913 resultados para model reference adaptive control systems


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In this paper, we introduce a DAI approach called hereinafter Fuzzy Distributed Artificial Intelligence (FDAI). Through the use of fuzzy logic, we have been able to develop mechanisms that we feel may effectively improve current DAI systems, giving much more flexibility and providing the subsidies which a formal theory can bring. The appropriateness of the FDAI approach is explored in an important application, a fuzzy distributed traffic-light control system, where we have been able to aggregate and study several issues concerned with fuzzy and distributed artificial intelligence. We also present a number of current research directions necessary to develop the FDAI approach more fully.

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The task of controlling urban traffic requires flexibility, adaptability and handling uncertain information spread through the intersection network. The use of fuzzy sets concepts convey these characteristics to improve system performance. This paper reviews a distributed traffic control system built upon a fuzzy distributed architecture previously developed by the authors. The emphasis of the paper is on the application of the system to control part of Campinas downtown area. Simulation experiments considering several traffic scenarios were performed to verify the capabilities of the system in controlling a set of coupled intersections. The performance of the proposed system is compared with conventional traffic control strategies under the same scenarios. The results obtained show that the distributed traffic control system outperforms conventional systems as far as average queues, average delay and maximum delay measures are concerned.

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A Maximum Principle is derived for a class of optimal control problems arising in midcourse guidance, in which certain controls are represented by measures and, the state trajectories are functions of bounded variation. The optimality conditions improves on previous optimality conditions by allowing nonsmooth data, measurable time dependence, and a possibly time varying constraint set for the conventional controls.

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A new concept of fault detection and isolation using robust observation for systems with random noises is presented. The method selects the parameters from components that may fault during the process and constructs well conditioned robust observers, considering sensors faults. To isolate component failures via robust observation, a bank of detection observers is constructed, where each observer is only sensitive to one specified component failure while robust to all other component failures.

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We develop an economic model for X̄ control charts having all design parameters varying in an adaptive way, that is, in real time considering current sample information. In the proposed model, each of the design parameters can assume two values as a function of the most recent process information. The cost function is derived and it provides a device for optimal selection of the design parameters. Through a numerical example one can foresee the savings that the developed model possibly provides. © 2001 Elsevier Science B.V. All rights reserved.

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An overview is given on the possibility of controlling the status of circuit breakers (CB) in a substations with the use of a knowledge base that relates some of the operation magnitudes, mixing status variables with time variables and fuzzy sets. It is shown that even when all the magnitudes to be controlled cannot be included in the analysis, it is possible to control the desired status while supervising some important magnitudes as the voltage, power factor, and harmonic distortion, as well as the present status.

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This article addresses the problem of stability of impulsive control systems whose dynamics are given by measure driven differential inclusions. One important feature concerns the adopted solution which allows the consideration of systems whose singular dynamics do not satisfy the so-called Frobenius condition. After extending the conventional notion of control Lyapounov pair for impulsive systems, some stability conditions of the Lyapounov type are given. Some conclusions follow the outline of the proof of the main result.

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Grinding process is usually the last finishing process of a precision component in the manufacturing industries. This process is utilized for manufacturing parts of different materials, so it demands results such as low roughness, dimensional and shape error control, optimum tool-life, with minimum cost and time. Damages on the parts are very expensive since the previous processes and the grinding itself are useless when the part is damaged in this stage. This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 e VC131 steels. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. In each test AE data was analyzed off-line, with results compared to inspection of each workpiece for burn and other metallurgical anomaly. A number of statistical signal processing tools have been evaluated.

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This paper investigates both theoretically and experimentally the effect of the location and number of sensors and magnetic bearing actuators on both global and local vibration reduction along a rotor using a feedforward control scheme. Theoretical approaches developed for the active control of beams have been shown to be useful as simplified models for the rotor scenario. This paper also introduces the time-domain LMS feedforward control strategy, used widely in the active control of sound and vibration, as an alternative control methodology to the frequency-domain feedforward approaches commonly presented in the literature. Results are presented showing that for any case where the same number of actuators and error sensors are used there can be frequencies at which large increases in vibration away from the error sensors can occur. It is also shown that using a larger number of error sensors than actuators results in better global reduction of vibration but decreased local reduction. Overall, the study demonstrated that an analysis of actuator and sensor locations when feedforward control schemes are used is necessary to ensure that harmful increased vibrations do not occur at frequencies away from rotor-bearing natural frequencies or at points along the rotor not monitored by error sensors.

