Identification of a thermal system using continuous linear parameter-varying fractional modelling

Gabano, Jean-Denis; Poinot, Thierry; Kanoun, Houcem

doi:10.1049/iet-cta.2010.0222

Cited by 67 publications

(37 citation statements)

References 17 publications

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“…A multi-step procedure is then implemented (Tóth, 2008): local experiments are first carried out in which the ultracapacitor bias voltage is held constant and the input charging current is excited; local linear time invariant (LTI) continuous fractional models are then estimated based on these sets of local input/output (I/O) measurements and finally, an interpolation phase is performed to derive a final global parameter-dependent model. Such a viewpoint has already been considered in Steinbuch et al (2003), Van Helvoort, Steinbuch, Lambrechts, and van de Molengraft (2004), and Paijmans, Symens, Van Brussel, and Swevers (2008) for motion and robot control or in Gabano and Poinot (2011) for thermal system identification. The resulting LPV fractional model is then validated by comparing the estimated low level voltage provided for different sets of charging or discharging current profiles with the voltage measurements acquired on an available commercial ultracapacitor at any initial bias voltage.…”

Section: Introductionmentioning

confidence: 99%

LPV continuous fractional modeling applied to ultracapacitor impedance identification

Gabano

Poinot

Kanoun

2015

Control Engineering Practice

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Section: Introductionmentioning

confidence: 99%

LPV continuous fractional modeling applied to ultracapacitor impedance identification

Gabano

Poinot

Kanoun

2015

Control Engineering Practice

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“…The PRBS technique has previously been applied to thermal systems [17]. The results can be used to assess thermal performance; however, the long time constants and significant noise associated with thermal systems make the PRBS process time consuming.…”

Section: Introductionmentioning

confidence: 99%

Improved Bandwidth and Noise Resilience in Thermal Impedance Spectroscopy by Mixing PRBS Signals

Davidson

Stone

Foster

et al. 2014

IEEE Trans. Power Electron.

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This paper presents a method of mixing pseudorandom binary sequences (PRBSs) to form a new signal that can be used to obtain the thermal impedance spectrum of power electronic systems. The proposed technique increases the useful frequency range of a PRBS by mixing two identical sequences at different frequencies. The new signal incorporates the frequency responses of both contributions. Mixing can be performed using a number of mathematical operators and analysis reveals that AND is the operator of choice since it has the lowest average input power for the same effectiveness. The bandwidth, frequency-domain representation, and noise resilience of PRBS signals are also reported. It is shown that the noise floor is significantly reduced under the mixed technique, which allows lower impedances to be measured under noisy measurement conditions. For a typical 8-bit PRBS, mixing reduces the noise floor by a factor of 10.5. Simulated and experimental validation are performed and results show the mixed scheme offers increased bandwidth, reduced computation and improved noise resilience compared to single PRBS techniques.

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“…In an analysis of the past ten years of trends and results in the fractional calculus application to dynamic problems of solid mechanics, the method of mechanical system dynamics analysis based on fractional calculus has gradually become one of main methods in the dynamics analysis of engineering [21]. Fractional calculus has been introduced into the various engineering and science domains [22,23], including image processing [24][25][26], thermal systems identification [27,28], biological tissues identification [29][30][31], control theory and application [32][33][34][35][36], signal processing [37,38], path planning [39] and path tracking [40,41], robotics [42,43], mechanical damping [10,44], battery [45,46], mechanics [47,48], diffusion [49,50], chaos [51,52], and others. Therefore, the application of fractional calculus has become a focus of international academic research.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm-Based Identification of Fractional-Order Systems

Zhou

Cao

Chen

2013

Entropy

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Abstract:Fractional calculus has become an increasingly popular tool for modeling the complex behaviors of physical systems from diverse domains. One of the key issues to apply fractional calculus to engineering problems is to achieve the parameter identification of fractional-order systems. A time-domain identification algorithm based on a genetic algorithm (GA) is proposed in this paper. The multi-variable parameter identification is converted into a parameter optimization by applying GA to the identification of fractional-order systems. To evaluate the identification accuracy and stability, the time-domain output error considering the condition variation is designed as the fitness function for parameter optimization. The identification process is established under various noise levels and excitation levels. The effects of external excitation and the noise level on the identification accuracy are analyzed in detail. The simulation results show that the proposed method could identify the parameters of both commensurate rate and non-commensurate rate fractional-order systems from the data with noise. It is also observed that excitation signal is an important factor influencing the identification accuracy of fractional-order systems.

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Identification of a thermal system using continuous linear parameter-varying fractional modelling

Cited by 67 publications

References 17 publications

LPV continuous fractional modeling applied to ultracapacitor impedance identification

LPV continuous fractional modeling applied to ultracapacitor impedance identification

Improved Bandwidth and Noise Resilience in Thermal Impedance Spectroscopy by Mixing PRBS Signals

Genetic Algorithm-Based Identification of Fractional-Order Systems

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