Input Estimation from Sparse Measurements in LPV Systems and Isotopic Ratios in Polar Firns

Emmanuel Witrant (1) , Patricia Martinerie (2)

1 GIPSA-SLR - GIPSA - Systèmes linéaires et robustesse
2 LGGE - Laboratoire de glaciologie et géophysique de l'environnement

We adress the problem of inverse input reconstruction for linear parameter-varying (LPV) systems when only a limited amount of data (i.e. sparse measurements at final time) is available. We include the LPV property by deriving a time-varying Green's function that models the input/output behavior. The estimation is achieved by solving a least-squares optimization problem parameterized in terms of the input rugosity (regularization term) to take into account the under-constrained nature of the problem. Several automatic tuning methods for the rugosity are described, based on stochastic analysis of the data. A new LPV model is derived for the isotopic ratio of chemical species and our results are applied to the atmospheric history reconstruction of trace gases from polar firn measurements, a problem of prime interest in the environmental sciences community.

Domaines: Automatique / Robotique; Glaciologie

Emmanuel Witrant, Patricia Martinerie. Input Estimation from Sparse Measurements in LPV Systems and Isotopic Ratios in Polar Firns. IFAC Joint conference SSSC - 5th Symposium on System Structure and Control, Feb 2013, Grenoble, France. pp.150. ⟨hal-00931664⟩