测量数据丢失的一类非线性系统迭代学习控制

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已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.

Iterative learning control for a class of nonlinear systems with measurement dropouts

This paper analyzes the stability of the iterative learning control (ILC) applied to a class of nonlinear discretetime 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.