Zero-Sum-Game-Based Fixed-Time Event-Triggered Optimal Consensus Control of Multi-Agent Systems Under FDI Attacks
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 on sensors and actuators that may cause systems to deviate from the reference trajectory, a zero-sum game framework is established, where...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/3/543 |
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| Summary: | 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 on sensors and actuators that may cause systems to deviate from the reference trajectory, a zero-sum game framework is established, where the secure control protocol aims at the better system performance, yet the attacker plays a contrary role. By solving the Hamilton–Jacobi–Isaacs (HJI) equation related to the zero-sum game, an optimal secure tracking controller based on the event-triggered mechanism (ETM) is obtained to decrease the consumption of system resources while the fixed-time consensus can be guaranteed. Moreover, a critic-only online reinforcement learning (RL) algorithm is proposed to approximate the optimal policy, in which the critic neural networks are constructed by the experience replay-based approach. The unmanned aerial vehicle (UAV) systems are adopted to verify the feasibility of the presented approach. |
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| ISSN: | 2227-7390 |