Iterative learning control of nonlinear non-minimum phase systems and its application to system and model inversion
Iterative learning control of nonlinear non-minimum phase systems and its application to system and model inversion
复制标题
非线性非最小相位系统的迭代学习控制及其在系统和模型反演中的应用
DOI：
10.1109/cdc.2001.980908
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
M. Norrlof
中科院分区：
文献类型：
--
作者：
O. Markusson;H. Hjalmarsson;M. Norrlof
提出了一种基于模型的迭代学习控制（ILC）框架下的参考跟踪方法。该方法可以应用于非线性，可能非最小相位，系统。其思想是在ILC更新中使用线性化模型的逆。在非最小相位的情况下，ILC的批处理特性通过非因果滤波进行了研究。除了参考跟踪，这种方法是有用的系统和模型反演的问题，出现在许多学科的非线性系统和模型，如最大似然识别和输入设计识别控制。数值算例说明了该方法。
We present a model based method for reference tracking in the iterative learning control (ILC) framework. The method can be applied to nonlinear, possibly non-minimum phase, systems. The idea is to use the inverse of a linearized model in the ILC update. In the non-minimum phase case, the batch property of ILC is explored by means of non-causal filtering. Apart from reference tracking, this method is useful for system and model inversion-a problem that arises in many disciplines where nonlinear systems and models are involved, e.g. maximum likelihood identification and input design for identification for control. The method is illustrated on a numerical example.