| Autor: |
Sami El-Ferik, Muhammad Maaruf, Fouad M. Al-Sunni, Abdulwahid Abdulaziz Saif, Mujahed Mohammad Al Dhaifallah |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 11, Pp 77656-77668 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3296741 |
| Popis: |
In this research, we investigate the reinforcement learning-based control strategy for second-order continuous-time multi-agent systems (MASs) subjected to actuator cyberattacks during affine formation maneuvers. In this case, a long-term performance index is created to track the MASs tracking faults using a leader-follower structure. In order to approximate the ideal solution, which is challenging to find for systems vulnerable to cyberattacks during time-varying maneuvers, a critical neural network is used. The distributed control protocol is obtained, and the long-term performance index is minimized, using an actor neural network strengthened with critic signals. The actor-critic neural networks calculate unknown dynamics and the severity of attacks on the MAS actuators. The Nussbaum functions are applied to address this issue since attacks can result in a loss of control direction. The stability of the closed-loop system has been emphasized with the use of a Lyapunov candidate function. The performance of the suggested strategy is then supported by a numerical simulation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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