884 resultados para Closed-loop recycling


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The problem of signal tracking, in the presence of a disturbance signal in the plant, is solved using a zero-variation methodology. A state feedback controller is designed in order to minimise the H-2-norm of the closed-loop system, such that the effect of the disturbance is attenuated. Then, a state estimator is designed and the modification of the zeros is used to minimise the H-infinity-norm from the reference input signal to the error signal. The error is taken to be the difference between the reference and the output signals, thereby making it a tracking problem. The design is formulated in a linear matrix inequality framework, such that the optimal solution of the stated control problem is obtained. Practical examples illustrate the effectiveness of the proposed method.

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In this paper we use the Hermite-Biehler theorem to establish results for the design of fixed order controllers for a class of time delay systems. We extend results of the polynomial case to quasipolynomials using the property of interlacing in high frequencies of the class of time delay systems considered. (C) 2003 Elsevier B.V. All rights reserved.

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This paper presents a 2kW single-phase high power factor boost rectifier with four cells in interleave connection, operating in critical conduction mode, and employing a soft-switching technique, controlled by Field Programmable Gate Array (FPGA). The soft-switching technique Is based on zero-current-switching (ZCS) cells, providing ZC (zero-current) turn-on and ZCZV (zero-current-zero-voltage) turn-off for the active switches, and ZV (zero-voltage) turn-on and ZC (zero-current) turn-off for the boost diodes. The disadvantages related 'to reverse recovery effects of boost diodes operated in continuous conduction mode (additional losses, and electromagnetic interference (EMI) problems) are minimized, due to the operation in critical conduction mode. In addition, due to the Interleaving technique, the rectifer's features include the reduction in the input current ripple, the reduction in the output voltage ripple, the use of low stress devices, low volume for the EMI input filter, high input power factor (PF), and low total harmonic distortion (THD) In the input current, in compliance with the TEC61000-3-2 standards. The digital controller has been developed using a hardware description language (VHDL) and implemented using a XC2S200E-SpartanII-E/Xilinx FPGA device, performing a true critical conduction operation mode for four interleaved cells, and a closed-loop to provide the output voltage regulation, like as a pre-regulator rectifier. Experimental results are presented for a 2kW implemented prototype with four interleaved cells, 400V nominal output voltage and 220V(rms) nominal input voltage, in order to verify the feasibility and performance of the proposed digital control through the use of a FPGA device.

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

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This paper aims with the use of linear matrix inequalities approach (LMIs) for application in active vibration control problems in smart strutures. A robust controller for active damping in a panel was designed with piezoelectrical actuators in optimal locations for illustration of the main proposal. It was considered, in the simulations of the closed-loop, a model identified by eigensystem realization algorithm (ERA) and reduced by modal decomposition. We tested two differents techniques to solve the problem. The first one uses LMI approach by state-feedback based in an observer design, considering several simultaneous constraints as: a decay rate, limited input on the actuators, bounded output peak (output energy) and robustness to parametic uncertainties. The results demonstrated the vibration attenuation in the structure by controlling only the first modes and the increased damping in the bandwidth of interest. However, it is possible to occur spillover effects, because the design has not been done considering the dynamic uncertainties related with high frequencies modes. In this sense, the second technique uses the classical H. output feedback control, also solved by LMI approach, considering robustness to residual dynamic to overcome the problem found in the first test. The results are compared and discussed. The responses shown the robust performance of the system and the good reduction of the vibration level, without increase mass.

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The separation methods are reduced applications as a result of the operational costs, the low output and the long time to separate the uids. But, these treatment methods are important because of the need for extraction of unwanted contaminants in the oil production. The water and the concentration of oil in water should be minimal (around 40 to 20 ppm) in order to take it to the sea. Because of the need of primary treatment, the objective of this project is to study and implement algorithms for identification of polynomial NARX (Nonlinear Auto-Regressive with Exogenous Input) models in closed loop, implement a structural identification, and compare strategies using PI control and updated on-line NARX predictive models on a combination of three-phase separator in series with three hydro cyclones batteries. The main goal of this project is to: obtain an optimized process of phase separation that will regulate the system, even in the presence of oil gushes; Show that it is possible to get optimized tunings for controllers analyzing the mesh as a whole, and evaluate and compare the strategies of PI and predictive control applied to the process. To accomplish these goals a simulator was used to represent the three phase separator and hydro cyclones. Algorithms were developed for system identification (NARX) using RLS(Recursive Least Square), along with methods for structure models detection. Predictive Control Algorithms were also implemented with NARX model updated on-line, and optimization algorithms using PSO (Particle Swarm Optimization). This project ends with a comparison of results obtained from the use of PI and predictive controllers (both with optimal state through the algorithm of cloud particles) in the simulated system. Thus, concluding that the performed optimizations make the system less sensitive to external perturbations and when optimized, the two controllers show similar results with the assessment of predictive control somewhat less sensitive to disturbances

