O projeto de controladores baseado em dados consiste no ajuste dos parâmetros do controlador diretamente das bateladas de dados do processo, sem a necessidade de um modelo. O ajuste é feito resolvendo um problema de otimização, onde procura-se o argumento que minimize uma determinada função custo. Para resolver o problema de otimização são utilizados nesses métodos o algoritmo do gradiente, o algoritmo de Newton e variações destes. O algoritmo do gradiente apenas necessita informação do gradiente da função custo enquanto que os outros utilizam mais informações como a hessiana. Para obter estas últimas informações são utilizados experimentos mais longos e mais complexos, o que torna a aplicação mais complicada. Nesta linha o algoritmo do gradiente se apresenta como a melhor alternativa, por este motivo foi escolhido como foco deste trabalho. A convergência do algoritmo do gradiente para o mínimo global da função custo, no contexto de projeto de controladores, não é encontrada na bibliografia, decidiu-se portanto estudá-la. Essa convergência depende das condições iniciais do algoritmo e do tamanho do passo de iteração utilizado. É mostrado que as condições iniciais precisam estar dentro de uma certa região de atração. Formas de aumentar esta região de atração são tratadas na metodologia chamada Shaping da Função Custo. A principal contribuição deste trabalho é apresentar um método eficiente para a escolha do tamanho do passo de iteração que garante a convergência para o mínimo global da função custo. Algumas informações do processo são necessárias para o cálculo do tamanho do passo de iteração, também são apresentadas maneiras de obter estimativas para estas informações. Simulações e experimentos demonstram o funcionamento dos métodos. / Data-based control design methods consist of adjusting the parameters of the controller directly from batches of input-output data of the process; no process model is used. The adjustment is done by solving an optimization problem, which searches the argument that minimizes a specific cost function. Iterative algorithms based on the gradient are applied to solve the optimization problem, like the steepest descent algorithm, Newton algorithm and some variations. The only information utilized for the steepest descent algorithm is the gradient of the cost function, while the others need more information like the hessian. Longer and more complex experiments are used to obtain more informations, that turns the application more complicated. For this reason, the steepest descent method was chosen to be studied in this work. The convergence of the steepest descent algorithm to the global minimum is not fully studied in the literature. This convergence depends on the initial conditions of the algorithm and on the step size. The initial conditions must be inside a specific domain of attraction, and how to enlarge this domain is treated by the methodology Cost Function Shaping. The main contribution of this work is a method to compute efficiently the step size, to ensure convergence to the global minimum. Some informations about the process are utilized, and this work presents how to estimate these informations. Simulations and experiments demonstrate how the methods work.

O projeto de controladores baseado em dados consiste no ajuste dos parâmetros do controlador diretamente das bateladas de dados do processo, sem a necessidade de um modelo. O ajuste é feito resolvendo um problema de otimização, onde procura-se o argumento que minimize uma determinada função custo. Para resolver o problema de otimização são utilizados nesses métodos o algoritmo do gradiente, o algoritmo de Newton e variações destes. O algoritmo do gradiente apenas necessita informação do gradiente da função custo enquanto que os outros utilizam mais informações como a hessiana. Para obter estas últimas informações são utilizados experimentos mais longos e mais complexos, o que torna a aplicação mais complicada. Nesta linha o algoritmo do gradiente se apresenta como a melhor alternativa, por este motivo foi escolhido como foco deste trabalho. A convergência do algoritmo do gradiente para o mínimo global da função custo, no contexto de projeto de controladores, não é encontrada na bibliografia, decidiu-se portanto estudá-la. Essa convergência depende das condições iniciais do algoritmo e do tamanho do passo de iteração utilizado. É mostrado que as condições iniciais precisam estar dentro de uma certa região de atração. Formas de aumentar esta região de atração são tratadas na metodologia chamada Shaping da Função Custo. A principal contribuição deste trabalho é apresentar um método eficiente para a escolha do tamanho do passo de iteração que garante a convergência para o mínimo global da função custo. Algumas informações do processo são necessárias para o cálculo do tamanho do passo de iteração, também são apresentadas maneiras de obter estimativas para estas informações. Simulações e experimentos demonstram o funcionamento dos métodos. / Data-based control design methods consist of adjusting the parameters of the controller directly from batches of input-output data of the process; no process model is used. The adjustment is done by solving an optimization problem, which searches the argument that minimizes a specific cost function. Iterative algorithms based on the gradient are applied to solve the optimization problem, like the steepest descent algorithm, Newton algorithm and some variations. The only information utilized for the steepest descent algorithm is the gradient of the cost function, while the others need more information like the hessian. Longer and more complex experiments are used to obtain more informations, that turns the application more complicated. For this reason, the steepest descent method was chosen to be studied in this work. The convergence of the steepest descent algorithm to the global minimum is not fully studied in the literature. This convergence depends on the initial conditions of the algorithm and on the step size. The initial conditions must be inside a specific domain of attraction, and how to enlarge this domain is treated by the methodology Cost Function Shaping. The main contribution of this work is a method to compute efficiently the step size, to ensure convergence to the global minimum. Some informations about the process are utilized, and this work presents how to estimate these informations. Simulations and experiments demonstrate how the methods work.

