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Either from a control theoretic viewpoint or from an analysis viewpoint it is necessary to convert smooth systems to discrete systems, which can then be implemented on computers for numerical simulations. Discrete models can be obtained either by discretizing a smooth model, or by directly modeling at the discrete level itself. One of the goals of this paper is to model port-Hamiltonian systems at the discrete level. We also show that the dynamics of the discrete models we obtain exactly correspond to the dynamics obtained via a usual discretization procedure. In this sense we offer an alternative to the usual procedure of modeling (at the smooth level) and discretization.

Invited Talk; International audience; This paper is aimed at exploiting Fault Detection and Isolation (FDI) techniques widely known in automatic control for solving online test problem in embedded Integrated Circuits (ICs). Before reaching this aim, we will briefly review the field of microelectronics testing, introducing basic concepts and techniques. We will next introduce FDI model-based approaches and their application for online testing of embedded ICs considering linear systems with potential faults and disturbances. The parity relation-based residual is specially suitable for this type of application. As an example, we will apply it to concurrent fault detection in a digital embedded filter. The proposed scheme will then be illustrated for a linear digital pass-band elliptic filter.

This paper considers the problem of estimating the parameters of an autoregressive (AR) process in presence of additive white noise and proposes a new identification method, based on theoretical results originally developed in errors-in-variables contexts. This approach allows to estimate the AR parameters, the driving noise variance and the variance of the additive noise in a congruent way in that these estimates assure the positive definiteness of the autocorrelation matrix. The performance of the proposed algorithm is compared with that of bias-compensated least-squares methods by means fo Monte Carlo simulations. The results show the effectivenesss of the new method also in presence of high amounts of noise.

Classical model–based fault detection schemes for linear multivariable systems require the definition of suitable residual functions. This paper shows the possibility of identifying residual generators even when the system model is unknown, by following a black–box approach. The result is obtained by using canonical input–output polynomial forms which lead to characterise in a straightforward fashion the basis of the subspace described by all possible residual generators. The performance of the proposed identification method is tested by means of a Monte Carlo simulation.