Andrea Giantomassi

Learn More
This paper presents a robust discrete-time sliding mode control coupled with an uncertainty estimator designed for planar robotic manipulators. Experimental evidence shows satisfactory trajectory tracking performances and noticeable robustness in the presence of model inaccuracies, disturbances and payload perturbations. Ultimate boundedness of the tracking(More)
This paper presents a discrete-time sliding mode control based on neural networks designed for robotic manipulators. Radial basis function neural networks are used to learn about uncertainties affecting the system. The online learning algorithm combines the growing criterion and the pruning strategy of the minimal resource allocating network technique with(More)
Nowadays there is a strong competition between companies in order to develop new solutions to keep competitive on the market, e.g. by reducing the production costs. Since the maintenance costs are substantial share of the production costs, companies need to plan the maintenance costs effectively. Deterioration prognosis of electric machine can decrease the(More)
In the contest of household energy management, a growing interest is addressed to smart system development, able to monitor and manage resources in order to minimize wasting. One of the key factors in curbing energy consumption in the household sector is the amendment of occupant erroneous behaviours and systems malfunctioning, due to the lack of awareness(More)
residential microgrid monitoring system based on kernel canonical variate analysis, Neurocomputing, This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley(More)