数据丢失下多智能体系统迭代学习跟踪控制

考虑数据丢失下非线性多智能体系统的一致性跟踪问题。假设多智能体系统使用固定网络通信拓扑结构，由于通信网络自身限制导致多智能体系统中存在数据丢失现象。将数据丢失现象描述为取值0/1的随机伯努利序列，设计分布式一致性跟踪误差，提出该系统在数据丢失下的P型迭代学习控制算法。采用压缩映射的方法给出收敛性条件，并在理论上分析了跟踪误差的收敛性。仿真结果表明，提出的算法可以实现该系统在有限时间区间上对期望轨迹的完全跟踪，验证了算法的有效性。

Iterative learning consensus tracking control for a class of multi-agent systems with data dropouts

It is considered the consensus tracking problem for a class of nonlinear Multi-Agent Systems（MAS） with data dropouts. It is assumed that the multi-agent systems use fixed communication topology. Moreover, due to the communication channel failure the measurements missing often occurs. The data missing phenomena is described as a random Bernoulli sequence with 0/1. Then the consensus tracking error is designed and a distributed P-type iterative learning control algorithm with data dropouts is proposed. By using the approach of contraction mapping, the convergence condition of tracking error is given and the theoretical analysis is also provided. It is shown that the proposed Iterative Learning Control（ILC） algorithm can realize the perfect?tracking of the desired trajectory over a finite time interval and verify the effectiveness of the algorithm.