Minimal required excitation for closed-loop identification: Some implications for data-driven, system identification

2015 ◽  
Vol 27 ◽  
pp. 22-35 ◽  
Author(s):  
Yuri A.W. Shardt ◽  
Biao Huang ◽  
Steven X. Ding
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Data Driven ◽  
Driven System ◽  
Closed Loop Identification

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 Data-driven System Identification of Thermal Systems using Machine Learning

2021 ◽  
Vol 54 (7) ◽  
pp. 162-167
Author(s):  
Ştefan-Cristian Nechita ◽  
Roland Tóth ◽  
Koos van Berkel
Keyword(s):  
Machine Learning ◽  
System Identification ◽  
Data Driven ◽  
Thermal Systems ◽  
Driven System

Discrete-Time Closed-Loop Model Identification of Fixed-Structure Unstable Multivariable Systems

2007 ◽  
Author(s):  
Huzefa Shakir ◽  
Won-Jong Kim
Keyword(s):  
System Identification ◽  
Discrete Time ◽  
Closed Loop ◽  
Model Identification ◽  
Order Reduction ◽  
Loop System ◽  
Loop Model ◽  
Frequency Range ◽  
Closed Loop System ◽  
Closed Loop Identification

This paper presents improved empirical representations of a general class of open-loop unstable systems using closed-loop system identification. A multi-axis magnetic-levitation (maglev) nanopositioning system with an extended translational travel range is used as a test bed to verify the closed-loop system-identification method proposed in this paper. A closed-loop identification technique employing the Box-Jenkins (BJ) method and a known controller structure is developed for model identification and validation. Direct and coupling transfer functions (TFs) are then derived from the experimental input-output time sequences and the knowledge of controller dynamics. A persistently excited signal with a frequency range of [0, 2500] Hz is used as a reference input. An order-reduction algorithm is applied to obtain TFs with predefined orders, which give a close match in the frequency range of interest without missing any significant plant dynamics. The entire analysis is performed in the discrete-time domain in order to avoid any errors due to continuous-to-discrete-time conversion and vice versa. Continuous-time TFs are used only for order-reduction and performance analysis of the identified plant TFs. Experimental results in the time as well as frequency domains verified the accuracy of the plant TFs and demonstrated the effectiveness of the closed-loop identification and order-reduction methods.

System Identification of Small Loudspeakers Using ARMA Model

2010 ◽  
Author(s):  
Jaewon Choi ◽  
Mohsen Nakhaeinejad ◽  
Michael D. Bryant
Keyword(s):  
System Identification ◽  
Electrical Impedance ◽  
Moving Average ◽  
Arma Model ◽  
Data Driven ◽  
Autoregressive Moving Average ◽  
Autoregressive Moving Average Model ◽  
Driven System ◽  
Moving Average Model ◽  
Arma Modeling

This study illustrates a data driven system identification method for loudspeaker model estimation using the knowledge of the underlying physics of loudspeakers. In this study, diaphragm displacement is analyzed to estimate the model structure and parameters based on impulse response equivalent sampling and autoregressive moving average model. The estimated loudspeaker models are compared in the frequency response function plot. It is shown that the autoregressive moving average (ARMA) based loudspeaker models are comparable to the model estimated by the conventional method based on electrical impedance. Also ARMA modeling strategies with and without knowledge of the physics-based model are compared. Some issues related to ARMA modeling are addressed.

scholarly journals Reconstruction of Governing Equations from Vibration Measurements for Geometrically Nonlinear Systems

Lubricants ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 64 ◽  
Author(s):  
Marco Didonna ◽  
Merten Stender ◽  
Antonio Papangelo ◽  
Filipe Fontanela ◽  
Michele Ciavarella ◽  
...  
Keyword(s):  
System Identification ◽  
Differential Equations ◽  
Duffing Oscillator ◽  
Vibration Signal ◽  
Data Driven ◽  
Geometrically Nonlinear ◽  
Limited Data ◽  
Weakly Coupled ◽  
Driven System ◽  
Quasi Linear System

Data-driven system identification procedures have recently enabled the reconstruction of governing differential equations from vibration signal recordings. In this contribution, the sparse identification of nonlinear dynamics is applied to structural dynamics of a geometrically nonlinear system. First, the methodology is validated against the forced Duffing oscillator to evaluate its robustness against noise and limited data. Then, differential equations governing the dynamics of two weakly coupled cantilever beams with base excitation are reconstructed from experimental data. Results indicate the appealing abilities of data-driven system identification: underlying equations are successfully reconstructed and (non-)linear dynamic terms are identified for two experimental setups which are comprised of a quasi-linear system and a system with impacts to replicate a piecewise hardening behavior, as commonly observed in contacts.

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