1,721,241 research outputs found
Psicologia di Comunità. Famiglie miste e comunità
No full textL’interesse che la psicologia di comunità nutre per il tema delle famiglie è stato già oggetto di un numero monografico della Rivista (n.1/2008), nel quale ci si è soffermati sui processi generativi/degenerativi che sono all’opera e nella famiglia e nella comunità, scommettendo sulla connessione virtuosa tra generatività familiare e sociale.
In questo numero l’obiettivo è mettere a fuoco una tra le tante configurazioni familiari del nostro presente la cui assodata variabilità interculturale rende più probabile, e se vogliamo anche auspicabile, l’incontro con l’Altro già nelle relazioni di coppia binazionale
Control of MIMO nonlinear systems: A data-driven model inversion approach
Full text linkA data-driven control design approach for Multiple Input Multiple Output nonlinear systems is presented in this paper. The approach, called Nonlinear Inversion Control (NIC), is based on the identification of a polynomial prediction model of the system to control and the on-line inversion of this model. The main features of the NIC approach can be summarized as follows: it does not require a physical model of the plant to control which, in many real-world situations, may be difficult to derive; it can guarantee a priori properties such as closed-loop stability and tracking error accuracy; it is general, numerically efficient and relatively simple. Extensive simulations are carried out to test the numerical efficiency of the NIC approach. A simulated example of industrial interest is also presented, concerned with control of a robotic manipulator
Design of experiments for nonlinear system identification: A set membership approach
Full text linkDesign of Experiments (DoE) is an important step in system identification. Regardless of the chosen model structure and identification method, the DoE quality determines an upper bound on the accuracy of the identified models. One of the greatest challenges in this context is to design an experiment which gives the maximum information about the dynamics of the system of interest. In this paper, a novel DoE algorithm for input-constrained MISO nonlinear systems, based on set membership identification, is proposed. The DoE algorithm is aimed to minimize the so-called radius of information, a quantity giving the worst-case model error. Two numerical examples are presented, showing the effectiveness of the approach and its potential in view of real-world applications
Control analysis and design via randomised coordinate polynomial minimisation
Full text linkA relevant family of control analysis and design problems can be reduced to the minimisation of a multivariate polynomial objective over a semialgebraic set. Such control problem formulations, however, are nonconvex in general and hard to solve in practice. In this paper, we propose a novel approach to polynomial control design based on iterations that involve either a fast coordinate-wise minimisation or a univariate minimisation along a randomly chosen direction. We provide a detailed iteration complexity analysis of the method, and we prove its convergence in probability to the global optimum. The practical effectiveness of the proposed method is also illustrated via a comparison with state-of-the-art tools available in the literature. An example of application to an automated space rendezvous manoeuvre is finally presented, showing how the method can be particularly relevant in the context of nonlinear model predictive control
A time-varying SIRD model for the COVID-19 contagion in Italy
Full text linkThe purpose of this work is to give a contribution to the understanding of the COVID-19 contagion in Italy. To this end, we developed a modified Susceptible-Infected-Recovered-Deceased (SIRD) model for the contagion, and we used official data of the pandemic for identifying the parameters of this model. Our approach features two main non-standard aspects. The first one is that model parameters can be time-varying, allowing us to capture possible changes of the epidemic behavior, due for example to containment measures enforced by authorities or modifications of the epidemic characteristics and to the effect of advanced antiviral treatments. The time-varying parameters are written as linear combinations of basis functions and are then inferred from data using sparse identification techniques. The second non-standard aspect resides in the fact that we consider as model parameters also the initial number of susceptible individuals, as well as the proportionality factor relating the detected number of positives with the actual (and unknown) number of infected individuals. Identifying the model parameters amounts to a non-convex identification problem that we solve by means of a nested approach, consisting in a one-dimensional grid search in the outer loop, with a Lasso optimization problem in the inner step
A Modified SIR Model for the COVID-19 Contagion in Italy
Full text linkThe purpose of this work is to give a contribution to the understanding of the COVID-19 contagion in Italy. To this end, we developed a modified Susceptible-Infected-Recovered (SIR) model for the contagion, and we used official data of the pandemic up to March 30th, 2020 for identifying the parameters of this model. The non standard part of our approach resides in the fact that we considered as model parameters also the initial number of susceptible individuals, as well as the proportionality factor relating the detected number of positives with the actual (and unknown) number of infected individuals. Identifying the contagion, recovery and death rates as well as the mentioned parameters amounts to a non-convex identification problem that we solved by means of a two-dimensional grid search in the outer loop, with a standard weighted least-squares optimization problem as the inner step
Direct filtering: A new approach to optimal filter design for nonlinear systems
No full textOptimal filters for nonlinear systems are in general difficult to derive or implement. The common approach is to use approximate solutions such as extended Kalman filters, ensemble filters or particle filters. However, no optimality properties can be guaranteed by these approximations, and even the stability of the estimation error cannot often be ensured. Another relevant issue is that, in most practical situations, the system whose variables have to be estimated is not known, and a two-step procedure is adopted, based on model identification from data and filter design from the identified model. However, the designed filter may display large performance deteriorations in the case of modeling errors. In this paper, a new approach overcoming these issues is proposed, allowing the design of optimal filters for nonlinear systems in both the cases of known and unknown system. The approach is based on the direct filter design from a set of data generated by the system. Either experimental or simulated data can be used for design. A bound on the number of data necessary to ensure a given filter accuracy is also provided, showing that the proposed approach is not affected by the curse of dimensionality. © 2012 IEEE
Direct identification of optimal filters for LPV systems
No full textDirect identification of filters for Linear Parameter Varying (LPV) systems is considered. In the literature on filter design, the system whose state has to be estimated is usually assumed known. However, in most applications, this assumption does not hold, and a two-step procedure is adopted: 1) an LPV model is identified from a set of noise-corrupted data; 2) on the basis of the identified model, an LPV Kalman filter is designed. In this paper, the idea of directly identifying the LPV filter from data is investigated. In previous works by the authors, it has been shown that the direct identification may be more convenient than the two-step design. In some of these works, optimal filter design techniques for time invariant systems have been developed. In the present paper, an approach for the direct identification of optimal filters for LPV systems is proposed. The approach is developed within a Set Membership framework and optimality refers to minimizing the worst-case estimation error. © 2008 IEEE
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