IDENTIFICATION OF THE DETERMINISTIC PART OF MIMO STATE-SPACE MODELS GIVEN IN INNOVATIONS FORM FROM INPUT-OUTPUT DATA

被引:588
|
作者
VERHAEGEN, M
机构
[1] Delft University of Technology, Department of Electrical Engineering, Network Theory Section, NL-2600 GA Delft
关键词
STATE SPACE; SYSTEM IDENTIFICATION; LINEAR SYSTEMS; LINEAR ALGEBRA;
D O I
10.1016/0005-1098(94)90229-1
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper we describe two algorithms to identify a linear, time-invariant, finite dimensional state space model from input-output data. The system to be identified is assumed to be excited by a measurable input and an unknown process noise and the measurements are disturbed by unknown measurement noise. Both noise sequences are discrete zero-mean white noise. The first algorithm gives consistent estimates only for the case where the input also is zero-mean white noise, while the same result is obtained with the second algorithm without this constraint. For the special case where the input signal is discrete zero-mean white noise, it is explicitly shown that this second algorithm is a special case of the recently developed Multivariable Output-Error State Space (MOESP) class of algorithms based on instrumental variables. The usefulness of the presented schemes is highlighted in a realistic simulation study.
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页码:61 / 74
页数:14
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