A ocorrência de congestionamento degrada o desempenho das redes de computadores. Dentre as conseqüências negativas da sua ocorrência cita-se a diminuição da vazão, a perda de pacotes, e o aumento do atraso. Para prevenir e controlar o congestionamento, o protocolo Transmission Control Protocol (TCP) varia a taxa de transmissão de dados de acordo com o nível de congestionamento existente. As políticas de Gerenciamento Ativo de Filas, do Inglês Active Queue Management (AQM), monitoram o nível de ocupação das filas, afim de notificar o congestionamento incipiente aos nós emissores. Esta notificação é realizada através da marca¸c?ao ou do descarte de pacotes. O sistema de controle de congestionamento em redes TCP/IP, pode ser visto como um sistema de controle por retroalimentação, no qual, a taxa de transmissão dos n´os fontes é ajustada de acordo com o nível de ocupação da fila. Os controladores para o gerenciamento ativo de filas determinam o valor da probabilidade de descarte ou de marcação, buscando a maximização da vazão e a minimiza¸c?ao das perdas, garantindo, assim, a estabilidade do tamanho da fila independentemente das variações das condições da rede. Nesta tese, são utilizadas técnicas da teoria de controle ótimo para definir uma política ótima de gerenciamento ativo de filas...
A utilização eficiente da banda passante em redes de alta velocidade e grandes atrasos, denominadas redes com produto banda-atraso elevado (PBA), tornou-se um grande desafio. Isto ocorre devido aos ajustes do protocolo Transmission Control Protocol (TCP). O High Speed TCP (HSTCP), uma variante do TCP para redes com PBA elevado, emprega ajustes mais agressivos permitindo, assim, que a utilização da banda seja escalável. As políticas de Gerenciamento Ativo de Filas ou Active Queue Management (AQM), monitoram o nível de ocupação das filas nos roteadores e notificam o congestionamento incipiente aos emissores TCP através do descarte/marcação de pacotes. O sistema de controle de congestionamento apresenta natureza de retroalimentação, na qual a taxa de transmissão dos nós fontes é ajustada em função do nível de ocupação da fila. Os controladores AQM determinam a probabilidade de descarte/marcação para maximizar a vazão e minimizar perdas, garantindo, assim, a estabilidade do tamanho da fila independentemente das variações das condições da rede. Neste trabalho, define-se a política de gerenciamento ativo de filas HSTCP-H2 para redes com PBA elevado que utilizam o protocolo HSTCP. Para a derivação de HSTCPH2: são utilizadas técnicas de Teoria de Controle Ótimo. A principal característica desta política é considerar o atraso do sistema o que permite melhor utilização dos recursos disponíveis. A estabilidade e os objetivos de desempenho do sistema são expressos e solucionados através de Desigualdades Matriciais Lineares...
Este trabalho apresenta um estudo relativo ao problema de alocação, operação e análise de desempenho de Controladores FACTS em Sistemas Elétricos de Potência. Nesse sentido são descritos os mais importantes Controladores FACTS em termos de suas características operativas e potencialidades e uma rotina computacional para cálculo de fluxo de potência, baseada no método de Newton-Raphson incluindo a representação dos diversos Controladores FACTS é apresentada. A abordagem de alocação e determinação ótima dos parâmetros de controle é vista de maneira desacoplada, sendo o problema de alocação tratado pela técnica de Algoritmos Genéticos e a otimização dos parâmetros dos controladores por meio do método do Gradiente Reduzido. É também proposta uma estratégia alternativa de alocação/operação baseada na funcionalidade dos diferentes controladores. Também neste trabalho, é apresentado o conceito de tracking de potência, que é estendido para permitir uma avaliação não convencional da interferência dos Controladores FACTS na redistribuição dos fluxos de potência. A fim de sumarizar os diversos aspectos associados a alocação, operação e análise de desempenho de Controladores FACTS, um estudo de caso é apresentado; This thesis presents a study related to the problem of allocating...
