An instrumental variable technique for open-loop and closed-loop identification of input-output LPV models

2009 ◽  
Author(s):  
Hossam Abbas ◽  
Herbert Werner
Keyword(s):  
Instrumental Variable ◽  
Closed Loop ◽  
Open Loop ◽  
Input Output ◽  
Variable Technique ◽  
Closed Loop 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.

Closed-loop identification of carotid sinus baroreflex open-loop transfer characteristics in rabbits

1997 ◽  
Vol 273 (2) ◽  
pp. H1024-H1031 ◽  
Author(s):  
T. Kawada ◽  
M. Sugimachi ◽  
T. Sato ◽  
H. Miyano ◽  
T. Shishido ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Closed Loop ◽  
Transfer Functions ◽  
Carotid Sinus ◽  
Open Loop ◽  
Parametric Models ◽  
Efferent Nerve ◽  
Transfer Characteristics ◽  
Carotid Sinus Baroreflex ◽  
Closed Loop Identification

In the circulatory system, a change in blood pressure operates through the baroreflex to alter sympathetic efferent nerve activity, which in turn affects blood pressure. Existence of this closed feedback loop makes it difficult to identify the baroreflex open-loop transfer characteristics by means of conventional frequency domain approaches. Although several investigators have demonstrated the advantages of the time domain approach using parametric models such as the autoregressive moving average model, specification of the model structure critically affects their results. Thus we investigated the applicability of a nonparametric closed-loop identification technique to the carotid sinus baroreflex system by using an exogenous perturbation according to a binary white-noise sequence. To validate the identification method, we compared the transfer functions estimated by the closed-loop identification with those estimated by open-loop identification. The transfer functions determined by the two identification methods did not differ statistically in their fitted parameters. We conclude that exogenous perturbation to the baroreflex system enables us to estimate the open-loop baroreflex transfer characteristics under closed-loop conditions.

Identification of Multi-Input Multi-Output Systems Using Combined Direct and Indirect Methods

2014 ◽  
Vol 625 ◽  
pp. 414-417
Author(s):  
Abdelraheem Faisal ◽  
Marappagounder Ramasamy ◽  
Mahadzir Shuhaimi ◽  
Mohamed Rahim
Keyword(s):  
Closed Loop ◽  
Mimo Systems ◽  
Open Loop ◽  
Identification Procedure ◽  
Indirect Methods ◽  
Representative Case ◽  
Closed Loop Identification ◽  
Decentralized Model ◽  
Direct And Indirect Methods

Successful deployment of cooperative decentralized model predicative control needs reasonably accurate subsystem interactions models. Processes in which open-loop tests are not permitted, closed-loop identification of subsystems interactions is crucial. An approach that combines the direct and indirect methods of closed-loop identification is proposed in this paper. It is shown that full dynamics of MIMO systems can be determined following a two-steps identification procedure. A representative case study is used to demonstrate the efficacy of the proposed approach.

Pseudo-random binary sequence closed-loop system identification error with integration control

2009 ◽  
Vol 223 (6) ◽  
pp. 877-884 ◽  
Author(s):  
Z Ren ◽  
G G Zhu
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Binary Sequence ◽  
Open Loop ◽  
Loop System ◽  
Loop Model ◽  
Closed Loop System ◽  
Identification Error ◽  
Closed Loop Identification ◽  
Integral Controller

This paper studies the closed-loop system identification (ID) error when a dynamic integral controller is used. Pseudo-random binary sequence (PRBS) q-Markov covariance equivalent realization (Cover) is used to identify the closed-loop model, and the open-loop model is obtained based upon the identified closed-loop model. Accurate open-loop models were obtained using PRBS q-Markov Cover system ID directly. For closed-loop system ID, accurate open-loop identified models were obtained with a proportional controller, but when a dynamic controller was used, low-frequency system ID error was found. This study suggests that extra caution is required when a dynamic integral controller is used for closed-loop system identification. The closed-loop identification framework also has significant effects on closed-loop identification error. Both first- and second-order examples are provided in this paper.

Parameter Identification of Compressor Dynamics During Closed-Loop Operation

1993 ◽  
Vol 115 (4) ◽  
pp. 694-703 ◽  
Author(s):  
J. Paduano ◽  
L. Valavani ◽  
A. H. Epstein
Keyword(s):  
Instrumental Variable ◽  
Closed Loop ◽  
Rotating Stall ◽  
Feedback Stabilization ◽  
Variable Technique ◽  
Basic Approach ◽  
Basic Technique ◽  
Inlet Guide Vanes ◽  
Quantitative Identification ◽  
Output Behavior

A low-speed axial research compressor has been fitted with movable inlet guide vanes to allow for feedback stabilization of rotating stall. A model exists whose structure captures the input-output behavior, and stabilization of rotating stall is possible using this model. Quantitative identification of the parameters in the rotating stall model requires the ability to identify MIMO dynamics, which may be unstable, during closed loop operation. The ‘instrumental variable’ technique is presented as the basic approach to this problem. The necessary extensions to the basic technique are discussed, and the resulting algorithm is applied. Experimental results are presented which verify that the methodology yields useful estimates.

scholarly journals Iterative LQG Controller Design Through Closed-Loop Identification

1996 ◽  
Vol 118 (2) ◽  
pp. 366-372 ◽  
Author(s):  
Min-Hung Hsiao ◽  
Jen-Kuang Huang ◽  
David E. Cox
Keyword(s):  
Kalman Filter ◽  
State Feedback ◽  
Closed Loop ◽  
Controller Design ◽  
Open Loop ◽  
The State ◽  
Magnetic Suspension ◽  
Loop Model ◽  
Closed Loop Identification ◽  
Noise Statistics

This paper presents an iterative LQG controller design approach for a linear stochastic system with an uncertain openloop model and unknown noise statistics. This approach consists of closed-loop identification and controller redesign cycles. In each cycle, the closed-loop identification method is used to identify an open-loop model and a steady-state Kalman filter gain from closed-loop input/output test data obtained by using a feedback LQG controller designed from the previous cycle. Then the identified open-loop model is used to redesign the state feedback. The state feedback and the identified Kalman filter gain are used to form an updated LQG controller for the next cycle. This iterative process continues until the updated controller converges. The proposed controller design is demonstrated by numerical simulations and experiments on a highly unstable large-gap magnetic suspension system.

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