基于2D模型的网络系统迭代学习控制设计方法

Network-based iterative learning control design based on 2D model

迭代学习控制(iterative learning control,ILC)方法应用于网络控制系统时,由于数据需要在控制器和远程对象间传输经常产生数据丢失现象。给出了一种存在数据丢失时网络系统的随机迭代学习控制设计方法,首先将数据丢失现象描述为随机伯努利序列,在此基础上将迭代学习的控制器设计转化为随机2D-Roesser系统的稳定问题。定义了随机意义下2D系统的均方渐进稳定,基于线性矩阵不等式(linear matrix inequality,LMI)给出一个判别稳定性的条件,该条件同时可实现迭代学习控制器的设计。仿真示例验证了设计方法的有效性。

When the iterative learning control（ILC）is applied to networked control systems, packet drop- outs often occur due to the data transfer from the remote plant to the ILC controller. A stochastic ILC design approach for networked control systems with data dropouts is given. Missing data is firstly modeled by stochas- tic variables satisfying the Bernoulli random binary distribution. Then, the design of ILC is transformed into the stability of a 2D stochastic system described by the Roesser model. The mean-square asymptotic stability is de- fined for such 2D stochastic systems. A sufficient condition for stability is established by means of linear matrix inequality（LMI）technique, and formulas can be given for the controller design simultaneously. The effective- ness of the proposed method is illustrated by a numerical example.