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  4. Identification of Continuous-Time Linear Parameter Varying Systems with Noisy Scheduling Variable Using Local Regression
 
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Identification of Continuous-Time Linear Parameter Varying Systems with Noisy Scheduling Variable Using Local Regression

Journal
IEEE Access
Date Issued
2024-01-01
Author(s)
Padilla, Arturo
Garnier, Hugues
YUZ EISSMANN, JUAN IGNACIO  
Departamento de Electrónica  
Chen, Fengwei
Poblete, Carlos Muñoz
DOI
10.1109/ACCESS.2024.3371897
Abstract
Some nonlinear systems can be represented through linear parameter varying models. In this work, we address the estimation of continuous-time linear parameter varying models in output error form, using a refined instrumental variable method. A distinguished feature of a linear parameter varying model is that it has parameters that depend on an external signal called the scheduling variable. In this paper, we assume that the scheduling variable is noisy, a condition which is often met in practice, but not frequently considered in the literature. On the other hand, there are applications in which the noise-free version of the scheduling variable is smooth. Under such scenario we can simply filter the scheduling variable before estimating the linear parameter model. Nonetheless, there are cases where special smoothing techniques are required. In this study, we consider one of these special cases, and we use the well-known local regression method as smoothing technique. A numerical example based on a Monte Carlo simulation shows the benefits of the proposed approach.
Subjects

Continuous-time syste...

instrumental variable...

linear parameter vary...

local regression

smoothing method

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