This paper is aimed at cooperative iterative learning tasks for leaderless networks with nonlinear agents, and the effects arising from switching topologies, locally Lipschitz nonlinearities, initial state shifts, and external disturbances of agents are addressed. By proposing a learning-based distributed algorithm, desired relative formation behaviors of leaderless networks can be realized, where all agents’ trajectories can be ensured to be uniformly bounded. A Lyapunov-like analysis approach is introduced to ensure learning convergence with an exponential rate by leveraging the properties of products of stochastic matrices, which can also be employed to develop input-to-state consensus results of discrete parameterized systems.

本文旨在针对具有非线性主体的无领导者网络进行协作式迭代学习任务，并解决了因切换拓扑，局部Lipschitz非线性，初始状态转移以及主体的外部干扰而引起的影响。通过提出一种基于学习的分布式算法，可以实现无领导者网络的所需相对形成行为，其中可以确保所有主体的轨迹被均匀地界定。引入了一种类似Lyapunov的分析方法，以通过利用随机矩阵乘积的性质来确保以指数速率进行学习​​收敛，该方法还可以用于开发离散参数化系统的输入到状态共识结果。