Tendo em vista a melhora da qualidade do ar, são impostos limites de emissões de poluentes aos motores diesel, obrigando a busca de soluções capazes de reduzir essas emissões. O motor movido a diesel tem como emissão crítica de poluentes os óxidos de nitrogênio e a emissão de material particulado. Toda a química de formação de poluentes está interligada ao processo de combustão, que é diretamente influenciado pelas concentrações de reagentes admitidos no cilindro. A recirculação controlada de gases de escape para a admissão é uma técnica de redução de emissões de óxidos de nitrogênio comumente aplicada ao motor diesel. Motores instalados em veículos estão sujeitos a variações de regime de operação devido à dinâmica veicular, como por exemplo acelerações ou troca de marchas, que produzem alterações na pressão e no fluxo de gases modificando a proporção da mistura de ar limpo e recirculante admitida no cilindro do motor. O sistema de controle dos gases, portanto, é elemento importante no controle das emissões de poluentes. O objeto deste estudo é o projeto e análise de configurações de controle aplicadas ao sistema de ar de um motor diesel. Foram escolhidos os controladores PID descentralizado...
by Javier de Luis.; Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1989.; Title as it appeared in M.I.T. Graduate List, February, 1989 : Design and implementation of optimal controllers for intelligent structures using a functional analysis design method.; Includes bibliographical references (leaves 176-179).
Fonte: The American Institute of Aeronautics and Astronautics (AIAA)Publicador: The American Institute of Aeronautics and Astronautics (AIAA)
Tipo: Conference Paper
Relevância na Pesquisa
The article of record as published may be located at http://arc.aiaa.org; Approved for public display, distribution unlimited; Proceedings of AIAA Guidance, Navigation, and Control Conference ; Paper no. AIAA 2004-5346, Providence, Rhode Island, Aug. 16-19, 2004; Spacecraft guidance and control (G & C) problems are uniquely different from similar problems arising in other control systems because the amount of fuel consumed by a proposed G and C algorithm dictates its engineering feasibility. Despite this stringent requirement, it has become quite prevalent, particularly in the recent formation keeping literature, to recast space G & C problems in terms of a quadratic cost or ignore propellant consumption altogether. In this paper, we show that the proper
cost functions to use are lp-variants of the L1-norm of the control and not the popular quadratic forms. The penalty for not using the L1-norm is significantly more propellant consumption and undesirable continuous thrusting. An illustrative double-integrator problem is used to demonstrate that the fuel penalty incurred for using quadratic costs is at least 18 percent and could be as high as fifty percent. Techniques for solving the nonsmooth L1-optimal control problem for nonlinear dynamical systems are discussed. An example low-thrust orbit transfer problem is formulated and solved to illustrate the techniques.
Fonte: The American Institute of Aeronautics and Astronautics (AIAA)Publicador: The American Institute of Aeronautics and Astronautics (AIAA)
Tipo: Conference Paper
Relevância na Pesquisa
The article of record as published may be located at http://arc.aiaa.org; Approved for public display, distribution unlimited; Proceedings of the American Control Conference ; Arlington, VA June 25-27, 2001, pp. 2388-2393.; We develop state feedback control laws for linear time-varying systems with quadratic cost criteria by an indirect Legendre pseudospectral method. This method approximates the linear two-point boundary value problem to a system of algebraic equations by way of a differentiation matrix. The algebraic system is solved to generate discrete linear transformations between the states and controls at the Legendre-Gauss-Lobatto points. Since these linear transformations involve simple matrix operations, they can be computed rapidly and efficiently. Two methods are proposed: one that circumvents solving the differential Riccati equation by a discrete solution of the boundary value problem, and another that generates a predictor feedback law without the use of transition matrices. Thus our methods obviate the need for solving the time-intensive backward integration of the matrix Riccati differential equation or inverting ill-conditioned transition matrices. A numerical example illustrates the techniques and demonstrates the accuracy and efficiency of these controllers.
This work presents the application of Linear Matrix Inequalities to the robust and optimal adjustment of Power System Stabilizers with pre-defined structure. Results of some tests show that gain and zeros adjustments are sufficient to guarantee robust stability and performance with respect to various operating points. Making use of the flexible structure of LMI's, we propose an algorithm that minimizes the norm of the controllers gain matrix while it guarantees the damping factor specified for the closed loop system, always using a controller with flexible structure. The technique used here is the pole placement, whose objective is to place the poles of the closed loop system in a specific region of the complex plane. Results of tests with a nine-machine system are presented and discussed, in order to validate the algorithm proposed. (C) 2012 Elsevier Ltd. All rights reserved.
Fuzzy logic controllers (FLC) are intelligent systems, based on heuristic knowledge, that have been largely applied in numerous areas of everyday life. They can be used to describe a linear or nonlinear system and are suitable when a real system is not known or too difficult to find their model. FLC provide a formal methodology for representing, manipulating and implementing a human heuristic knowledge on how to control a system. These controllers can be seen as artificial decision makers that operate in a closed-loop system, in real time.
