Stabilizing controller design for uncertain nonlinear systems using fuzzy models

A Lyapunov-based stabilizing control design method for uncertain nonlinear dynamical systems using fuzzy models is proposed. The controller is constructed using a design model of the dynamical process to be controlled. The design model is obtained from the truth model using a fuzzy modeling approach. The truth model represents a detailed description of the process dynamics. The truth model is used in a simulation experiment to evaluate the performance of the controller design. A method for generating local models that constitute the design model is proposed. Sufficient conditions for stability and stabilizability of fuzzy models using fuzzy state-feedback controllers are given. The results obtained are illustrated with a numerical example involving a four-dimensional nonlinear model of a stick balancer.

On relaxed LMI-based design for fuzzy controllers

Relaxed conditions for stability of nonlinear continuous-time systems given by fuzzy models axe presented. A theoretical analysis shows that the proposed method provides better or at least the same results of the methods presented in the literature. Digital simulations exemplify this fact. This result is also used for fuzzy regulators design. The nonlinear systems are represented by fuzzy models proposed by Takagi and Sugeno. The stability analysis and the design of controllers axe described by LMIs (Linear Matrix Inequalities), that can be solved efficiently using convex programming techniques.

On relaxed LMI-based designs for fuzzy regulators and fuzzy observers

Relaxed conditions for stability of nonlinear, continuous and discrete-time systems given by fuzzy models are presented. A theoretical analysis shows that the proposed methods provide better or at least the same results of the methods presented in the literature. Numerical results exemplify this fact. These results are also used for fuzzy regulators and observers designs. The nonlinear systems are represented by fuzzy models proposed by Takagi and Sugeno. The stability analysis and the design of controllers are described by linear matrix inequalities, that can be solved efficiently using convex programming techniques. The specification of the decay rate, constrains on control input and output are also discussed.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Design of fuzzy regulators with optimal initial conditions compensation

In almost all cases, the goal of the design of automatic control systems is to obtain the parameters of the controllers, which are described by differential equations. In general, the controller is artificially built and it is possible to update its initial conditions. In the design of optimal quadratic regulators, the initial conditions of the controller can be changed in an optimal way and they can improve the performance of the controlled system. Following this idea, a LNU-based design procedure to update the initial conditions of PI controllers, considering the nonlinear plant described by Takagi-Sugeno fuzzy models, is presented. The importance of the proposed method is that it also allows other specifications, such as, the decay rate and constraints on control input and output. The application in the control of an inverted pendulum illustrates the effectively of proposed method.

Controle ótimo H∞ de sistemas não-lineares com modelos fuzzy takagi-sugeno

Neste trabalho é proposta uma metodologia de rastreamento de sinais e rejeição de distúrbios aplicada a sistemas não-lineares. Para o projeto do sistema de rastreamento, projeta-se os controladores fuzzy M(a) e N(a) que minimizam o limitante superior da norma H∞ entre o sinal de referência r(t) e o sinal de erro de rastreamento e(t), sendo e(t) a diferença entre a entrada de referência e a saída do sistema z(t). No método de rejeição de distúrbio utiliza-se a realimentação dinâmica da saída através de um controlador fuzzy Kc(a) que minimiza o limitante superior da norma H∞ entre o sinal de entrada exógena w(t) e o sinal de saída z(t). O procedimento de projeto proposto considera as não-linearidades da planta através dos modelos fuzzy Takagi-Sugeno. Os métodos são equacionados utilizando-se inequações matriciais lineares (LMIs), que quando factíveis, podem ser facilmente solucionados por algoritmos de convergência polinomial. Por fim, um exemplo ilustra a viabilidade da metodologia proposta.

