Search results for "Theory"
showing 10 items of 24627 documents
Sensorless Speed Control for Double-Sided Linear Induction Motor Applications
2019
In this work, a flux and speed observer for double-sided linear induction motor applications is presented and experimentally validated. More in detail, from a Double-Sided Linear Induction Motor (DLIM) prototype, the complete modelling and the determination of the related parameters are here reported. Furthermore, the equations for a d-flux and q-flux observer are conceived and several simulation tests are performed. From the good agreements between the trends over time of the speed estimated by the observer and the simulated one, it can be stated that the observer is well designed. Moreover, in order to experimentally validate the proposed observer, a test bench is set-up for the DLIM/obse…
Consensus via multi-population robust mean-field games
2017
In less prescriptive environments where individuals are told ‘what to do’\ud but not ‘how to do’, synchronization can be a byproduct of strategic thinking,\ud prediction, and local interactions. We prove this in the context of multipopulation\ud robust mean-field games. The model sheds light on a multi-scale\ud phenomenon involving fast synchronization within the same population and\ud slow inter-cluster oscillation between different populations.
Wind component estimation for UAS flying in turbulent air
2019
One of the most important problem of autonomous flight for UAS is the wind identification, especially for small scale vehicles. This research focusses on an identification methodology based on the Extended Kalman Filter (EKF). In particular authors focus their attention on.the filter tuning problem. The proposed procedure requires low computational power, so it is very useful for UAS. Besides it allows a robust wind component identification even when, as it is usually, the measurement data set is affected by noticeable noises. (C) 2019 Elsevier Masson SAS. All rights reserved.
Adjusted bat algorithm for tuning of support vector machine parameters
2016
Support vector machines are powerful and often used technique of supervised learning applied to classification. Quality of the constructed classifier can be improved by appropriate selection of the learning parameters. These parameters are often tuned using grid search with relatively large step. This optimization process can be done computationally more efficiently and more precisely using stochastic search metaheuristics. In this paper we propose adjusted bat algorithm for support vector machines parameter optimization and show that compared to the grid search it leads to a better classifier. We tested our approach on standard set of benchmark data sets from UCI machine learning repositor…
Algebraic parameter estimation of a multi-sinusoidal waveform signal from noisy data
2013
International audience; In this paper, we apply an algebraic method to estimate the amplitudes, phases and frequencies of a biased and noisy sum of complex exponential sinusoidal signals. Let us stress that the obtained estimates are integrals of the noisy measured signal: these integrals act as time-varying filters. Compared to usual approaches, our algebraic method provides a more robust estimation of these parameters within a fraction of the signal's period. We provide some computer simulations to demonstrate the efficiency of our method.
Algebraic parameter estimation of a biased sinusoidal waveform signal from noisy data
2012
International audience; The amplitude, frequency and phase of a biased and noisy sum of two complex exponential sinusoidal signals are estimated via new algebraic techniques providing a robust estimation within a fraction of the signal period. The methods that are popular today do not seem able to achieve such performances. The efficiency of our approach is illustrated by several computer simulations.
Identification of Objects Based on Generalized Amplitude-Phase Images Statistical Models
2017
The article presents the dynamical objects identification technology based on statistical models of amplitude-phase images (APIm) – multidimensional data arrays (semantic models) and statistical correlation analysis methods using the generalized discrete Hilbert transforms (DHT) – 2D Hilbert (Foucault) isotropic (HTI), anisotropic (HTA) and total transforms – AP-analysis (APA) to calculate the APIm. The identified objects are modeled with 3D airplanes templates rotated in space around the center of Cartesian coordinate system. The DHT domain system of coordinates displaying the plane projections (2D flat images) remains to be space-invariant. That causes the anisotropic properties of APIm a…
Development Overview of a Smart Customizable Product
2018
Abstract This paper presents the development phases of a smart product that can be manufactured on a cyber physical production system. The intelligent product’s requirements are defined from the manufacturing perspective and then converted into technical product characteristics. Finally, we refined the technical characteristics to create a functional tablet with customizable hardware functionalities as a customizable product.
Modeling Energy Demand Aggregators for Residential Consumers
2013
International audience; Energy demand aggregators are new actors in the energy scenario: they gather a group of energy consumers and implement a demand- response paradigm. When the energy provider needs to reduce the current energy demand on the grid, it can pay the energy demand aggregator to reduce the load by turning off some of its consumers loads or postponing their activation. Currently this operation involves only greedy energy consumers like industrial plants. In this paper we want to study the potential of aggregating a large number of small energy consumers like home users as it may happen in smart grids. In particular we want to address the feasibility of such approach by conside…
Robust Mean Field Games with Application to Production of an Exhaustible Resource
2012
International audience; In this paper, we study mean field games under uncertainty. We consider a population of players with individual states driven by a standard Brownian motion and a disturbance term. The contribution is three-fold: First, we establish a mean field system for such robust games. Second, we apply the methodology to an exhaustible resource production. Third, we show that the dimension of the mean field system can be significantly reduced by considering a functional of the first moment of the mean field process.