The main aim of this work was to develop a single optimal fuzzy controller, easily adaptable to a wide range of systems – simple to complex, linear to nonlinear – and able to control all these systems. Due to their efficiency in searching and finding optimal solution for high complexity problems, GAs were used to perform the FLC tuning by finding the best parameters to obtain the best responses.
The work was performed using the MATLAB/SIMULINK software. This is a very useful tool that provides an easy way to test and analyse the FLC, the PID and the GAs in the same environment. Therefore, it was proposed a Fuzzy PID controller (FL-PID) type namely, the Fuzzy PD+I. For that, the controller was compared with the classical PID controller tuned with...
We consider the joint design of packet forwarding policies and controllers
for wireless control loops where sensor measurements are sent to the controller
over an unreliable and energy-constrained multi-hop wireless network. For fixed
sampling rate of the sensor, the co-design problem separates into two
well-defined and independent subproblems: transmission scheduling for
maximizing the deadline-constrained reliability and optimal control under
packet loss. We develop optimal and implementable solutions for these
subproblems and show that the optimally co-designed system can be efficiently
found. Numerical examples highlight the many trade-offs involved and
demonstrate the power of our approach.
In this paper, we propose an approach to reduce the optimal controller
synthesis problem of hybrid systems to quantifier elimination; furthermore, we
also show how to combine quantifier elimination with numerical computation in
order to make it more scalable but at the same time, keep arising errors due to
discretization manageable and within bounds. A major advantage of our approach
is not only that it avoids errors due to numerical computation, but it also
gives a better optimal controller. In order to illustrate our approach, we use
the real industrial example of an oil pump provided by the German company HYDAC
within the European project Quasimodo as a case study throughout this paper,
and show that our method improves (up to 7.5%) the results reported in 
based on game theory and model checking.
In this paper, we consider the problem of multi-objective optimal control of
a dynamical system with additive and multiplicative noises with given second
moments and arbitrary probability distributions. The objectives are given by
quadratic constraints in the state and controller, where the quadratic forms
maybe indefinite and thus not necessarily convex. We show that the problem can
be transformed to a semidefinite program and hence convex. The optimization
problem is to be optimized with respect to a certain variable serving as the
covariance matrix of the state and the controller. We show that affine
controllers are optimal and depend on the optimal covariance matrix.
Furthermore, we show that optimal controllers are linear if all the quadratic
forms are convex in the control variable. The solutions are presented for both
the finite and infinite horizon cases. We give a necessary and sufficient
condition for mean square stabilizability of the dynamical system with additive
and multiplicative noises. The condition is a Lyapunov-like condition whose
solution is again given by the covariance matrix of the state and the control
variable. The results are illustrated with an example.
We develop a complete state-space solution to H_2-optimal decentralized
control of poset-causal systems with state-feedback. Our solution is based on
the exploitation of a key separability property of the problem, that enables an
efficient computation of the optimal controller by solving a small number of
uncoupled standard Riccati equations. Our approach gives important insight into
the structure of optimal controllers, such as controller degree bounds that
depend on the structure of the poset. A novel element in our state-space
characterization of the controller is a remarkable pair of transfer functions,
that belong to the incidence algebra of the poset, are inverses of each other,
and are intimately related to prediction of the state along the different paths
on the poset. The results are illustrated by a numerical example.; Comment: 39 pages, 2 figures, submitted to IEEE Transactions on Automatic
This paper presents a method to synthesize a sequence of control inputs for a
discrete-time piecewise linear system equipped with a cost function, such that
the controlled system behavior satisfies a finite-word linear-time temporal
objective with minimal cost. An abstract finite state weighted transition
system is constructed, such that the cost of the optimal control on the
abstract system provides an upper bound on the cost of the optimal control for
the original system. Specifically, the abstract system is constructed from
finite partitions of the state and input spaces by solving optimization
problems, and a sequence of suboptimal controllers is obtained by considering a
sequence of uniformly refined partitions. Furthermore, the costs achieved by
the sequence of suboptimal controllers converge to the optimal cost for the
piecewise linear system. The abstraction refinement algorithm is implemented in
the tool OptCAR. The feasibility of this approach is illustrated on examples,
by constructing automatically, sub-optimal controllers with improving optimal
costs.; Comment: Extended version of ACC 2015 submission
The method of generalized Hamilton-Jacobi-Bellman equations (GHJB) is a
powerful way of creating near-optimal controllers by learning. It is based on
the fact that if we have a feedback controller, and we learn to compute the
gradient grad-J of its cost-to-go function, then we can use that gradient to
define a better controller. We can then use the new controller's grad-J to
define a still-better controller, and so on. Here I point out that GHJB works
indirectly in the sense that it doesn't learn the best approximation to grad-J
but instead learns the time derivative dJ/dt, and infers grad-J from that. I
show that we can get simpler and lower-cost controllers by learning grad-J
directly. To do this, we need teaching signals that report grad-J(x) for a
varied set of states x. I show how to obtain these signals, using the GHJB
equation to calculate one component of grad-J(x) -- the one parallel with dx/dt
-- and computing all the other components by backward-in-time integration,
using a formula similar to the Euler-Lagrange equation. I then compare this
direct algorithm with GHJB on 2 test problems.
