860 resultados para Controller
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Pós-graduação em Engenharia Elétrica - FEIS
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Pós-graduação em Engenharia Mecânica - FEIS
Métodos de identificação e redução de modelos para atenuação de vibrações em estruturas inteligentes
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Pós-graduação em Engenharia Mecânica - FEIS
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Pós-graduação em Engenharia Mecânica - FEIS
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Pós-graduação em Engenharia Elétrica - FEIS
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Pós-graduação em Engenharia Elétrica - FEIS
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Pós-graduação em Zootecnia - FMVZ
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Pós-graduação em Engenharia Elétrica - FEIS
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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The main objective of this work is to illustrate an application of angular active control in a sectioned airfoil using shape memory alloys. In the proposed model, one wants to establish the shape of the airfoil profile based on the determination of an angle between its two sections. This angle is obtained by the effect of the shape memory of the alloy by passing an electric current that modifies the temperature of the wire through the Joule effect, changing the shape of the alloy. This material is capable of converting thermal energy into mechanical energy and once permanently deformed, the material can return to its original shape by heating. Due to the presence of nonlinear effects, especially in the mathematical model of the alloy, this work proposes the application of a control system based on fuzzy logic. Through numerical tests, the performance of the fuzzy controller is compared with an on-off controller applied in a sectioned airfoil model.
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In this work a Nonzero-Sum NASH game related to the H2 and H∞ control problems is formulated in the context of convex optimization theory. The variables of the game are limiting bounds for the H2 and H∞ norms, and the final controller is obtained as an equilibrium solution, which minimizes the `sensitivity of each norm' with respect to the other. The state feedback problem is considered and illustrated by numerical examples.
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Neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of recurrent neural networks for solving the N-Queens problem. More specifically, a modified Hopfield network is developed and its internal parameters are explicitly computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points, which represent a solution of the considered problem. The network is shown to be completely stable and globally convergent to the solutions of the N-Queens problem. A fuzzy logic controller is also incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.
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Pós-graduação em Engenharia Elétrica - FEIS
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Pós-graduação em Engenharia Elétrica - FEB
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Pós-graduação em Engenharia Elétrica - FEIS