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A framework for perfect group consensus tracking: Iterative learning control scheme
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DOI：10.1016/j.jfranklin.2024.107091.png)
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Abstract
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The study focuses on the framework for solving the perfect group consensus tracking problem with linear multi-agent systems (MASs) under a directed topology. The novel iterative learning control (ILC) protocols are introduced for achieving group consensus tracking. The design comprises distributed iterative learning group consensus protocols and initial state learning laws for second-order MASs with two subgroups under fixed and switched topologies, respectively. Sufficient conditions for perfect group consensus tracking are provided based on the principle of compression mapping. Additionally, sufficient conditions for group consensus tracking and formation control with multiple subgroups are also derived. These algorithms enable followers to achieve perfect tracking of their respective leaders within a defined time interval [0, T] through cooperation and competition. The effectiveness of the proposed theories is validated through three simulation examples.
Keywords:
MASs
ILC
Group consensus
Perfect tracking
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