Consistency of noise covariance estimation in joint input–output closed-loop subspace identification with application in LQG benchmarking

2009 ◽  
Vol 19 (10) ◽  
pp. 1649-1657 ◽  
Author(s):  
N. Danesh Pour ◽  
B. Huang ◽  
S.L. Shah
Keyword(s):  
Closed Loop ◽  
Covariance Estimation ◽  
Subspace Identification ◽  
Input Output ◽  
Noise Covariance

Improved closed-loop subspace identification with prior information

2018 ◽  
Vol 49 (9) ◽  
pp. 1821-1835 ◽  
Author(s):  
Youqing Wang ◽  
Ling Zhang ◽  
Yali Zhao
Keyword(s):  
Prior Information ◽  
Closed Loop ◽  
Subspace Identification

Multi-Stage System Identification of a Gas Turbine

2017 ◽  
Author(s):  
Amit Pandey ◽  
Maurício de Oliveira ◽  
Chad M. Holcomb
Keyword(s):  
Gas Turbine ◽  
Closed Loop ◽  
Open Loop ◽  
Loop System ◽  
Closed Loop Control ◽  
Input Output ◽  
Open Loop System ◽  
Loop Control ◽  
Multi Stage ◽  
Identification Techniques

Several techniques have recently been proposed to identify open-loop system models from input-output data obtained while the plant is operating under closed-loop control. So called multi-stage identification techniques are particularly useful in industrial applications where obtaining input-output information in the absence of closed-loop control is often difficult. These open-loop system models can then be employed in the design of more sophisticated closed-loop controllers. This paper introduces a methodology to identify linear open-loop models of gas turbine engines using a multi-stage identification procedure. The procedure utilizes closed-loop data to identify a closed-loop sensitivity function in the first stage and extracts the open-loop plant model in the second stage. The closed-loop data can be obtained by any sufficiently informative experiment from a plant in operation or simulation. We present simulation results here. This is the logical process to follow since using experimentation is often prohibitively expensive and unpractical. Both identification stages use standard open-loop identification techniques. We then propose a series of techniques to validate the accuracy of the identified models against first principles simulations in both the time and frequency domains. Finally, the potential to use these models for control design is discussed.

Two-Degree-of-Freedom Helicopter Closed-Loop Identification through a Cascade Controller

2011 ◽  
Vol 403-408 ◽  
pp. 4649-4658 ◽  
Author(s):  
Pouya Ghalei ◽  
Alireza Fatehi ◽  
Mohamadreza Arvan
Keyword(s):  
Closed Loop ◽  
Flight Simulation ◽  
Degree Of Freedom ◽  
Input Output ◽  
Closed Loop Identification ◽  
Practical Algorithm ◽  
Nonlinear Autoregressive Model ◽  
And Control ◽  
Nonlinear Autoregressive ◽  
Two Degree Of Freedom

Input-Output data modeling using multi layer perceptron networks (MLP) for a laboratory helicopter is presented in this paper. The behavior of the two degree-of-freedom platform exemplifies a high order unstable, nonlinear system with significant cross-coupling between pitch and yaw directional motions. This paper develops a practical algorithm for identifying nonlinear autoregressive model with exogenous inputs (NARX) and nonlinear output error model (NOE) through closed loop identification. In order to collect input-output identifier pairs, a cascade state feedback (CSF) controller is introduced to stabilize the helicopter and after that the procedure of system identification is proposed. The estimated models can be utilized for nonlinear flight simulation and control and fault detection studies.

scholarly journals Closed-loop subspace identification taking initial state into account

2018 ◽  
Vol 51 (15) ◽  
pp. 604-609
Author(s):  
Hideyuki Tanaka ◽  
Kenji Ikeda
Keyword(s):  
Closed Loop ◽  
Subspace Identification ◽  
Initial State

P-D Feedback for Decoupling and Pole Assignment of Singular Systems

1998 ◽  
Vol 120 (3) ◽  
pp. 378-388 ◽  
Author(s):  
F. N. Koumboulis ◽  
B. G. Mertzios
Keyword(s):  
Closed Loop ◽  
Singular Systems ◽  
Sufficient Conditions ◽  
Pole Assignment ◽  
Loop System ◽  
Necessary And Sufficient Conditions ◽  
Algebraic Equations ◽  
Input Output ◽  
Closed Loop System ◽  
Necessary And Sufficient

The problem of reducing a multi input-multi output system to many single input-single output systems, namely the problem of input-output decoupling, is studied for the case of singular systems i.e., for systems described by dynamic and algebraic equations. The problem of input-output decoupling with simultaneous arbitrary pole assignment, via proportional plus derivative (P-D) state feedback, is extensively solved. The general explicit expression of all P-D controllers solving the decoupling problem is determined. The general form of the diagonal elements of the decoupled closed-loop system is proven to be in a form having a fixed numerator polynomial and an arbitrary denominator polynomial. The necessary and sufficient conditions for the solvability of the problem of decoupling with simultaneous asymptotic stabilizability or arbitrary pole assignment are established. Furthermore, the necessary and sufficient conditions for decoupling with simultaneous impulse elimination, as well as the necessary and sufficient conditions for decoupling with arbitrary assignment of the finite and infinite poles of the closed-loop system, are established.

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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