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This paper solves the adaptive consensus problem for first-order linearly parameterized agents with completely nonidentical unknown high-frequency gain signs under directed graphs. A new class of Nussbaum-type function-based algorithms are… Click to show full abstract
This paper solves the adaptive consensus problem for first-order linearly parameterized agents with completely nonidentical unknown high-frequency gain signs under directed graphs. A new class of Nussbaum-type function-based algorithms are proposed to handle the unknown high-frequency gain signs adaptively and cooperatively. It is shown that if the underlying topology is a fixed graph with strongly connected or switching topologies having a jointly strongly connected basis, the first-order linearly parameterized agents with nonidentical unknown high-frequency gain signs can achieve asymptotic consensus. Finally, the effectiveness of proposed algorithms are verified by one simulation example.
Journal Title: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Year Published: 2020
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