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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The aim of this paper is to apply methods from optimal control theory, and from the theory of dynamic systems to the mathematical modeling of biological pest control. The linear feedback control problem for nonlinear systems has been formulated in order to obtain the optimal pest control strategy only through the introduction of natural enemies. Asymptotic stability of the closed-loop nonlinear Kolmogorov system is guaranteed by means of a Lyapunov function which can clearly be seen to be the solution of the Hamilton-Jacobi-Bellman equation, thus guaranteeing both stability and optimality. Numerical simulations for three possible scenarios of biological pest control based on the Lotka-Volterra models are provided to show the effectiveness of this method. (c) 2007 Elsevier B.V. All rights reserved.

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This paper presents the control and synchronization of chaos by designing linear feedback controllers. The linear feedback control problem for nonlinear systems has been formulated under optimal control theory viewpoint. Asymptotic stability of the closed-loop nonlinear system is guaranteed by means of a Lyapunov function which can clearly be seen to be the solution of the Hamilton-Jacobi-Bellman equation thus guaranteeing both stability and optimality. The formulated theorem expresses explicitly the form of minimized functional and gives the sufficient conditions that allow using the linear feedback control for nonlinear system. The numerical simulations were provided in order to show the effectiveness of this method for the control of the chaotic Rossler system and synchronization of the hyperchaotic Rossler system. (C) 2007 Elsevier B.V. All rights reserved.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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This work presents the design of a fuzzy controller with simplified architecture that use an artificial neural network working as the aggregation operator for several active fuzzy rules. The simplified architecture of the fuzzy controller is used to minimize the time processing used in the closed loop system operation, the basic procedures of fuzzification are simplified to maximum while all the inference procedures are computed in a private way. As consequence, this simplified architecture allows a fast and easy configuration of the simplified fuzzy controller. The structuring of the fuzzy rules that define the control actions is previously computed using an artificial neural network based on CMAC Cerebellar Model Articulation Controller. The operational limits are standardized and all the control actions are previously calculated and stored in memory. For applications, results and conclusions several configurations of this fuzzy controller are considered.

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An electronic ballast for multiple tubular fluorescent lamp systems is presented. The proposed structure has a high value for the power factor, a dimming capability, and soft switching of the semiconductor devices operated at high frequencies. A zero-current switching pulse width modulated SEPIC converter is used as the rectifying stage and it is controlled using the instantaneous average input current technique. The inverting stage consists of classical resonant half-bridge converter with series-resonant parallel-loaded filters. The dimming control technique is based on varying the switching frequency and monitoring the phase shift of the current drained by the filters and lamps in order to establish a closed loop control. Experimental results are presented that validate the theoretical analysis.

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This paper presents a multi-cell single-phase high power factor boost rectifier in interleave connection, operating in critical conduction mode, employing a soft-switching technique, and controlled by Field Programmable Gate Array (FPGA). The soft-switching technique is based on zero-current-switching (ZCS) cells, providing ZC (zero-current) turn-on and ZCZV (zero-current-zero-voltage) turn-off for the active switches, and ZV (zero-vohage) turn-on and ZC (zero-current) turn-off for the boost diodes. The disadvantages related to reverse recovery effects of boost diodes operated in continuous conduction mode (additional losses, and electromagnetic interference (EMI) problems) are minimized, due to the operation in critical conduction mode. In addition, due to the interleaving technique, the rectifier's features include the reduction in the input current ripple, the reduction in the output voltage ripple, the use of low stress devices, low volume for the EMI input filter, high input power factor (PF), and low total harmonic distortion (THD) in the input current, in compliance with the IEC61000-3-2 standards. The digital controller has been developed using a hardware description language (VHDL) and implemented using a XC2S200E-SpartanII-E/Xilinx FPGA device, performing a true critical conduction operation mode for all interleaved cells, and a closed-loop to provide the output voltage regulation, like as a preregulator rectifier. Experimental results are presented for a implemented prototype with two and with four interleaved cells, 400V nominal output voltage and 220V(rms) nominal input voltage, in order to verify the feasibility and performance of the proposed digital control through the use of a FPGA device.

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The present work introduces a new strategy of induction machines speed adjustment using an adaptive PID (Proportional Integral Derivative) digital controller with gain planning based on the artificial neural networks. This digital controller uses an auxiliary variable to determine the ideal induction machine operating conditions and to establish the closed loop gain of the system. The auxiliary variable value can be estimated from the information stored in a general-purpose artificial neural network based on CMAC (Cerebellar Model Articulation Controller).