876 resultados para Robust controllers


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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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O objetivo principal desta dissertação é apresentar uma solução eficiente, prática e de simples implementação para um problema recorrente em projetos de controladores robustos multivariáveis do tipo LQG/LTR: a elevada ordem que estes controladores podem obter dependendo das complicações apresentadas pelo sistema dificultando para que este possa ser controlado de maneira satisfatória. Para que esta meta seja alcançada, é apresentada uma técnica de redução do modelo de sistemas com metodologia bastante descomplicada, dispensando qualquer necessidade de complexas programações para a sua utilização. Esta metodologia porém, é somente aplicável a uma classe bastante específica de sistema. Em suma, o sistema deve possuir variáveis de estado desacopladas do restante do sistema, ou seja, variáveis que não sofram influências de outras e que também não provoquem grande efeito nas saídas do sistema. Foi escolhido um sistema multivariável de sexta ordem, com duas entradas e duas saídas para que a técnica de redução de ordem de modelo seja testada. Este sistema possui as características especiais mencionadas anteriormente bem como exige o projeto de compensador dinâmico e a adição de integradores às suas saídas para que seja controlado adequadamente. Este trabalho pretende apresentar o procedimento de todo o projeto mencionado, desde a obtenção de um modelo de ordem reduzida até a implementação do controlador LQG/LTR. Em seguida, o controlador obtido é testado através de diversas simulações e os resultados encontrados são discutidos para a avaliação da eficácia e da praticidade do método proposto para obtenção de controladores de ordem reduzida.

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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 Engenharia Elétrica - FEIS

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Pulse-width modulation is widely used to control electronic converters. One of the most topologies used for high DC voltage/low DC voltage conversion is the Buck converter. It is obtained as a second order system with a LC filter between the switching subsystem and the load. The use of a coil with an amorphous magnetic material core instead of air core lets design converters with smaller size. If high switching frequencies are used for obtaining high quality voltage output, the value of the auto inductance L is reduced throughout the time. Then, robust controllers are needed if the accuracy of the converter response must not be affected by auto inductance and load variations. This paper presents a robust controller for a Buck converter based on a state space feedback control system combined with an additional virtual space variable which minimizes the effects of the inductance and load variations when a not-toohigh switching frequency is applied. The system exhibits a null steady-state average error response for the entire range of parameter variations. Simulation results are presented.

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Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained.

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The specific objective of this paper is to develop multivariable controllers that would achieve asymptotic regulation in the presence of parameter variations and disturbance inputs for a tubular reactor used in ammonia synthesis. A ninth order state space model with three control inputs and two disturbance inputs is generated from the nonlinear distributed model using linearization and lumping approximations. Using this model, an approach for control system design is developed keeping in view the imperfections of the model and the measurability of the state variables. Specifically, the design of feedforward and robust integral controllers using state and output feedback is considered. Also, the design of robust multiloop proportional integral controllers is presented. Finally the performance of these controllers is evaluated through simulation.

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The scale of the Software-Defined Network (SDN) Controller design problem has become apparent with the expansion of SDN deployments. Initial SDN deployments were small-scale, single controller environments for research and usecase testing. Today, enterprise deployments requiring multiple controllers are gathering momentum e.g. Google’s backbone network, Microsoft’s public cloud, and NTT’s edge gateway. Third-party applications are also becoming available e.g. HP SDN App Store. The increase in components and interfaces for the evolved SDN implementation increases the security challenges of the SDN controller design. In this work, the requirements of a secure, robust, and resilient SDN controller are identified, stateof-the-art open-source SDN controllers are analyzed with respect to the security of their design, and recommendations for security improvements are provided. This contribution highlights the gap between the potential security solutions for SDN controllers and the actual security level of current controller designs.

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This work proposes a computational tool to assist power system engineers in the field tuning of power system stabilizers (PSSs) and Automatic Voltage Regulators (AVRs). The outcome of this tool is a range of gain values for theses controllers within which there is a theoretical guarantee of stability for the closed-loop system. This range is given as a set of limit values for the static gains of the controllers of interest, in such a way that the engineer responsible for the field tuning of PSSs and/or AVRs can be confident with respect to system stability when adjusting the corresponding static gains within this range. This feature of the proposed tool is highly desirable from a practical viewpoint, since the PSS and AVR commissioning stage always involve some readjustment of the controller gains to account for the differences between the nominal model and the actual behavior of the system. By capturing these differences as uncertainties in the model, this computational tool is able to guarantee stability for the whole uncertain model using an approach based on linear matrix inequalities. It is also important to remark that the tool proposed in this paper can also be applied to other types of parameters of either PSSs or Power Oscillation Dampers, as well as other types of controllers (such as speed governors, for example). To show its effectiveness, applications of the proposed tool to two benchmarks for small signal stability studies are presented at the end of this paper.