Identification of the deterministic part of MIMO state space models given in innovations form from input-output data

Automatica

Volumn 30, Issue 1, 1994, Pages 61-74

  Verhaegen, Michel ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

linear algebra; linear systems; State space; system identification

Indexed keywords

ALGORITHMS; ERROR ANALYSIS; ITERATIVE METHODS; LINEAR ALGEBRA; MATHEMATICAL MODELS; MULTIVARIABLE SYSTEMS; OPTIMIZATION; STATE SPACE METHODS; WHITE NOISE;

LINEAR SYSTEMS; MULTIPLE INPUT MULTIPLE OUTPUT (MIMO) STATE SPACE MODELS; MULTIVARIABLE OUTPUT ERROR STATE SPACE (MOESP) ALGORITHMS; UNKNOWN MEASUREMENT NOISE; UNKNOWN PROCESS NOISE;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 0028330582     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/0005-1098(94)90229-1     Document Type: Article

References (16)

1. Markovian representation of stochastic processes by canonical variables
2. NASA's advanced control law program for the F-8 digital fly-by-wire aircraft
3. Canonical variate analysis in identification filtering and adaptive control
4. An exact subspace algorithm for the identification of combined deterministic-stochastic systems
5. Error and Perturbation Bounds for Subspaces Associated with Certain Eigenvalue Problems
6. Identification of MIMO output-error state space models
7. Subspace model identification
8. Subspace model identification Part 1. The output-error state-space model identification class of algorithms
9. Subspace model identification Part 2. Analysis of the elementary output-error state-space model identification algorithm
10. Subspace model identification Part 3. Analysis of the ordinary output-error state-space model identification algorithm
11. Identification of the deterministic and stochastic part of MIMO state space models under the presence of process and measurement noise