H-2 and H-infinity - norm control of intelligent structures using LMI techniques

Linear Matrix Inequalities (LMIs) is a powerful too] that has been used in many areas ranging from control engineering to system identification and structural design. There are many factors that make LMI appealing. One is the fact that a lot of design specifications and constrains can be formulated as LMIs [1]. Once formulated in terms of LMIs a problem can be solved efficiently by convex optimization algorithms. The basic idea of the LMI method is to formulate a given problem as an optimization problem with linear objective function and linear matrix inequalities constrains. An intelligent structure involves distributed sensors and actuators and a control law to apply localized actions, in order to minimize or reduce the response at selected conditions. The objective of this work is to implement techniques of control based on LMIs applied to smart structures.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Estimation of electrical machine speed using sensorless technology and neural networks

The use of sensorless technologies is an increasing tendency on industrial drivers for electrical machines. The estimation of electrical and mechanical parameters involved with the electrical machine control is used very frequently in order to avoid measurement of all variables related to this process. The cost reduction may also be considered in industrial drivers, besides the increasing robustness of the system, as an advantage of the use of sensorless technologies. This work proposes the use of a recurrent artificial neural network to estimate the speed of induction motor for sensorless control schemes using one single current sensor. Simulation and experimental results are presented to validate the proposed approach. ©2008 IEEE.

Damage detection in nonlinear structures using discrete-time volterra series

Structural damage identification is basically a nonlinear phenomenon; however, nonlinear procedures are not used currently in practical applications due to the complexity and difficulty for implementation of such techniques. Therefore, the development of techniques that consider the nonlinear behavior of structures for damage detection is a research of major importance since nonlinear dynamical effects can be erroneously treated as damage in the structure by classical metrics. This paper proposes the discrete-time Volterra series for modeling the nonlinear convolution between the input and output signals in a benchmark nonlinear system. The prediction error of the model in an unknown structural condition is compared with the values of the reference structure in healthy condition for evaluating the method of damage detection. Since the Volterra series separate the response of the system in linear and nonlinear contributions, these indexes are used to show the importance of considering the nonlinear behavior of the structure. The paper concludes pointing out the main advantages and drawbacks of this damage detection methodology. © (2013) Trans Tech Publications.

Modelagem de séries de monitoramento de nível freático em área de afloramento do sistema aquífero guarani (sag) sob influência de diferentes usos agrícolas da terra

Pós-graduação em Agronomia (Irrigação e Drenagem) - FCA

Avaliação das propriedades mecânicas de fios de Níquel-Titânio após incorporação de dobras e/ou tratamento elétrico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação dos sistemas de redes viárias florestais em função dos custos e do risco de erosão

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Pesquisa-ação sobre o uso de indicadores de desempenho do equipamento em uma fábrica de geradores

In the industrial environment the challenge is use better the productive resources: people and machine. The following work has the main goal improve the efficient losses analysis in the stator bar’s production bottleneck equipment situated in the Electric generator’s factory. The action research involved Theory of Constraints on the restriction system identification and developed the data collection framework by losses typology for indicator measurement. The research showed the data collection standardization importance to obtain reliable data and strategic efficiency indicator to optimize equipments. Besides of this, OEE and TEEP indicator demonstrated efficiency results to analyze the actual efficiency when the machine works and the increase capacity opportunity to treat the hide costs in the organization following the continuous improvement

Métodos de identificação e redução de modelos para atenuação de vibrações em estruturas inteligentes

Pós-graduação em Engenharia Mecânica - FEIS

Controle ativo de estruturas reticuladas utilizando atuadores de pilha de pzt

Pós-graduação em Engenharia Mecânica - FEIS

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Neural Network-Based Approach for Identification of the Harmonic Content of a Nonlinear Load in a Single-Phase System

In this paper an alternative method based on artificial neural networks is presented to determine harmonic components in the load current of a single-phase electric power system with nonlinear loads, whose parameters can vary so much in reason of the loads characteristic behaviors as because of the human intervention. The first six components in the load current are determined using the information contained in the time-varying waveforms. The effectiveness of this method is verified by using it in a single-phase active power filter with selective compensation of the current drained by an AC controller. The proposed method is compared with the fast Fourier transform.

Candida spp.: manual identification (reference method) and automated identification (Vitek system platform)

Yeasts are becoming a common cause of nosocomial fungal infections that affect immunocompromised patients. Such infections can evolve into sepsis, whose mortality rate is high. This study aimed to evaluate the viability of Candida species identification by the automated system Vitek-Biomerieux (Durham, USA). Ninety-eight medical charts referencing the Candida spp. samples available for the study were retrospectively analyzed. The system Vitek-Biomerieux with Candida identification card is recommended for laboratory routine use and presents 80.6% agreement with the reference method. By separate analysis of species, 13.5% of C. parapsilosis samples differed from the reference method, while the Vitek system wrongly identified them as C. tropicalis, C. lusitaneae or as Candida albicans. C. glabrata presented a discrepancy of only one sample (25%), and was identified by Vitek as C. parapsilosis. C. guilliermondii also differed in only one sample (33.3%), being identified as Candida spp. All C. albicans, C. tropicalis and C. lusitaneae samples were identified correctly.

A fuzzy inference system for identification of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behavior. In this paper, a system based on fuzzy logic systems is developed to overcome the problems usually found in the conventional mathematical models. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the fuzzy approach. Simulation results are presented to justify the validity of the proposed approach.

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.