Event-Triggered MFAILC Bipartite Formation Control for Multi-Agent Systems Under DoS Attacks
For multi-input multi-output (MIMO) nonlinear discrete-time bipartite formation multiagent systems (BFMASs) performing trajectory tracking tasks with unknown dynamics, a dynamic event-triggered model-free adaptive iterative learning control (DET-MFAILC) algorithm is proposed to address periodic deni...
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MDPI AG
2025-02-01
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| author | Han Li Lixia Fu Wenchao Wu |
| author_facet | Han Li Lixia Fu Wenchao Wu |
| author_sort | Han Li |
| collection | DOAJ |
| description | For multi-input multi-output (MIMO) nonlinear discrete-time bipartite formation multiagent systems (BFMASs) performing trajectory tracking tasks with unknown dynamics, a dynamic event-triggered model-free adaptive iterative learning control (DET-MFAILC) algorithm is proposed to address periodic denial-of-service (DoS) attacks. First, using the pseudo-partial derivative, a compact format dynamic linearization (CFDL) method is employed to construct an equivalent CFDL data model for the MIMO multi-agent system. A DoS attack model and its corresponding compensation algorithm are developed, while a dynamic event-triggered condition is designed considering both the consensus error and the tracking error. Subsequently, the proposed DoS attack compensation algorithm and the dynamic event-triggered mechanism are integrated with the model-free adaptive iterative learning control algorithm to design a controller, which is further extended from fixed-topology systems to time-varying topology systems. The convergence of the control system is rigorously proven. Finally, simulation experiments are conducted on bipartite formation multi-agent systems (BFMASs) under fixed and time-varying communication topologies. The results demonstrate that the proposed algorithm effectively mitigates the impact of DoS attacks, reduces controller updates, conserves network resources, and ensures that both the tracking error and consensus error converge to an ideal range close to zero within a finite number of iterations while maintaining a good formation shape. |
| format | Article |
| id | doaj-art-c7790bf64f704473b0e4b7a322a0dd26 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-c7790bf64f704473b0e4b7a322a0dd262025-08-20T02:44:55ZengMDPI AGApplied Sciences2076-34172025-02-01154192110.3390/app15041921Event-Triggered MFAILC Bipartite Formation Control for Multi-Agent Systems Under DoS AttacksHan Li0Lixia Fu1Wenchao Wu2Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFor multi-input multi-output (MIMO) nonlinear discrete-time bipartite formation multiagent systems (BFMASs) performing trajectory tracking tasks with unknown dynamics, a dynamic event-triggered model-free adaptive iterative learning control (DET-MFAILC) algorithm is proposed to address periodic denial-of-service (DoS) attacks. First, using the pseudo-partial derivative, a compact format dynamic linearization (CFDL) method is employed to construct an equivalent CFDL data model for the MIMO multi-agent system. A DoS attack model and its corresponding compensation algorithm are developed, while a dynamic event-triggered condition is designed considering both the consensus error and the tracking error. Subsequently, the proposed DoS attack compensation algorithm and the dynamic event-triggered mechanism are integrated with the model-free adaptive iterative learning control algorithm to design a controller, which is further extended from fixed-topology systems to time-varying topology systems. The convergence of the control system is rigorously proven. Finally, simulation experiments are conducted on bipartite formation multi-agent systems (BFMASs) under fixed and time-varying communication topologies. The results demonstrate that the proposed algorithm effectively mitigates the impact of DoS attacks, reduces controller updates, conserves network resources, and ensures that both the tracking error and consensus error converge to an ideal range close to zero within a finite number of iterations while maintaining a good formation shape.https://www.mdpi.com/2076-3417/15/4/1921model-free adaptive iterative learning controldynamic event-triggered mechanismDoS attack compensationbipartite formationmulti-agent systems |
| spellingShingle | Han Li Lixia Fu Wenchao Wu Event-Triggered MFAILC Bipartite Formation Control for Multi-Agent Systems Under DoS Attacks Applied Sciences model-free adaptive iterative learning control dynamic event-triggered mechanism DoS attack compensation bipartite formation multi-agent systems |
| title | Event-Triggered MFAILC Bipartite Formation Control for Multi-Agent Systems Under DoS Attacks |
| title_full | Event-Triggered MFAILC Bipartite Formation Control for Multi-Agent Systems Under DoS Attacks |
| title_fullStr | Event-Triggered MFAILC Bipartite Formation Control for Multi-Agent Systems Under DoS Attacks |
| title_full_unstemmed | Event-Triggered MFAILC Bipartite Formation Control for Multi-Agent Systems Under DoS Attacks |
| title_short | Event-Triggered MFAILC Bipartite Formation Control for Multi-Agent Systems Under DoS Attacks |
| title_sort | event triggered mfailc bipartite formation control for multi agent systems under dos attacks |
| topic | model-free adaptive iterative learning control dynamic event-triggered mechanism DoS attack compensation bipartite formation multi-agent systems |
| url | https://www.mdpi.com/2076-3417/15/4/1921 |
| work_keys_str_mv | AT hanli eventtriggeredmfailcbipartiteformationcontrolformultiagentsystemsunderdosattacks AT lixiafu eventtriggeredmfailcbipartiteformationcontrolformultiagentsystemsunderdosattacks AT wenchaowu eventtriggeredmfailcbipartiteformationcontrolformultiagentsystemsunderdosattacks |