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Transfer function

About: Transfer function is a research topic. Over the lifetime, 14362 publications have been published within this topic receiving 214983 citations. The topic is also known as: system function & network function.


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DOI
01 Jan 2003
TL;DR: In this article, a model-based control of thermoacoustic combustion instabilities in lean premixed combustion systems is presented, where the model of each component or subsystem is obtained analytically, numerically, or by making use of experimental techniques.
Abstract: This work deals with modeling and control of thermoacoustic combustion instabilities in lean premixed combustion systems. Because of the complex interactions present in thermoacoustic systems, a network modeling approach is used. The model of each network element or subsystem is obtained analytically, numerically, or by making use of experimental techniques. The dynamics of a network system are determined experimentally by making use of a transfer matrix measurement technique. The transfer functions of a premixed flame have been determined experimentally on an atmospheric combustion test facility with a full-scale gas turbine burner, for a wide variety of operating conditions. An analytical model of the dynamic behavior of the reaction zone was made. In this model, the heat release fluctuations are assumed to be caused by fluctuations of the mass fraction of fuel and by fluctuations in the burning velocity. The model proved to be in good agreement with experimental results. Wave propagation in complex three-dimensional geometries is modeled by making use of a modal expansion technique. The modes used for the modal expansion can be obtained analytically for relatively simple geometries, or numerically (finite element method) for geometries of any complexity. By representing the modal expansion in state-space, a very numerically efficient and robust model is obtained. The thermoacoustic network model combines the state-space representations of the sub-systems in one system. The system can be analyzed in the time domain or in the frequency domain. The stability analysis is straightforward and does not require a numerical search. Non linear elements can easily be incorporated in the time domain simulation. This novel method has been validated by comparison with analytic solutions of simple thermoacoustic systems found in literature, by comparison with Finite Element codes, and by comparison with experimental results. An excellent agreement was found for all comparisons. When including non-linear elements in an annular system, a rotating acoustic field is predicted, which corresponds to experimental observations. This result has been verified analytically. Based on network models, a model based controller has been obtained using H∞ optimization. This controller has been tested in simulation and experiment on a single burner rig and proved to suppress acoustic levels by more than 25dB. An adaptive controller, based on a genetic algorithm, has been developed that does not require any knowledge about the system. This controller has been tested and proved to have similar performance as the model-based controllers. An active control system for multi-burner configurations has been developed and proved to perform well in simulations.

81 citations

Journal ArticleDOI
TL;DR: In this article, the authors considered the d-dimensional case of the one-to-one correspondence between one-dimensional linear (stationary, causal) input/state/output systems and scattering systems with one evolution operator, in which the scattering function coincides with the transfer function of the linear system.
Abstract: The one-to-one correspondence between one-dimensional linear (stationary, causal) input/state/output systems and scattering systems with one evolution operator, in which the scattering function of the scattering system coincides with the transfer function of the linear system, is well understood, and has significant applications in H∞ control theory. Here we consider this correspondence in the d-dimensional setting in which the transfer and scattering functions are defined on the polydisk. Unlike in the onedimensional case, the multidimensional state space realizations and the corresponding multi-evolution scattering systems are not necessarily equivalent, and the cases d = 2 and d > 2 differ substantially. A new proof of Ando’s dilation theorem for a pair of commuting contraction operators and a new statespace realization theorem for a matrix-valued inner function on the bidisk are obtained as corollaries of the analysis.

81 citations

PatentDOI
TL;DR: In this article, an adaptive eigenvalue decomposition algorithm (AEDA) is employed to estimate the channel impulse response from the sound source to each of a pair of microphones, and then uses these estimated impulse responses to determine the time delay of arrival (TDOA) between the two microphones by measuring the distance between the first peaks thereof (i.e., the first significant taps of the corresponding transfer functions).
Abstract: A real-time passive acoustic source localization system for video camera steering advantageously determines the relative delay between the direct paths of two estimated channel impulse responses. The illustrative system employs an approach referred to herein as the “adaptive eigenvalue decomposition algorithm” (AEDA) to make such a determination, and then advantageously employs a “one-step least-squares algorithm” (OSLS) for purposes of acoustic source localization, providing the desired features of robustness, portability, and accuracy in a reverberant environment. The AEDA technique directly estimates the (direct path) impulse response from the sound source to each of a pair of microphones, and then uses these estimated impulse responses to determine the time delay of arrival (TDOA) between the two microphones by measuring the distance between the first peaks thereof (i.e., the first significant taps of the corresponding transfer functions). In one embodiment, the system minimizes an error function (i.e., a difference) which is computed with the use of two adaptive filters, each such filter being applied to a corresponding one of the two signals received from the given pair of microphones. The filtered signals are then subtracted from one another to produce the error signal, which is minimized by a conventional adaptive filtering algorithm such as, for example, an LMS (Least Mean Squared) technique. Then, the TDOA is estimated by measuring the “distance” (i.e., the time) between the first significant taps of the two resultant adaptive filter transfer functions.

81 citations

Journal ArticleDOI
Abstract: This paper represents a sequel to an earlier paper, where a unified study of the applications of Volterra functional series to nonlinear analysis is presented. The same philosophy is followed here, giving special emphasis on frequency-domain results which either have not been published before, or where rigour had been lacking. Some of the results presented include generalisations or rigorous formalisations of wellknown special cases. Explicit and recursive formulas for obtaining n th-order transfer functions of composite nonlinear systems are presented. A recursive method for obtaining the n th-order output of a nonlinear circuit by solving a linear circuit n times is derived. Each time different input sources arc used. Recursive formulas for obtaining the n th-order transfer functions of nonlinear circuits are then generated. These results are used to obtain formulas for n th-order transfer functions of cascade systems, as well as inverse systems. Methods for synthesising nonlinear circuits and inverse systems via feedback configurations are given. Finally, the general structures of transfer functions for a large class of nonlinear systems are also derived.

81 citations

Journal ArticleDOI
01 May 1998
TL;DR: In this paper, the multivariable continuous-time generalised predictive controller (CGPC) is recast in a state-space form and shown to include generalised minimum variance (GMV) and a new algorithm, predictive GMV (PGMV) as special cases.
Abstract: The multivariable continuous-time generalised predictive controller (CGPC) is recast in a state-space form and shown to include generalised minimum variance (GMV) and a new algorithm, predictive GMV (PGMV) as special cases. Comparisons are drawn with the exact linearisation methods of nonlinear control, and it is noted that, unlike the transfer function approach, the state-space approach extends readily to the nonlinear case. The resulting state space design algorithms are conceptually and algorithmically simpler than the corresponding transfer function based versions and have been realised as a freely available Matlab tool-box.

81 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023351
2022810
2021329
2020421
2019461
2018493