This paper studies a distributed average tracking problem for a class of networked agents subject to heterogeneous unknown nonlinearities. The object is to design distributed protocols so as to drive these dynamic agents cooperatively tracking the average of multiple unknown signals. First, an initialize-free robust algorithm is designed for each agent incorporating a local filter, a neural network (NN) compensator, and state-dependent coupling gains with its neighbors. Here the filter is crucial for seeking the average of multiple references signals and is necessary due to the existence of uncertainties in the agents' dynamics. Then, by using adaption schemes, the algorithm is extended to a dynamic version releasing the requirement of certain global information such as the eigenvalues of the network Laplacian and the NN approximation errors. Both algorithms are rigorously proved to guarantee asymptotical average tracking with the help of well-designed Lyapunov candidates. Finally, two illustrative examples are provided to validate the theoretical results.

中文翻译：

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具有匹配未知非线性的代理的神经网络嵌入式分布式平均跟踪

本文研究了一类受异构未知非线性影响的网络代理的分布式平均跟踪问题。目的是设计分布式协议，以驱动这些动态代理协同跟踪多个未知信号的平均值。首先，为包含本​​地滤波器、神经网络 (NN) 补偿器和与其邻居的状态相关耦合增益的每个代理设计了一种无需初始化的稳健算法。在这里，滤波器对于寻求多个参考信号的平均值至关重要，并且由于代理动态中存在不确定性，因此是必要的。然后，通过使用自适应方案，将算法扩展为动态版本，释放某些全局信息的要求，例如网络拉普拉斯算子的特征值和神经网络逼近误差。在精心设计的李雅普诺夫候选算法的帮助下，这两种算法都经过严格证明可以保证渐近平均跟踪。最后，提供了两个说明性的例子来验证理论结果。