Model-free adaptive iterative learning containment control for unknown heterogeneous nonlinear MASs with disturbances
A novel model-free adaptive iterative learning control scheme is proposed for the containment control problem of unknown heterogeneous nonlinear multi-agent systems (MASs) with bounded measurable and unmeasurable disturbances. In details, the proposed scheme includes following parts. First, the agen...
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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 515; S. 121 - 132 |
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| Sprache: | Englisch |
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01.01.2023
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | A novel model-free adaptive iterative learning control scheme is proposed for the containment control problem of unknown heterogeneous nonlinear multi-agent systems (MASs) with bounded measurable and unmeasurable disturbances. In details, the proposed scheme includes following parts. First, the agent dynamics are transformed into a partial form dynamic linearization data model along the iteration axis by using the novel concept of the pseudo gradient. Second, the distributed containment control scheme is designed for MASs under fixed topology based on the obtained data model at each working point. Then, the scheme is extended to the case of iteration-switching topologies. Finally, the convergences have been proved by rigorous mathematical analysis. The whole containment control process is characterized with data-driven nature by only using the input and output data. Simulation results illustrate the effectiveness of the schemes. |
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| AbstractList | A novel model-free adaptive iterative learning control scheme is proposed for the containment control problem of unknown heterogeneous nonlinear multi-agent systems (MASs) with bounded measurable and unmeasurable disturbances. In details, the proposed scheme includes following parts. First, the agent dynamics are transformed into a partial form dynamic linearization data model along the iteration axis by using the novel concept of the pseudo gradient. Second, the distributed containment control scheme is designed for MASs under fixed topology based on the obtained data model at each working point. Then, the scheme is extended to the case of iteration-switching topologies. Finally, the convergences have been proved by rigorous mathematical analysis. The whole containment control process is characterized with data-driven nature by only using the input and output data. Simulation results illustrate the effectiveness of the schemes. |
| Author | Liu, Tong Hou, Zhongsheng |
| Author_xml | – sequence: 1 givenname: Tong surname: Liu fullname: Liu, Tong – sequence: 2 givenname: Zhongsheng surname: Hou fullname: Hou, Zhongsheng email: zshou@qdu.edu.cn |
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| Keywords | Model-free adaptive control (MFAC) Containment control Iterative learning control (ILC) Disturbances Multi-agent systems (MASs) |
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