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— In this paper, a new identification technique is introduced to estimate a linear fractional representation of a linear parameter-varying (LPV) system from local experiments by using a dedicated non-smooth optimization procedure. More precisely, the developed approach consists in estimating the parameters of an LPV state-space model from local(More)
Identifying a linear parameter-varying (LPV) model of a non-linear system from local experiments (i.e., experiments with small displacements around given positions) is a problem which still deserves attention. Rather than building a model either from the law of physics or from experimental data independently, the combination of an analytic and an(More)
Identifying a linear parameter-varying (LPV) model of a non-linear system from local experimental data is still a problem which deserves attention. Many difficulties related to the determination of the local models with respect to coherent bases have been recently pointed out and must be solved in order to ensure a good behavior of the interpolated LPV(More)
In this paper, the challenging problem of determining the unknown parameters of an identifiable LTI statespace representation of a stable system is addressed by resorting to a specific H<sub>&#x221E;</sub>-norm-based optimization algorithm. More specifically, by assuming the availability of a reliable fully-parameterized representation of the system to(More)
— In this paper, a new identification technique is introduced to estimate a linear fractional representation of a linear parameter-varying (LPV) system from local experiments by using a dedicated non-smooth optimization procedure. More precisely, the developed approach consists in estimating the parameters of an LPV state-space model from local(More)
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