Open-loop versus closed-loop identification of Box-Jenkins models: a new variance analysis

2006 ◽  
Author(s):  
X. Bombois ◽  
M. Gevers ◽  
G. Scorletti
Keyword(s):  
Closed Loop ◽  
Open Loop ◽  
Variance Analysis ◽  
Closed Loop Identification

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.

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.

A Neural Network-Based Closed Loop Identification of a Magnetic Bearings System

2004 ◽  
Author(s):  
Jose´ Medina ◽  
Mo´nica Parada ◽  
Victor Guzma´n ◽  
Luis Medina ◽  
Sergio Di´az
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
Magnetic Bearing ◽  
External Input ◽  
Open Loop ◽  
Active Magnetic Bearing ◽  
The Neural Network ◽  
Closed Loop Identification ◽  
Artificial Neural Network Ann ◽  
Amb System

This paper deals with the identification of a radial-type active magnetic bearing (AMB) system using Artificial Neural Network (ANN). Identification and validation experiments are performed on a laboratory magnetic bearing system. Since the electromechanical configuration is inherently unstable, the identification data is gathered while the AMB is operating in closed loop with a controller in the loop. From this data, the identification procedure generates an open-loop plant model. A NNARX (Neural network autoregressive external input model) structure is proposed and evaluated for emulating the system’s dynamic. The model is implemented by a Neural network, constructed using a multilayer perceptron (MLP) topology, and trained using as inputs the rotor’s displacements and excitation currents. Validation tests are performed under perturbation conditions (impact applied on the rotor). Results show that the neural network based model presented here is a powerful tool for dynamic plant’s identification, and that it could be also suitable for robust control application.

DIRECT CLOSED-LOOP IDENTIFICATION OF MULTI-INPUT SYSTEMS: VARIANCE ANALYSIS

2006 ◽  
Vol 39 (1) ◽  
pp. 873-878
Author(s):  
L. Mišković ◽  
A. Karimi ◽  
D. Bonvin ◽  
M. Gevers
Keyword(s):  
Closed Loop ◽  
Variance Analysis ◽  
Closed Loop Identification

scholarly journals Closed-Loop Identification of Baroreflex Properties in the Frequency Domain

2021 ◽  
Vol 15 ◽  
Author(s):  
Toru Kawada ◽  
Keita Saku ◽  
Tadayoshi Miyamoto
Keyword(s):  
Frequency Domain ◽  
Dynamic Characteristics ◽  
Closed Loop ◽  
A Priori ◽  
Open Loop ◽  
Arterial Baroreflex ◽  
Loop Analysis ◽  
Autonomic Nervous ◽  
Carotid Sinus Baroreflex ◽  
Closed Loop Identification

The arterial baroreflex system plays a key role in maintaining the homeostasis of arterial pressure (AP). Changes in AP affect autonomic nervous activities through the baroreflex neural arc, whereas changes in the autonomic nervous activities, in turn, alter AP through the baroreflex peripheral arc. This closed-loop negative feedback operation makes it difficult to identify open-loop dynamic characteristics of the neural and peripheral arcs. Regarding sympathetic AP controls, we examined the applicability of a nonparametric frequency-domain closed-loop identification method to the carotid sinus baroreflex system in anesthetized rabbits. This article compares the results of an open-loop analysis applied to open-loop data, an open-loop analysis erroneously applied to closed-loop data, and a closed-loop analysis applied to closed-loop data. To facilitate the understanding of the analytical method, sample data files and sample analytical codes were provided. In the closed-loop identification, properties of the unknown central noise that modulated the sympathetic nerve activity and the unknown peripheral noise that fluctuated AP affected the accuracy of the estimation results. A priori knowledge about the open-loop dynamic characteristics of the arterial baroreflex system may be used to advance the assessment of baroreflex function under closed-loop conditions in the future.

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