Complete Sets of Invariants and Canonical Input-Output Forms for Multivariable Systems Structural and Parametric Identification

1979 ◽  
Vol 12 (8) ◽  
pp. 429-436 ◽  
Author(s):  
R.P. Guidorzi
Keyword(s):  
Parametric Identification ◽  
Multivariable Systems ◽  
Input Output ◽  
Complete Sets ◽  
Structural And Parametric Identification

scholarly journals Stability of time-delay systems via Lyapunov functions

2002 ◽  
Vol 8 (3) ◽  
pp. 197-205 ◽  
Author(s):  
Carlos F. Alastruey ◽  
Manuel de la Sen
Keyword(s):  
Lyapunov Function ◽  
Lyapunov Functions ◽  
A Priori ◽  
Original System ◽  
Delay Systems ◽  
Multivariable Systems ◽  
Time Delay Systems ◽  
Input Output ◽  
Practical Applications ◽  
System A

In this paper, a Lyapunov function candidate is introduced for multivariable systems with inner delays, without assuminga prioristability for the nondelayed subsystem. By using this Lyapunov function, a controller is deduced. Such a controller utilizes an input–output description of the original system, a circumstance that facilitates practical applications of the proposed approach.

Closed-loop parametric identification of DC-DC converter

2018 ◽  
Vol 232 (10) ◽  
pp. 1429-1438 ◽  
Author(s):  
Subhransu Padhee ◽  
Umesh Chandra Pati ◽  
Kamalakanta Mahapatra
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Model Simulation ◽  
Moving Average ◽  
Parametric Identification ◽  
Buck Converter ◽  
Input Output ◽  
Closed Loop System ◽  
Auto Regressive ◽  
Error Method

This study provides a step-by-step analysis of closed-loop parametric system identification for DC-DC buck converter. In closed-loop parametric identification, input–output experimental data are used to estimate the transfer function coefficients of DC-DC buck converter. For system identification purpose, a high-frequency perturbation signal is injected in to the closed-loop system which acts as an input signal for identification experiment. Different input–output models such as Auto-Regressive eXogenous, Auto-Regressive Moving Average with eXogenous, output error, and Box–Jenkins are used to model the converter structure and prediction error method is used to estimate the parameters. Model validation schemes are used to validate the estimated model. Simulation and experimental analysis have been provided to validate the results obtained.

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