Adaptive Consensus of Multiagent Systems With Unknown High-Frequency Gain Signs Under Directed Graphs
针对一阶线性参数化智能体，提出基于Nussbaum型函数的算法，解决有向图下完全非相同未知高频增益符号的自适应一致性问题，实现渐近一致性。
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.