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Michele Taragna

Publications and source records attributed to Michele Taragna.

3 recordsLinked to original sources

On the Scientific Method: The Role of Hypotheses and Involved Mathematics

The paper investigates the role of data, hypotheses and mathematical methods that can be used in the discovery of a law y=fo(u), relating variables u and y of a physical phenomenon, making use of experimental measurements of such variables. Since the exact knowledge of the function fo cannot be expected, the problem of deriving approximate functions giving a small approximation error, measured by some function norm, is discussed. The main contributions of the paper are summarized as follows. At first, it is proven that deriving a reliable approximation, i.e., having a finite error, is not possible using measured data only. Thus, for deriving a reliable approximation, hypotheses on the function fo and on the disturbances corrupting the measurements must be introduced. Second, necessary and sufficient conditions for deriving a reliable approximation are provided. If such conditions are satisfied, suitable accuracy properties of the approximation can be defined, called theoretical properties. Third, it is shown that it is not possible to verify the conditions necessary for deriving a reliable approximation, but it is possible to verify that hypotheses on fo and on the disturbances are falsified by experimental measurements, showing that no function and disturbances satisfying the given hypotheses exist, able to reproduce the measurements (this is called falsification property). The above properties are then discussed for hypotheses belonging to the following classes: Parametric Probabilistic, where fo is assumed to be a function depending on a vector p and the disturbances are assumed to be stochastic variables; Set Membership class, where fo is assumed to be a bounded smooth function and the disturbances are assumed to be bounded variables; Parametric Set Membership class, able to integrate Parametric Probabilistic hypotheses with Set Membership hypotheses.

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Set Membership based Nonlinear Model Predictive Control

We present a numerically efficient Nonlinear Model Predictive Control (NMPC) approach, called Set Membership based NMPC (SM-NMPC). In particular, a Set Membership method is used to derive from data an approximation and tight bounds on the optimal NMPC control law. These quantities are used to reduce the dimensionality and volume of the search domain of the NMPC optimization problem, allowing a significant shortening of the computation time. The proposed SM-NMPC strategy is tested in simulation, considering realistic autonomous vehicle scenarios, like parallel parking and lane keeping maneuvers.

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Leading Impulse Response Identification via the Weighted Elastic Net Criterion

This paper deals with the problem of finding a low-complexity estimate of the impulse response of a linear time-invariant discrete-time dynamic system from noise-corrupted input-output data. To this purpose, we introduce an identification criterion formed by the average (over the input perturbations) of a standard prediction error cost, plus a weighted l1 regularization term which promotes sparse solutions. While it is well known that such criteria do provide solutions with many zeros, a critical issue in our identification context is where these zeros are located, since sensible low-order models should be zero in the tail of the impulse response. The flavor of the key results in this paper is that, under quite standard assumptions (such as i.i.d. input and noise sequences and system stability), the estimate of the impulse response resulting from the proposed criterion is indeed identically zero from a certain time index (named the leading order) onwards, with arbitrarily high probability, for a sufficiently large data cardinality. Numerical experiments are reported that support the theoretical results, and comparisons are made with some other state-of-the-art methodologies.

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