最差情况H_∞辨识的时域设计方法

A TIME DOMAIN APPROACH TO THE WORST CASESYSTEM IDENTIFICATION IN H∞

针对最差情况Ｈ∞辨识中最常见的一类模型集，给出了一种基于时域数据的两步辨识算法．第一步通过信息一致性原理把辨识问题转化为受限凸规划问题，第二步利用一个多项式逼近定理在第一步结果基础上，对不确定集中的系统进行逼近，得到辨识出的名义模型．最后分析了辨识算法的局部误差、全局误差和算法的收敛性．

This paper presents a twostep algorithm for the worst case H∞ identification of a class of wellknown model set with time domain experimental data.Using the information consistency principle,the first step of the algorithm transforms the identification problem into a constrained convex programming,the result of which is then used,in the second step,to approximate systems in the uncertainty set to obtain the identified nominal model based on a polynomial approximation theorem.Discussions on the local and global identification errors and the convergence of the algorithm are also carried out respectively.