In this article, the adaptive output consensus problem of high-order nonlinear heterogeneous agents is addressed using only delayed, sampled neighbor output measurements. A class of auxiliary variables is introduced which are nn -times differentiable functions and include the agent’s output along with delayed, sampled output neighbor measurements. It is proven that if these variables are bounded and regulated to zero then asymptotic consensus among all agent outputs is ensured. In view of this property, an adaptive distributed backstepping design procedure is presented that guarantees boundedness and regulation of the proposed variables. This design procedure ensures not only the desired asymptotic output consensus but also the uniform boundedness of all closed-loop variables. The main feature of our approach is that, in the proposed control law for each agent, the entire state vector of the neighbors is not needed and only delayed sampled measurements of the neighbors’ outputs are utilized. The simulation results are also presented that verify our theoretical analysis.

中文翻译：

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具有延迟和采样邻居测量的非线性异构代理的分布式自适应一致性


在本文中，仅使用延迟的采样邻居输出测量来解决高阶非线性异构代理的自适应输出一致性问题。引入了一类辅助变量，它们是 nn 次可微函数，包括代理的输出以及延迟的采样输出邻居测量值。事实证明，如果这些变量有界并调节为零，则可以确保所有代理输出之间的渐近共识。鉴于这一特性，提出了一种自适应分布式反步设计程序，保证了所提出的变量的有界性和调节性。这种设计过程不仅确保了所需的渐近输出一致性，而且确保了所有闭环变量的一致有界性。我们的方法的主要特点是，在每个智能体所提出的控制律中，不需要邻居的整个状态向量，并且仅利用邻居输出的延迟采样测量。仿真结果也验证了我们的理论分析。