Black and Gray-Box Identification of a Hydraulic Pumping System

Barbosa, Bruno Henrique Groenner; Aguirre, Luís A.; Martinez, Carlos Barreira; Braga, Antônio de Pádua

doi:10.1109/tcst.2010.2042600

Cited by 36 publications

(56 citation statements)

References 20 publications

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“…In general, model structure is selected using orthogonal techniques as the error reduction ratio and parameters are estimated using prediction error minimization-based techniques. Freerun simulation is often used as a criterion to validate the polynomial NARMAX (Barbosa, Aguirre, Martinez, & Braga, 2011;Billings, 2013). In many cases, n step-ahead free predictions are performed and compared to a specific validation data set.…”

Section: The Polynomial Narmaxmentioning

confidence: 99%

A lower bound error for free-run simulation of the polynomial NARMAX

Nepomuceno

Martins

2016

Systems Science & Control Engineering

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A lower bound error for free-run simulation of the polynomial NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous input) is introduced. The ultimate goal of the polynomial NARMAX is to predict an arbitrary number of steps ahead. Free-run simulation is also used to validate the model. Although free-run simulation of the polynomial NARMAX is essential, little attention has been given to the error propagation to round off in digital computers. Our procedure is based on the comparison of two pseudo-orbits produced from two mathematical equivalent models, but different from the point of view of floating point representation. We apply successfully our technique for three identified models of the systems: sine map, Chua's circuit and Duffing-Ueda oscillator. This technique may be used to reject a simulation, if a required precision is greater than the lower bound error, increasing the numerical reliability in free-run simulation of the polynomial NARMAX. ARTICLE HISTORY

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Section: The Polynomial Narmaxmentioning

confidence: 99%

A lower bound error for free-run simulation of the polynomial NARMAX

Nepomuceno

Martins

2016

Systems Science & Control Engineering

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“…where J X (Q, P ) is given by (2) and M D (Q, P ) λ ⊗ P + β ⊗ Q + β T ⊗ Q T is the eigenvalue constraint written as a LMI corresponding to the the convex set D of the z-plane defined by…”

Section: Problem Statementmentioning

confidence: 99%

“…Prior information can be used in system identification to possibly improve some properties of the mathematical model [1,2,3]. Such gray-box approach is especially of interest when the dynamical data are limited in terms of persistence of excitation, signal-to-noise ratio, number of data samples, and, for nonlinear systems, coverage of operating points.…”

Section: Introductionmentioning

confidence: 99%

Mapping prior information onto LMI eigenvalue‐regions for discrete‐time subspace identification

Ricco

Teixeira

2020

IET Control Theory & Applications

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In subspace identification, prior information can be used to constrain the eigenvalues of the estimated state-space model by defining corresponding LMI regions. In this paper, first we argue on what kind of practical information can be extracted from historical data or step-response experiments to possibly improve the dynamical properties of the corresponding model and, also, on how to mitigate the effect of the uncertainty on such information. For instance, prior knowledge regarding the overshoot, the period between damped oscillations and settling time may be useful to constraint the possible locations of the eigenvalues of the discrete-time model. Then, we show how to map the prior information onto LMI regions and, when the obtaining regions are non-convex, to obtain convex approximations.

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“…For that purpose, a pseudo-random multi-level signal (PRMS), chirp signal, band-limited white noise, and all their combinations could be utilized to create an input signal exciting the system at the frequencies of interest. In the literature, the PRMS is the most common choice for the identification of hydraulic systems [24,40]. Therefore, the servo-valve manipulation signal shown in Fig.…”

Section: Black-box Approachmentioning

confidence: 99%