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中文摘要：
迭代学习控制方法应用于网络控制系统时，由于通信网络的约束导致数据包丢失现象经常发生．针对存在输出测量数据丢失的一类非线性系统，研究P型迭代学习控制算法的收敛性问题．将数据丢失描述为一个概率己知的随机伯努利过程，在此基础上给出P型迭代学习控制算法的收敛条件，理论上证明了算法的收敛性，并通过仿真验证理论结果．研究表明，当非线性系统存在输出测量数据丢失时，迭代学习控制算法仍然可以保证跟踪误差的收敛性．
英文摘要：
This paper analyzes the stability of the iterative learning control （ILC） applied to a class of nonlinear discrete- time systems with output measurement data dropouts. It is assumed that an ILC scheme is implemented via a networked control loop for the nonlinear system and that the packet dropout occurs due to limitations in network communication. The data dropout is described as a stochastic Bernoulli process with a given probability; on this basis we derive the convergence condition for the P-type ILC algorithm. The theoretical analysis is supported by the simulation of a numerical example; the convergence of ILC can be guaranteed when some output measurements are missing.
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