Abstract
We propose a new model predictive control (MPC) framework to generate feedback controls for time-varying nonlinear systems with input constraints. We provide a set of conditions on the design parameters that permits to verify a priori the stabilizing properties of the control strategies considered. The supplied sufficient conditions for stability can also be used to analyse the stability of most previous MPC schemes. The class of nonlinear systems addressed is significantly enlarged by removing the traditional assumptions on the continuity of the optimal controls and on the stabilizability of the linearized system. Some important classes of nonlinear systems, including some nonholonomic systems, can now be stabilized by MPC. In addition, we can exploit increased flexibility in the choice of design parameters to reduce the constraints of the optimal control problem, and thereby reduce the computational effort in the optimization algorithms used to implement MPC.

Abstract
Voltage stability is an important concern of power system managers not only in the net planning phase but also in operation. This issue has become especially critical in recent years due to the deregulation phenomenon because of new exploration policies complying a system operation closer to its security limits. In particular, voltage collapse distance may approach emergency values or, in the worst case, make the system collapse. As voltage profile is extremely dependent on reactive power compensation, most common approaches integrate both objectives in the operation setting phase, trying to optimize reactive power production taking voltage profile into consideration. In this paper, authors propose an evolutionary approach application to the same problem but in the planning phase. It is shown that the cooperative procedure of planning and preventive control provides better solutions that if one deals with these issues one at a time. © 2001 IEEE.

Abstract
This work presents an on-chip silicon bulk-micromachined Soil Moisture Sensor (SMS) suited for irrigation control and management applications. The same basic fabrication concepts and materials, which made microelectronics successful, are now being adapted to making low-cost, small, high-performance sensor systems with integrated electronics on the same chip. As a result, this system-on-a-chip includes the SMS, readout electronics, self-test, calibration facilities and a digital bus interface for external data transmission, Moreover, since this sensor has low-cost, it could be employed several sensors networked together with the 1-wire bus, to achieve an accurate measure of the soil moisture at the plant root level. A heat-pulse technique is used (for measuring the maximum temperature on a distant point) to determine the volumetric heat capacity and hence the water content of a porous media, such as soil. This method is based on the Joule effect (heater probe shank) and in Seebeck effect (thermopile - temperature probe shank). The heater and the thermopile are suspended on a dielectric window to reduce undesired heat conduction to the substrate (silicon is a good heat conductor). Thermal simulations of the bulk-micromachined SMS are performed to test sensor performance. In order to validate the method, simulations are made and experimental results were achieved with a macrosensor based on this technique. The results were compared with the measurements performed by the conventional thermo-gravimetric method.

Abstract
This paper deals with a problem of identification of the best subset of variables that should be used for dynamic security assessment of a power system, when this task is pro-vided by artificial neural networks (ANN)- The approach de-scribed here exploits ANN output sensitivities relatively to the inputs and correlation degrees, to identify the most relevant system variables to be used for an effective security assessment task. The ANNs are initially trained with all low-correlated candidate features, which enables the sensitivity analyses for the initial set of system features. Derivatives of the ANN output relatively to each input are obtained by exploiting the chain rule, similar to the one used for weights adaptation on Back-propagation Algorithm. A description of the application of this approach in a real system is present in the paper. Results obtained in the dynamic security assessment problem of the network of the island of Crete were quite successful. © 2001 IEEE.

Abstract
New first-order necessary conditions of optimality for control problems with state constraints are provided. These conditions are a variant of the nonsmooth maximum principle in which an Euler-Lagrange inclusion is involved. The main novelty of the result is precisely the ability to address state constraints, generalizing a known Euler-Lagrange inclusion for optimal control problems. The conditions developed are, in some cases, stronger than the standard maximum principle, since they can reduce the set of candidates to minimizers. © 2001 EUCA.

Abstract
Feature subset selection is a central issue in a vast diversity of problems including classification, function approximation, machine learning and adaptive control. On a wide variety of applications, especially when using real data, input features may be not independent and output variable depends on the relationship among inputs rather than on input values themselves. Feature selection methods that assume independence of attributes will fail on these cases. On the other side, most of alternative approaches are quasi-exhaustive, requiring large CPU processing time. In this paper, an alternative methodology based on sensitivity analysis of trained artificial neural networks (ANN) is analyzed. Results so far attained on illustrative toy examples and on real data support the validity of the developed approach.