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This work aims at finding out the threshold to burning in surface grinding process. Acoustic emission and electric power signals are acquired from an analog-digital converter and processed through algorithms in order to generate a control signal to inform the operator or interrupt the process in the case of burning occurrence. The thresholds that dictate the situation of burn and non-burn were studied as well as a comparison between the two parameters was carried out. In the experimental work one type of steel (ABNT-1045 annealed) and one type of grinding wheel referred to as TARGA model 3TG80.3-NV were employed. Copyright © 2005 by ABCM.

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In this paper we use the Hermite-Biehler theorem to establish results for the design of proportional plus integral plus derivative (PID) controllers concerning a class of time delay systems. Using the property of interlacing at high frequencies of the class of systems considered and linear programming we obtain the set of all stabilizing PID controllers. © 2005 IEEE.

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Flutter is an in-flight vibration of flexible structures caused by energy in the airstream absorbed by the lifting surface. This aeroelastic phenomenon is a problem of considerable interest in the aeronautic industry, because flutter is a potentially destructive instability resulting from an interaction between aerodynamic, inertial, and elastic forces. To overcome this effect, it is possible to use passive or active methodologies, but passive control adds mass to the structure and it is, therefore, undesirable. Thus, in this paper, the goal is to use linear matrix inequalities (LMIs) techniques to design an active state-feedback control to suppress flutter. Due to unmeasurable aerodynamic-lag states, one needs to use a dynamic observer. So, LMIs also were applied to design a state-estimator. The simulated model, consists of a classical flat plate in a two-dimensional flow. Two regulators were designed, the first one is a non-robust design for parametric variation and the second one is a robust control design, both designed by using LMIs. The parametric uncertainties are modeled through polytopic uncertainties. The paper concludes with numerical simulations for each controller. The open-loop and closed-loop responses are also compared and the results show the flutter suppression. The perfomance for both controllers are compared and discussed. Copyright © 2006 by ABCM.

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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.

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Among the positioning systems that compose GNSS (Global Navigation Satellite System), GPS has the capability of providing low, medium and high precision positioning data. However, GPS observables may be subject to many different types of errors. These systematic errors can degrade the accuracy of the positioning provided by GPS. These errors are mainly related to GPS satellite orbits, multipath, and atmospheric effects. In order to mitigate these errors, a semiparametric model and the penalized least squares technique were employed in this study. This is similar to changing the stochastical model, in which error functions are incorporated and the results are similar to those in which the functional model is changed instead. Using this method, it was shown that ambiguities and the estimation of station coordinates were more reliable and accurate than when employing a conventional least squares methodology.

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In this paper, a mathematical model is derived via Lagrange's Equation for a shear building structure that acts as a foundation of a non-ideal direct current electric motor, controlled by a mass loose inside a circular carving. Non-ideal sources of vibrations of structures are those whose characteristics are coupled to the motion of the structure, not being a function of time only as in the ideal case. Thus, in this case, an additional equation of motion is written, related to the motor rotation, coupled to the equation describing the horizontal motion of the shear building. This kind of problem can lead to the so-called Sommerfeld effect: steady state frequencies of the motor will usually increase as more power (voltage) is given to it in a step-by-step fashion. When a resonance condition with the structure is reached, the better part of this energy is consumed to generate large amplitude vibrations of the foundation without sensible change of the motor frequency as before. If additional increase steps in voltage are made, one may reach a situation where the rotor will jump to higher rotation regimes, no steady states being stable in between. As a device of passive control of both large amplitude vibrations and the Sommerfeld effect, a scheme is proposed using a point mass free to bounce back and forth inside a circular carving in the suspended mass of the structure. Numerical simulations of the model are also presented Copyright © 2007 by ASME.