In this thesis, we focus on the numerical study on some inverse problems and optimal control problems. In the first part, we consider some linear inverse problems with discontinuous or piecewise constant solutions. We use the total variation to regularize these inverse problems and then the finite element technique to discretize the regularized problems. These discretized problems are treated from the saddle-point perspective; and some primal-dual numerical schemes are proposed. We intensively investigate the convergence of these primal-dual type schemes, establishing the global convergence and estimating their worst-case convergence rates measured by the iteration complexity. We test these schemes by some experiments and verify their efficiency numerically. In the second part, we consider the finite difference and finite element discretization for an optimal control problem which is governed by time fractional diffusion equation. The prior error estimate of the discretized model is analyzed, and a projection gradient method is applied for iteratively solving the fully discretized surrogate. Some numerical experiments are conducted to verify the efficiency of the proposed method. Overall speaking, the thesis has been mainly inspired by some most recent advances developed in optimization community, especially in the area of operator splitting methods for convex programming; and it can be regarded as a combination of some contemporary optimization techniques with some relatively mature inverse and control problems. Keywords: Total variation minimization, linear inverse problem, saddle-point problem, finite element method, primal-dual method, convergence rate, optimal control problem, time fractional diffusion equation, projection gradient method.

O projeto de controladores baseado em dados consiste no ajuste dos parâmetros do controlador diretamente das bateladas de dados do processo, sem a necessidade de um modelo. O ajuste é feito resolvendo um problema de otimização, onde procura-se o argumento que minimize uma determinada função custo. Para resolver o problema de otimização são utilizados nesses métodos o algoritmo do gradiente, o algoritmo de Newton e variações destes. O algoritmo do gradiente apenas necessita informação do gradiente da função custo enquanto que os outros utilizam mais informações como a hessiana. Para obter estas últimas informações são utilizados experimentos mais longos e mais complexos, o que torna a aplicação mais complicada. Nesta linha o algoritmo do gradiente se apresenta como a melhor alternativa, por este motivo foi escolhido como foco deste trabalho. A convergência do algoritmo do gradiente para o mínimo global da função custo, no contexto de projeto de controladores, não é encontrada na bibliografia, decidiu-se portanto estudá-la. Essa convergência depende das condições iniciais do algoritmo e do tamanho do passo de iteração utilizado. É mostrado que as condições iniciais precisam estar dentro de uma certa região de atração. Formas de aumentar esta região de atração são tratadas na metodologia chamada Shaping da Função Custo. A principal contribuição deste trabalho é apresentar um método eficiente para a escolha do tamanho do passo de iteração que garante a convergência para o mínimo global da função custo. Algumas informações do processo são necessárias para o cálculo do tamanho do passo de iteração, também são apresentadas maneiras de obter estimativas para estas informações. Simulações e experimentos demonstram o funcionamento dos métodos. / Data-based control design methods consist of adjusting the parameters of the controller directly from batches of input-output data of the process; no process model is used. The adjustment is done by solving an optimization problem, which searches the argument that minimizes a specific cost function. Iterative algorithms based on the gradient are applied to solve the optimization problem, like the steepest descent algorithm, Newton algorithm and some variations. The only information utilized for the steepest descent algorithm is the gradient of the cost function, while the others need more information like the hessian. Longer and more complex experiments are used to obtain more informations, that turns the application more complicated. For this reason, the steepest descent method was chosen to be studied in this work. The convergence of the steepest descent algorithm to the global minimum is not fully studied in the literature. This convergence depends on the initial conditions of the algorithm and on the step size. The initial conditions must be inside a specific domain of attraction, and how to enlarge this domain is treated by the methodology Cost Function Shaping. The main contribution of this work is a method to compute efficiently the step size, to ensure convergence to the global minimum. Some informations about the process are utilized, and this work presents how to estimate these informations. Simulations and experiments demonstrate how the methods work.