Detection and classification of voltage disturbances using a Fuzzy-ARTMAP-wavelet network

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

Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

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

Design of a Takagi-Sugeno Fuzzy Regulator for a Set of Operation Points

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Weight and neuro-psycho-motor development in children discharged from neonatal intensive care unit

Background: Low birth weight affects child growth and development, requiring the intensive use of health services. There are conversely proportional associations between prematurity and academic performance around the world. In this study we evaluated factors involved in weight and neuropsychomotor profile in one and two years old discharged from Intensive Care Units (ICU).Methods/Design: We investigated 203 children from the ICU who were followed for 24 +/- 4 months. The research was conducted by collecting data from medical records of patients in a Follow-up program. We investigated the following variables: inadequate weight at one year old; inadequate weight at two years old and a severe neurological disorder at two years old.Results: We observed increase of almost 20% in the proportion of children which weighted between the 10th and 90th percentiles and decrease of around 40% of children below the 15th percentile, from one to two years old. In almost 60% of the cases neuropsychomotor development was normal at 2 years old, less than 15% of children presented abnormal development. Variables that remained influential for clinical outcome at 1 and 2 years old were related to birth weight and gestational age, except for hypoglycemia. Neurological examination was the most influential variable for severe neurological disturbance.Conclusion: Hypoglycemia was considered a new fact to explain inadequate weight. The results, new in Brazil and difficult in terms of comparison, could be used to identify risk factors and for a better approach of newborn discharged from ICUs.

Ação neuro-muscular do veneno crotálico: dados preliminares

Estudamos 6 pacientes, 2 cães e um coelho com intoxicação crotálica. Avaliamos a condução nervosa periférica sensitiva e motora, a transmissão neuromuscular e eletromiografias. As biópsias de músculo foram processadas por histoquímica. Os 6 pacientes apresentaram mononeuropatia sensitiva no nervo periférico adjacente ao local da inoculação do veneno e encontramos evidências histoquímicas de miopatia mitocondrial. Os defeitos da transmissão neuromuscular foram mínimos. A maioria dos autores admite que veneno crotálico determina síndrome miastênica. Nossos achados indicam que ptose palpebral, facies miastênico e fraqueza muscular observados após acidente crotálico, correspondem provavelmente a miopatia mitocondrial, muitas vezes transitória e reversível.

Genética e linguagem na síndrome de Williams-Beuren: uma condição neuro-cognitiva peculiar

TEMA: aspectos genéticos, cognitivos e de linguagem na Síndrome de Williams-Beuren (SWB). OBJETIVO: revisar a literatura sobre a SWB, destacando aspectos genéticos, cognitivos e de linguagem. CONCLUSÕES: a literatura mostrou que a etiologia da SWB é conhecida, embora o diagnóstico precoce pode ser difícil pela variabilidade de manifestações clínicas dessa condição. O fenótipo variável tem sido atribuído a deleção de vários genes na região 7q11.23. que inclui o gene da elastina. A deleção desse gene é identificada pelo estudo citogenético molecular denominado Hibridização in situ por Fluorescência (FISH). A freqüência populacional desta síndrome é de 1 em 20,000 nascimentos e é resultante de uma alteração genética de novo. O quadro da SWB é caracterizado principalmente por fácies típica conhecida como face de duende, alterações cardíacas, prejuízos cognitivos e aspectos comportamentais que incluem a linguagem. A característica falante e sociável associada as dificuldades viso-construtivas conferem a esta síndrome um quadro neuro-cognitivo peculiar. A deficiência mental é variável e pode ou não estar presente. Estudos que descreveram as habilidades de linguagem nesta síndrome destacaram que a habilidade sintática pode estar íntegra ou parcialmente íntegra, a produção verbal pode ser precisa e inteligível, mostrando a integridade do sistema fonológico. O vocabulário receptivo-auditivo é citado em alguns estudos como adequado e em outros como prejudicado para a idade mental. Pesquisas na área têm produzido, resultados incongruentes com respeito ao perfil de habilidades cognitivas e lingüísticas nos portadores dessa condição. A correlação entre as habilidades de linguagem e a cognição e a divergência de achados na literatura serão abordadas neste artigo.

Fuzzy subset approach in coupled population dynamics of blowflies

This paper is a study on the population dynamics of blowflies employing a density-dependent. non-linear mathematical model and a coupled population formalism. In this Study, we investigated the coupled population dynamics applying fuzzy subsets to model the Population trajectory. analyzing demographic parameters such as fecundity, Survival, and migration. The main results suggest different possibilities in terms of dynamic behavior produced by migration in coupled Populations between distinct environments and the rescue effect generated by the connection between populations. It was possible to conclude that environmental heterogeneity can play an important role in blowfly metapopulation systems. The implications of these results for population dynamics of blowflies are discussed.