Dario Piga

High-Altitude Wind Power Generation

The paper presents the innovative technology of high-altitude wind power generation, indicated as Kitenergy, which exploits the automatic flight of tethered airfoils (e.g., power kites) to extract energy from wind blowing between 200 and 800 m above the ground. The key points of this technology are described and the design of large scale plants is investigated, in order to show that it has the potential to overcome the limits of the actual wind turbines and to provide large quantities of renewable energy, with competitive cost with respect to fossil sources. Such claims are supported by the results obtained so far in the Kitenergy project, undergoing at Politecnico di Torino, Italy, including numerical simulations, prototype experiments, and wind data analyses.

Benefits and challenges of using smart meters for advancing residential water demand modeling and management

Over the last two decades, water smart metering programs have been launched in a number of medium to large cities worldwide to nearly continuously monitor water consumption at the single household level. The availability of data at such very high spatial and temporal resolution advanced the ability in characterizing, modeling, and, ultimately, designing user-oriented residential water demand management strategies. Research to date has been focusing on one or more of these aspects but with limited integration between the specialized methodologies developed so far. This manuscript is the first comprehensive review of the literature in this quickly evolving water research domain. The paper contributes a general framework for the classification of residential water demand modeling studies, which allows revising consolidated approaches, describing emerging trends, and identifying potential future developments. In particular, the future challenges posed by growing population demands, constrained sources of water supply and climate change impacts are expected to require more and more integrated procedures for effectively supporting residential water demand modeling and management in several countries across the world. We review high resolution residential water demand modeling studies.We provide a classification of existing technologies and methodologies.We identify current trends, challenges and opportunities for future development.

Set-Membership Error-in-Variables Identification Through Convex Relaxation Techniques

In this technical note, the set membership error-in-variables identification problem is considered, that is the identification of linear dynamic systems when both output and input measurements are corrupted by bounded noise. A new approach for the computation of parameter uncertainty intervals is presented. First, the identification problem is formulated in terms of nonconvex optimization. Then, relaxation techniques based on linear matrix inequalities are employed to evaluate parameter bounds by means of convex optimization. The inherent structured sparsity of the original identification problems is exploited to reduce the computational complexity of the relaxed problems. Finally, convergence properties and complexity of the proposed procedure are discussed. Advantages of the presented technique with respect to previously published results are discussed and shown by means of two simulated examples.

Brief paper: Enforcing stability constraints in set-membership identification of linear dynamic systems

In this paper, we consider the identification of linear systems, a priori known to be stable, from input-output data corrupted by bounded noise. By taking explicitly into account a priori information on system stability, a formal definition of the feasible parameter set for a stable linear system is provided. On the basis of a detailed analysis of the geometrical structure of the feasible set, convex relaxation techniques are presented to solve nonconvex optimization problems arising in the computation of parameter uncertainty intervals. Properties of the computed relaxed bounds are discussed. A simulated example is presented to show the effectiveness of the proposed technique.

Bounded error identification of Hammerstein systems through sparse polynomial optimization

In this paper we present a procedure for the evaluation of bounds on the parameters of Hammerstein systems, from output measurements affected by bounded errors. The identification problem is formulated in terms of polynomial optimization, and relaxation techniques, based on linear matrix inequalities, are proposed to evaluate parameter bounds by means of convex optimization. The structured sparsity of the formulated identification problem is exploited to reduce the computational complexity of the convex relaxed problem. Analysis of convergence properties and computational complexity is reported.

An instrumental least squares support vector machine for nonlinear system identification

Least-Squares Support Vector Machines (LS-SVMs), originating from Stochastic Learning theory, represent a promising approach to identify nonlinear systems via nonparametric es- timation of nonlinearities in a computationally and stochastically attractive way. However, application of LS-SVMs in the identification context is formulated as a linear regression aim- ing at the minimization of the l2 loss in terms of the prediction error. This formulation corresponds to a prejudice of an auto-regressive noise structure, which, especially in the non- linear context, is often found to be too restrictive in practical applications. In [1], a novel Instrumental Variable (IV) based estimation is integrated into the LS-SVM approach provid- ing, under minor conditions, a consistent identification of nonlinear systems in case of a noise modeling error. It is shown how the cost function of the LS-SVM is modified to achieve an IV-based solution. In this technical report, a detailed derivation of the results presented in Section 5.2 of [1] is given as a supplement material for interested readers.

Brief paper: Set-membership LPV model identification of vehicle lateral dynamics

Set-membership identification of a Linear Parameter Varying (LPV) model describing the vehicle lateral dynamics is addressed in the paper. The model structure, chosen as much as possible on the ground of physical insights into the vehicle lateral behavior, consists of two single-input single-output LPV models relating the steering angle to the yaw rate and to the sideslip angle. A set of experimental data obtained by performing a large number of maneuvers is used to identify the vehicle lateral dynamics model. Prior information on the error bounds on the output and the time-varying parameter measurements are taken into account. Comparison with other vehicle lateral dynamics models is discussed.

LPV system identification under noise corrupted scheduling and output signal observations

Most of the approaches available in the literature for the identification of Linear Parameter-Varying (LPV) systems rely on the assumption that only the measurements of the output signal are corrupted by the noise, while the observations of the scheduling variable are considered to be noise free. However, in practice, this turns out to be an unrealistic assumption in most of the cases, as the scheduling variable is often related to a measured signal and, thus, it is inherently affected by a measurement noise. In this paper, it is shown that neglecting the noise on the scheduling signal, which corresponds to an error-in-variables problem, can lead to a significant bias on the estimated parameters. Consequently, in order to overcome this corruptive phenomenon affecting practical use of data-driven LPV modeling, we present an identification scheme to compute a consistent estimate of LPV Input/Output (IO) models from noisy output and scheduling signal observations. A simulation example is provided to prove the effectiveness of the proposed methodology.

Set-membership EIV identification through LMI relaxation techniques

In this paper the Set-membership Error-In-Variables (EIV) identification problem is considered, that is the identification of linear dynamic systems when both the output and the input measurements are corrupted by bounded noise. A new approach for the computation of the Parameters Uncertainty Intervals (PUIs) is discussed. First the problem is formulated in terms of non-convex semi-algebraic optimization. Then, a Linear-Matrix-Inequalities relaxation technique is presented to compute parameters bounds by means of convex optimization. Finally, convergence properties and computational complexity of the given algorithms are discussed. Advantages of the proposed technique with respect to previously published ones are discussed both theoretically and by means of a simulated example.

Fixed-order FIR approximation of linear systems from quantized input and output data

The problem of identifying a fixed-order FIR approximation of linear systems with unknown structure, assuming that both input and output measurements are subjected to quantization, is dealt with in this paper. A fixed-order FIR model providing the best approximation of the input-output relationship is sought by minimizing the worst-case distance between the output of the true system and the modeled output, for all possible values of the input and output data consistent with their quantized measurements. The considered problem is firstly formulated in terms of robust optimization. Then, two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques are presented. The effectiveness of the proposed approach is illustrated by means of a simulation example.