Showing papers by "José Ragot published in 2020"
TL;DR: The objective is to determine the state estimates consisting in the smallest interval containing the real-state value caused by the parametric uncertainties, characterised by the lower and upper bounds of the state trajectory.
Abstract: The aim of this article is the state estimation of uncertain polytopic dynamic systems. The parametric uncertainties affecting the system are time varying, unknown and bounded with known bounds. The objective is to determine the state estimates consisting in the smallest interval containing the real state value caused by the parametric uncertainties. This set will be characterized by the lower and upper bounds of the state trajectory. Given the uncertainty bounds, the set can be computed by a direct simulation of the system but a more accurate estimation is obtained with a Luenberger-type observer, fed with the system measurements. The proposed observer is designed to minimize the interval width of the estimates. The observer gains are obtained by solving an optimization problem under LMI constraints. The efficiency of the proposed approach is illustrated by numerical examples.
7 citations
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TL;DR: An approach to verify identifiability of unknown parameters for LPV state-space models makes use of a parity-space like formulation to eliminate the states of the model and provides a framework for both continuous-time and discrete-time models.
Abstract: In several model-based system maintenance problems, parameters are used to represent unknown characteristics of a component, equipment degradation, etc. This allows for modelling constant, slow-varying terms. The identifiability of these parameters is an important condition to estimate them. Linear Parameter Varying (LPV) models are being increasingly used in the industries as a bridge between linear and nonlinear models. Techniques exist that can rewrite some nonlinear models in LPV form. However, the problem of identifiability of these models is still at a nascent stage. In this paper, we propose an approach to verify identifiability of unknown parameters for LPV state-space models. It makes use of a parity-space like formulation to eliminate the states of the model. The resulting input-output-parameter equation is analysed to verify the identifiability of the original model or a subset of unknown parameters. This approach provides a framework for both continuous-time and discrete-time models and we illustrate it using examples.
3 citations
TL;DR: A new detection method which can detect regime change instants in sequential time series data is proposed based on a sensitivity study of a global model combining, with a multiplicative effect, local models describing the different operating modes without knowing their parameters.
Abstract: The purpose of detecting changes in the operating regime of a system is to identify abrupt changes from one regime to another. This paper proposes a new detection method which can detect regime cha...
2 citations