Identificação fuzzy-multimodelos para sistemas não lineares

This paper presents a new multi-model technique of dentification in ANFIS for nonlinear systems. In this technique, the structure used is of the fuzzy Takagi-Sugeno of which the consequences are local linear models that represent the system of different points of operation and the precursors are membership functions whose adjustments are realized by the learning phase of the neuro-fuzzy ANFIS technique. The models that represent the system at different points of the operation can be found with linearization techniques like, for example, the Least Squares method that is robust against sounds and of simple application. The fuzzy system is responsible for informing the proportion of each model that should be utilized, using the membership functions. The membership functions can be adjusted by ANFIS with the use of neural network algorithms, like the back propagation error type, in such a way that the models found for each area are correctly interpolated and define an action of each model for possible entries into the system. In multi-models, the definition of action of models is known as metrics and, since this paper is based on ANFIS, it shall be denominated in ANFIS metrics. This way, ANFIS metrics is utilized to interpolate various models, composing a system to be identified. Differing from the traditional ANFIS, the created technique necessarily represents the system in various well defined regions by unaltered models whose pondered activation as per the membership functions. The selection of regions for the application of the Least Squares method is realized manually from the graphic analysis of the system behavior or from the physical characteristics of the plant. This selection serves as a base to initiate the linear model defining technique and generating the initial configuration of the membership functions. The experiments are conducted in a teaching tank, with multiple sections, designed and created to show the characteristics of the technique. The results from this tank illustrate the performance reached by the technique in task of identifying, utilizing configurations of ANFIS, comparing the developed technique with various models of simple metrics and comparing with the NNARX technique, also adapted to identification

Identificação não linear usando uma rede fuzzy wavelet neural network modificada

In last decades, neural networks have been established as a major tool for the identification of nonlinear systems. Among the various types of networks used in identification, one that can be highlighted is the wavelet neural network (WNN). This network combines the characteristics of wavelet multiresolution theory with learning ability and generalization of neural networks usually, providing more accurate models than those ones obtained by traditional networks. An extension of WNN networks is to combine the neuro-fuzzy ANFIS (Adaptive Network Based Fuzzy Inference System) structure with wavelets, leading to generate the Fuzzy Wavelet Neural Network - FWNN structure. This network is very similar to ANFIS networks, with the difference that traditional polynomials present in consequent of this network are replaced by WNN networks. This paper proposes the identification of nonlinear dynamical systems from a network FWNN modified. In the proposed structure, functions only wavelets are used in the consequent. Thus, it is possible to obtain a simplification of the structure, reducing the number of adjustable parameters of the network. To evaluate the performance of network FWNN with this modification, an analysis of network performance is made, verifying advantages, disadvantages and cost effectiveness when compared to other existing FWNN structures in literature. The evaluations are carried out via the identification of two simulated systems traditionally found in the literature and a real nonlinear system, consisting of a nonlinear multi section tank. Finally, the network is used to infer values of temperature and humidity inside of a neonatal incubator. The execution of such analyzes is based on various criteria, like: mean squared error, number of training epochs, number of adjustable parameters, the variation of the mean square error, among others. The results found show the generalization ability of the modified structure, despite the simplification performed

Identificação de uma planta de corrente de um motor de indução utilizando redes de base radial

The present work describes the use of a mathematical tool to solve problems arising from control theory, including the identification, analysis of the phase portrait and stability, as well as the temporal evolution of the plant s current induction motor. The system identification is an area of mathematical modeling that has as its objective the study of techniques which can determine a dynamic model in representing a real system. The tool used in the identification and analysis of nonlinear dynamical system is the Radial Basis Function (RBF). The process or plant that is used has a mathematical model unknown, but belongs to a particular class that contains an internal dynamics that can be modeled.Will be presented as contributions to the analysis of asymptotic stability of the RBF. The identification using radial basis function is demonstrated through computer simulations from a real data set obtained from the plant

Obtenção dos parâmetros-x de estruturas planares

Due to major progress of communication system in the last decades, need for more precise characterization of used components. The S-parameters modeling has been used to characterization, simulation and test of communication system. However, limitation of S-parameters to model nonlinear system has created new modeling systems that include the nonlinear characteristics. The polyharmonic distortion modeling is a characterizationg technique for nonlinear systems that has been growing up due to praticity and similarity with S-parameters. This work presents analysis the polyharmonic distortion modeling, the test bench development for simulation of planar structure and planar structure characterization with X-parameters

Controlador preditivo não linear aplicado ao controle de golfadas em processos de produção de petróleo

Slugging is a well-known slugging phenomenon in multiphase flow, which may cause problems such as vibration in pipeline and high liquid level in the separator. It can be classified according to the place of its occurrence. The most severe, known as slugging in the riser, occurs in the vertical pipe which feeds the platform. Also known as severe slugging, it is capable of causing severe pressure fluctuations in the flow of the process, excessive vibration, flooding in separator tanks, limited production, nonscheduled stop of production, among other negative aspects that motivated the production of this work . A feasible solution to deal with this problem would be to design an effective method for the removal or reduction of the system, a controller. According to the literature, a conventional PID controller did not produce good results due to the high degree of nonlinearity of the process, fueling the development of advanced control techniques. Among these, the model predictive controller (MPC), where the control action results from the solution of an optimization problem, it is robust, can incorporate physical and /or security constraints. The objective of this work is to apply a non-conventional non-linear model predictive control technique to severe slugging, where the amount of liquid mass in the riser is controlled by the production valve and, indirectly, the oscillation of flow and pressure is suppressed, while looking for environmental and economic benefits. The proposed strategy is based on the use of the model linear approximations and repeatedly solving of a quadratic optimization problem, providing solutions that improve at each iteration. In the event where the convergence of this algorithm is satisfied, the predicted values of the process variables are the same as to those obtained by the original nonlinear model, ensuring that the constraints are satisfied for them along the prediction horizon. A mathematical model recently published in the literature, capable of representing characteristics of severe slugging in a real oil well, is used both for simulation and for the project of the proposed controller, whose performance is compared to a linear MPC

Um estudo na teoria do movimento brownianno com viscosidade variável

In this work we investigate the stochastic behavior of a large class of systems with variable damping which are described by a time-dependent Lagrangian. Our stochastic approach is based on the Langevin treatment describing the motion of a classical Brownian particle of mass m. Two situations of physical interest are considered. In the first one, we discuss in detail an application of the standard Langevin treatment (white noise) for the variable damping system. In the second one, a more general viewpoint is adopted by assuming a given expression to the so-called collored noise. For both cases, the basic diffententiaql equations are analytically solved and al the quantities physically relevant are explicitly determined. The results depend on an arbitrary q parameter measuring how the behavior of the system departs from the standard brownian particle with constant viscosity. Several types of sthocastic behavior (superdiffusive and subdiffusive) are obteinded when the free pamameter varies continuosly. However, all the results of the conventional Langevin approach with constant damping are recovered in the limit q = 1

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