基于ARMAX模型的子空间辨识算法  

A New Subspace Identification Method Based on ARMAX Model

作　　者：李天一[1] 郑建荣[1] 

机构地区：[1]华东理工大学机械与动力工程学院,上海200237

出　　处：《计算机仿真》2015年第1期310-313,437,共5页Computer Simulation

摘　　要：针对于子空间辨识算法辨识闭环系统时,由于输入信号与不可测噪声是相关的,往往会得到有偏估计的问题。提出一种采用自回归滑动平均模型(ARMAX)的闭环子空间辨识方法,通过扩展最小二乘方法(ELS)估计ARMAX模型中的马尔科夫(Markov)参数,使用预测的子空间辨识方法(PBSID)获取系统参数矩阵,避免了采用高阶自回归模型(ARX)所导致的过大的估计方差等问题。算法实例验证结果表明,改进方法能够获得较好的闭环系统一致性估计,辨识精度较高,有非常良好的应用前景。Most of the existing subspace identification methods get biased estimation for closed-loop conditions, because of the input signal and the unmeasured noise are related. This paper presents a closed-loop subspace identi- fication method based on Auto Regressive Moving Average model; estimates Markov parameters in the ARMAX model by the extended least square method; recovers system parameter matrix by using the predictor-based subspace identification method. It avoids the too large estimation variance caused by high order Auto Regressive model. A closed-loop simulation example is given to demonstrate that the identification algorithm can obtain consistent estimates and is effective.

关 键 词：子空间辨识 闭环系统 自回归滑动平均模型 扩展最小二乘法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]