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

Automatica ◽  
1994 ◽  
Vol 30 (1) ◽  
pp. 61-74 ◽  
Author(s):  
Michel Verhaegen
Keyword(s):  
State Space ◽  
State Space Models ◽  
Input Output ◽  
Output Data

IDENTIFICATION OF MULTISCALE STATE–SPACE MODELS FROM INPUT–OUTPUT DATA

2015 ◽  
Vol 43 (1) ◽  
Author(s):  
Marcelo N. Martins ◽  
Ronaldo Waschburger ◽  
Roberto K.H. Galvão
Keyword(s):  
State Space ◽  
State Space Models ◽  
Input Output ◽  
Output Data

scholarly journals Comparing global input-output behavior of frozen-equivalent LPV state-space models

2017 ◽  
Vol 50 (1) ◽  
pp. 9766-9771 ◽  
Author(s):  
Ziad Alkhoury ◽  
Mihály Petreczky ◽  
Guillaume Mercère
Keyword(s):  
State Space ◽  
State Space Models ◽  
Input Output ◽  
Output Behavior

scholarly journals Parameter and State Estimator for State Space Models

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ruifeng Ding ◽  
Linfan Zhuang
Keyword(s):  
State Space ◽  
State Equation ◽  
The State ◽  
Parameter Estimates ◽  
Identification Algorithm ◽  
State Variables ◽  
Input Output ◽  
State Estimator ◽  
Output Data ◽  
Canonical State

This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.

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