Identification of aircraft gas turbine dynamics using frequency-domain techniques

Evans, C.; Rees, D.; Borrell, A.

doi:10.1016/s0967-0661(99)00161-6

Cited by 33 publications

(12 citation statements)

References 9 publications

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“…Despite the desirable optimal Crest factor, for nonlinear system identification binary signals are not an option due to the lack of excitation of different amplitudes. For this work an excitation signal comprised of independent multisine signals as described in (Evan et al, 2000) was designed. This is explored in the following section.…”

Section: Excitation Signalmentioning

confidence: 99%

Approximate Model Predictive Control for Nonlinear Multivariable Systems

Witt¹,

Werner²

2010

Model Predictive Control

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Section: Excitation Signalmentioning

confidence: 99%

Approximate Model Predictive Control for Nonlinear Multivariable Systems

Witt¹,

Werner²

2010

Model Predictive Control

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“…In addition, the engine has an unknown combustion delay, which can be included as an estimated parameter in the frequency-domain estimator. Finally, s-domain models can be directly estimated in the frequency-domain and compared to the linearised thermodynamic models of the engine [5]. A physical interpretation can thus be made of the model poles and zeros and the estimated time delay.…”

Section: Linear Modellingmentioning

confidence: 99%

“…Parametric identification involves estimating continuous sdomain models with a pure time delay T d [10,11]. Parametric models were estimated at each operating point, using a model selection and validation procedure which has been described in detail in [2][3][4][5]…”

Section: Linear Modellingmentioning

confidence: 99%

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Global Nonlinear Modelling of Gas Turbine Dynamics Using NARMAX Structures

Chiras

Evans

Rees

2001

Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation;

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This paper examines the estimation of a global nonlinear gas turbine model using NARMAX techniques. Linear models estimated on small-signal data are first examined and the need for a global nonlinear model is established. A nonparametric analysis of the engine nonlinearity is then performed in the time and frequency domains. The information obtained from the linear modelling and nonlinear analysis is used to restrict the search space for nonlinear modelling. The nonlinear model is then validated using large-signal data and its superior performance illustrated by comparison with a linear model. This paper illustrates how periodic test signals, frequency domain analysis and identification techniques, and time-domain NARMAX modelling can be effectively combined to enhance the modelling of an aircraft gas turbine.

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“…Model-based control schemes are highly related to the accuracy of the process model. Evans et al (2000) concentrated on testing the gas turbine using small amplitude multisine signals and frequency domain techniques to identify linear models of high accuracy at a range of different operating points. Jurado and Cano (2004) presented the implementation of an efficient method for computing low-order linear system models of micro-turbines from time domain simulations.…”

Section: Introductionmentioning

confidence: 99%

Hammerstein-model-based predictive control of micro-turbines

Jurado

2006

Int. J. Energy Res.

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SUMMARYMicro-turbine operation is restricted due to mechanical, thermal, and flow limitations. Model predictive control is selected because it can explicitly handle the nonlinearities, and constraints of many variables in a single control formulation. The Hammerstein models are particular kinds of nonlinear systems where the nonlinear block is static and is accompanied by a linear system. This paper suggests a model-based controller for the regulation of a micro-turbine. The performances using both the linear and Hammerstein models are studied with constraints. The results show the capabilities of the proposed control to track a periodic reference trajectory and a significantly better closed-loop response.

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Identification of aircraft gas turbine dynamics using frequency-domain techniques

Cited by 33 publications

References 9 publications

Approximate Model Predictive Control for Nonlinear Multivariable Systems

Approximate Model Predictive Control for Nonlinear Multivariable Systems

Global Nonlinear Modelling of Gas Turbine Dynamics Using NARMAX Structures

Hammerstein-model-based predictive control of micro-turbines

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