In this work, a lifted event-triggered iterative learning control (lifted ETILC) is proposed aiming for addressing all the key issues of heterogeneous dynamics, switching topologies, limited resources, and model-dependence in the consensus of nonlinear multi-agent systems (MASs). First, we establish a linear data model for describing the I/O relationships of the heterogeneous nonlinear agents as a linear parametric form to make the non-affine structural MAS affine with respect to the control input. Both the heterogeneous dynamics and uncertainties of the agents are included in the parameters of the linear data model, which are then estimated through an iterative projection algorithm. On this basis, a lifted event-triggered learning consensus is proposed with an event-triggering condition derived through a Lyapunov function. In this work, no threshold condition but the event-triggering condition is used which plays a key role in guaranteeing both the stability and the iterative convergence of the proposed lifted ETILC. The proposed method can reduce the number of control actions significantly in batches while guaranteeing the iterative convergence of tracking error. Both rigorous analysis and simulations are provided and confirm the validity of the lifted ETILC.

在这项工作中，提出了一种提升事件触发的迭代学习控制（提升ETILC），旨在解决非线性多智能体系统（MAS）共识中异构动力学，切换拓扑，有限资源和模型相关性的所有关键问题。 ）。首先，我们建立一个线性数据模型，以线性参数模型形式描述异构非线性代理的I / O关系，以使非仿射结构MAS仿射相对于控制输入。代理的异质动力学和不确定性都包含在线性数据模型的参数中，然后通过迭代投影算法对其进行估算。在此基础上，提出了一个通过Lyapunov函数导出事件触发条件的事件触发学习共识。在这项工作中，不使用阈值条件，而是使用事件触发条件，它在保证所提出的提升ETILC的稳定性和迭代收敛性方面都起着关键作用。所提出的方法可以在保证跟踪误差迭代收敛的同时，显着减少批量控制动作的次数。提供了严格的分析和模拟，并确认了提起的ETILC的有效性。