This article addresses the batch-based learning consensus for linear and nonlinear multiagent systems (MASs) with faded neighborhood information. The motivation comes from the observation that agents exchange information via wireless networks, which inevitably introduces random fading effect and channel additive noise to the transmitted signals. It is therefore of great significance to investigate how to ensure the precise consensus tracking to a given reference leader using heavily contaminated information. To this end, a novel distributed learning consensus scheme is proposed, which consists of a classic distributed control structure, a preliminary correction mechanism, and a separated design of learning gain and regulation matrix. The influence of biased and unbiased randomness is discussed in detail according to the convergence rate and consensus performance. The iterationwise asymptotic consensus tracking is strictly established for linear MAS first to demonstrate the inherent principles for the effectiveness of the proposed scheme. Then, the results are extended to nonlinear systems with nonidentical initialization condition and diverse gain design. The obtained results show that the distributed learning consensus scheme can achieve high-precision tracking performance for an MAS under unreliable communications. The theoretical results are verified by two illustrative simulations.

中文翻译：

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具有褪色邻域信息的多智能体系统的基于批量的学习共识


本文讨论了具有褪色邻域信息的线性和非线性多智能体系统 (MAS) 的基于批量的学习共识。其动机来自于观察到代理通过无线网络交换信息，这不可避免地会给传输信号带来随机衰落效应和信道附加噪声。因此，研究如何使用严重污染的信息确保对给定参考领导者的精确共识跟踪具有重要意义。为此，提出了一种新颖的分布式学习共识方案，该方案由经典的分布式控制结构、初步校正机制以及学习增益和调节矩阵的分离设计组成。根据收敛速度和共识性能详细讨论了有偏随机性和无偏随机性的影响。首先为线性 MAS 严格建立迭代渐近共识跟踪，以证明所提出方案有效性的内在原理。然后，将结果扩展到具有不同初始化条件和不同增益设计的非线性系统。所得结果表明，分布式学习共识方案可以在不可靠通信下实现MAS的高精度跟踪性能。理论结果通过两个说明性模拟得到验证。