We consider optimal distributed controller synthesis for an interconnected
system subject to communication constraints, in linear quadratic settings.
Motivated by the problem of finite heavy duty vehicle platooning, we study
systems composed of interconnected subsystems over a chain graph. By
decomposing the system into orthogonal modes, the cost function can be
separated into individual components. Thereby, derivation of the optimal
controllers in state-space follows immediately. The optimal controllers are
evaluated under the practical setting of heavy duty vehicle platooning with
communication constraints. It is shown that the performance can be
significantly improved by adding a few communication links. The results show
that the proposed optimal distributed controller performs almost as well as the
centralized linear quadratic Gaussian controller and outperforms a suboptimal
controller in terms of control input. Furthermore, the control input energy can
be reduced significantly with the proposed controller compared to the
suboptimal controller, depending on the vehicle position in the platoon. Thus,
the importance of considering preceding vehicles as well as the following
vehicles in a platoon for fuel optimality is concluded.
Efforts in this paper seek to combine graph theory with adaptive dynamic
programming (ADP) as a reinforcement learning (RL) framework to determine
forward-in-time, real-time, approximate optimal controllers for distributed
multi-agent systems with uncertain nonlinear dynamics. A decentralized
continuous time-varying control strategy is proposed, using only local
communication feedback from two-hop neighbors on a communication topology that
has a spanning tree. An actor-critic-identifier architecture is proposed that
employs a nonlinear state derivative estimator to estimate the unknown dynamics
online and uses the estimate thus obtained for value function approximation.
Consider that a linear time-invariant (LTI) plant is given and that we wish
to design a stabilizing controller for it. Admissible controllers are LTI and
must comply with a pre-selected sparsity pattern. The sparsity pattern is
assumed to be quadratically invariant (QI) with respect to the plant, which,
from prior results, guarantees that there is a convex parametrization of all
admissible stabilizing controllers provided that an initial admissible stable
stabilizing controller is provided. This paper addresses the previously
unsolved problem of determining necessary and sufficient conditions for the
existence of an admissible stabilizing controller. The main idea is to cast the
existence of such a controller as the feasibility of an exact model-matching
problem with stability restrictions, which can be tackled using existing
methods. Furthermore, we show that, when it exists, the solution of the
model-matching problem can be used to compute an admissible stabilizing
controller. This method also leads to a convex parametrization that may be
viewed as an extension of Youla's classical approach so as to incorporate
sparsity constraints. Applications of this parametrization on the design of
norm-optimal controllers via convex methods are also explored. An illustrative
example is provided...
There is an increasing demand for controller design techniques capable of
addressing the complex requirements of todays embedded applications. This
demand has sparked the interest in symbolic control where lower complexity
models of control systems are used to cater for complex specifications given by
temporal logics, regular languages, or automata. These specification mechanisms
can be regarded as qualitative since they divide the trajectories of the plant
into bad trajectories (those that need to be avoided) and good trajectories.
However, many applications require also the optimization of quantitative
measures of the trajectories retained by the controller, as specified by a cost
or utility function. As a first step towards the synthesis of controllers
reconciling both qualitative and quantitative specifications, we investigate in
this paper the use of symbolic models for time-optimal controller synthesis. We
consider systems related by approximate (alternating) simulation relations and
show how such relations enable the transfer of time-optimality information
between the systems. We then use this insight to synthesize approximately
time-optimal controllers for a control system by working with a lower
complexity symbolic model. The resulting approximately time-optimal controllers
are equipped with upper and lower bounds for the time to reach a target...
In this paper we consider the problem of global asymptotic stabilization with
prescribed local behavior. We show that this problem can be formulated in terms
of control Lyapunov functions. Moreover, we show that if the local control law
has been synthesized employing a LQ approach, then the associated Lyapunov
function can be seen as the value function of an optimal problem with some
specific local properties. We illustrate these results on two specific classes
of systems: backstepping and feedforward systems. Finally, we show how this
framework can be employed when considering the orbital transfer problem.