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Prodan I.,CNRS System Conception and Integration Laboratory | Stoican F.,UPB | Zio E.,Electricite de France
IFAC-PapersOnLine | Year: 2015

This paper addresses the microgrid energy management problem within a coherent framework of control tools based on Mixed-Integer Linear Programming (MILP) and constrained Model Predictive Control (MPC). These help characterize the microgrid components' dynamics and the overall system control architecture. A fault tolerant strategy is considered in order to ensure the proper amount of energy in the storage devices such that (together with the utility grid) the essential consumer demand is reliably covered. Simulation results on a particular microgrid architecture validate the proposed approach. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Source


Almasi A.-D.,IBM | Wozniak S.,IBM | Wozniak S.,Ecole Polytechnique Federale de Lausanne | Cristea V.,UPB | And 2 more authors.
Neurocomputing | Year: 2016

This review provides a high-level synthesis of significant recent advances in artificial neural network research, as well as multi-disciplinary concepts connected to the far-reaching goal of obtaining intelligent systems. We assume that a global outlook of these interconnected fields can benefit researchers by providing alternative viewpoints. Therefore, we present different network and neuron models, we discuss model parameters and the means to obtain them, and we draw a quick outline of information encoding, before proceeding to an overview of the relevant learning mechanisms, ranging from established approaches to novel ideas. We specifically focus on comparing the classical artificial model with the biologically-feasible spiking neuron, and we take this comparison further into a discussion on the biological plausibility of various learning approaches. © 2015 . Source


Prodan I.,CNRS System Conception and Integration Laboratory | Zio E.,Electricite de France | Zio E.,Polytechnic of Milan | Stoican F.,UPB
Energy | Year: 2015

This paper presents an extension of a MPC (Model Predictive Control) approach for microgrid energy management which takes into account electricity costs, power consumption, generation profiles, power and energy constraints as well as uncertainty due to variations in the environment. The approach is based on a coherent framework of control tools, like mixed-integer programming and soft constrained MPC, for describing the microgrid components dynamics and the overall system control architecture. Fault tolerant strategies are inserted in order to ensure the proper amount of energy in the storage devices such that (together with the utility grid) the essential consumer demand is always covered. Simulation results on a particular microgrid architecture validate the proposed approach. © 2015 Elsevier Ltd. Source


Gavrus A.,CNRS Civil and Mechanical Engineering Laboratory | Bucur F.,ATM | Rotariu A.,ATM | Cananau S.,UPB
International Journal of Material Forming | Year: 2015

For non-conventional or rapid forming processes important gradients of the plastic strain and strain rate can be reached, especially for severe loadings or complex deformation paths applied to the material. It is then required to improve the accuracy of the experimental data generally obtained from high speed mechanical tests as the Split Hopkinson Pressure Bar ones (SHPB). In the same time it is necessary to define quantitatively reliable rheological constitutive equations and adequate values of corresponding material coefficients. This paper proposes to use a Finite Element model for the simulation of the entire SHPB experiment based on a new calibration method of the raw measurements, together with some applications concerning the use of special specimen shapes in order to obtain large ranges variation of plastic strains and strain rate values. A description of an inverse analysis strategy, applied in order to identify the thermo-mechanical behavior laws of the materials and the computation of the corresponding rheological parameters, is also presented. © 2014, Springer-Verlag France. Source


Stankovic N.,University Paris - Sud | Stoican F.,UPB | Olaru S.,University Paris - Sud | Niculescu S.-I.,University Paris - Sud
International Journal of Adaptive Control and Signal Processing | Year: 2016

In this paper, we consider a multi-sensor networked control configuration with linear plant which is affected by a bounded additive disturbance. Shared network is used for the communication between sensors and controller. It is assumed that the sensors are prone to abrupt faults, while the controller's input may be updated with a varying time-delay. In order to identify and isolate the sensor(s) providing faulty information, we equip the controller with a set-based detection and isolation routine. Furthermore, in the case when the network induces time-delays, control is performed based on the knowledge we have on the mathematical model of the plant. In the presence of model inaccuracies or disturbance, such a control action may not guarantee satisfying performance of the system. Therefore, a stabilising controller with delay compensation has been designed. The functioning of the proposed control algorithm has been illustrated through an example. © Copyright 2015 John Wiley & Sons, Ltd. Source

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