism (ETM) obtained decrease consumption resources while can be guaranteed. Moreover, critic-only online reinforcement learning (RL) algorithm proposed approximate policy, in which critic neural networks are constructed by experience replay-based approach. The unmanned aerial vehicle (UAV) adopted verify feasibility presented

Language: Английский

Zero-Sum-Game-Based Fixed-Time Event-Triggered Optimal Consensus Control of Multi-Agent Systems Under FDI Attacks DOI Creative Commons
Jing Yang, Ruihong Li, Qintao Gan

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(3), P. 543 - 543

Published: Feb. 6, 2025

This paper concentrates on the fixed-time optimal consensus issue of multi-agent systems (MASs) under false data injection (FDI) attacks. To mitigate FDI attacks sensors and actuators that may cause to deviate from reference trajectory, a zero-sum game framework is established, where secure control protocol aims at better system performance, yet attacker plays contrary role. By solving Hamilton–Jacobi–Isaacs (HJI) equation related game, an tracking controller based event-triggered mechanism (ETM) obtained decrease consumption resources while can be guaranteed. Moreover, critic-only online reinforcement learning (RL) algorithm proposed approximate policy, in which critic neural networks are constructed by experience replay-based approach. The unmanned aerial vehicle (UAV) adopted verify feasibility presented

Language: Английский

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