LPV Model Identification For The Stall And Surge Control of a Jet Engine

International audienceAn MR damper is a device that exhibits a high nonlinear and complex behavior with a hysteresis phenomenon. A comparison between different state-of-the-art and a Linear Parameter Varying (LPV) models for Magneto-Rheological (MR) dampers is presented. Several experimental datasets validate that Linear Parameter Varying (LPV)-based model outperforms the classical MR damper models for 51% than any structures considering the Error to Signal Ratio index and 37% better considering the Squared root of Sum of Squared Errors index

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“…Matrices dependency of the scheduling variable is proposed by means of a polynomial linear system [11], [12]:…”

Section: A State-of-the-art Modelsmentioning

confidence: 99%

LPV-based MR damper modelling

Diaz-Salas

Ramírez-Mendoza

Sename

et al. 2011

2011 19th Mediterranean Conference on Control &Amp; Automation (MED)

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“…In order to design a gain scheduling control, see (Zappa et al, 2001), we need to model the compression system dynamics into an LPV form. The identification of the LPV model form data is extensively reported in (Bamieh et al, 2001). …”

Section: Examplementioning

confidence: 99%

LPV models: Identification for gain scheduling control

Bamieh

Giarré

2001

2001 European Control Conference (ECC)

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In this paper the use of discrete-time Linear Parameter Varying (LPV) models for the gain scheduling control and identification methods for non-linear or time-varying system is considered. We report an overview on the existing literature on LPV systems for gain scheduling control and identification. Moreover, assuming that inputs, outputs and the scheduling parameters are measured, and a form of the functional dependence of the coefficients on the parameters is known, we show how the identification problem can be reduced to a linear regression so that a Least Mean Square and Recursive Least Square identification algorithm can be reformulated. Our methodology is applied for the identification of the LPV model of the stall and surge control for compressors of jet engines.

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Identification of linear parameter varying models

Bamieh

Giarré

2002

Intl J Robust & Nonlinear

367

120

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SUMMARYWe consider identification of a certain class of discrete-time nonlinear systems known as linear parameter varying system. We assume that inputs, outputs and the scheduling parameters are directly measured, and a form of the functional dependence of the system coefficients on the parameters is known. We show how this identification problem can be reduced to a linear regression, and provide compact formulae for the corresponding least mean square and recursive least-squares algorithms. We derive conditions on persistency of excitation in terms of the inputs and scheduling parameter trajectories when the functional dependence is of polynomial type. These conditions have a natural polynomial interpolation interpretation, and do not require the scheduling parameter trajectories to vary slowly. This method is illustrated with a simulation example using two different parameter trajectories.

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LPV Model Identification For The Stall And Surge Control of a Jet Engine

Cited by 4 publications

References 17 publications

LPV-based MR damper modelling

LPV-based MR damper modelling

LPV models: Identification for gain scheduling control

Identification of linear parameter varying models

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