Made available in DSpace on 2014-07-29T16:02:16Z (GMT). No. of bitstreams: 1 Dissertacao - Lays G C S de Jesus - Matematica.pdf: 936886 bytes, checksum: 303443d6b8eff2308a239c47a7c0d5af (MD5) Previous issue date: 2010-04-30 / In this work, we study the descent of methods for problem of optimization multiobjective which we introduce an order of relation induced by an closed convex cone.We study as it wiel calculate an descent of direction and we prove that every accumalation point of the sequence generated by the descent of methods with search of Armijo is weakly efficient. / Neste trabalho, estudamos o método de descida para problemas de otimização multiobjetivo, para o qual introduzimos uma relação de ordem induzida por um cone fechado e convexo. Estudamos como calcular uma direção de descida e provamos que todo ponto de acumulação da sequência gerada pelo método de descida com busca de Armijo é fracamente eficiente.

This paper proposes new iterative methods for the efficient computation of the smallest eigenvalue of the symmetric nonlinear matrix eigenvalue problems of large order with a monotone dependence on the spectral parameter. Monotone nonlinear eigenvalue problems for differential equations have important applications in mechanics and physics. The discretization of these eigenvalue problems leads to ill-conditioned nonlinear eigenvalue problems with very large sparse matrices monotone depending on the spectral parameter. To compute the smallest eigenvalue of large matrix nonlinear eigenvalue problem, we suggest preconditioned iterative methods: preconditioned simple iteration method, preconditioned steepest descent method, and preconditioned conjugate gradient method. These methods use only matrix-vector multiplications, preconditioner-vector multiplications, linear operations with vectors and inner products of vectors. We investigate the convergence and derive grid-independent error estimates of these methods for computing eigenvalues. Numerical experiments demonstrate practical effectiveness of the proposed methods for a class of mechanical problems.

Electromagnetic (EM) imaging schemes are inherently non-linear and ill-posed. Albeit there exist remedies to these fundamental problems, more efficient solutions are still being sought. To this end, in this thesis, the non-linearity is tackled in- corporating a multitude of techniques (ranging from Born approximation (linear), inexact Newton (linearized) to complete nonlinear iterative Landweber schemes) that can account for weak to strong scattering problems. The ill-posedness of the EM inverse scattering problem is circumvented by formulating the above methods into a minimization problem with a sparsity constraint. More specifically, four novel in- verse scattering schemes are formulated and implemented. (i) A greedy algorithm is used together with a simple artificial neural network (ANN) for efficient and accu- rate EM imaging of weak scatterers. The ANN is used to predict the sparsity level of the investigation domain which is then used as the L0 - constraint parameter for the greedy algorithm. (ii) An inexact Newton scheme that enforces the sparsity con- straint on the derivative of the unknown material properties (not necessarily sparse) is proposed. The inverse scattering problem is formulated as a nonlinear function of the derivative of the material properties. This approach results in significant spar- sification where any sparsity regularization method could be efficiently applied. (iii) A sparsity regularized nonlinear contrast source (CS) framework is developed to di- rectly solve the nonlinear minimization problem using Landweber iterations where the convergence is accelerated using a self-adaptive projected accelerated steepest descent algorithm. (iv) A 2.5D finite difference frequency domain (FDFD) based in- verse scattering scheme is developed for imaging scatterers embedded in lossy and inhomogeneous media. The FDFD based inversion algorithm does not require the Green’s function of the background medium and appears a promising technique for biomedical and subsurface imaging with a reasonable computational time. Numerical experiments, which are carried out using synthetically generated mea- surements, show that the images recovered by these sparsity-regularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.

The method of dual steepest descent is used to solve ordinary differential equations with nonlinear boundary conditions. A general boundary condition is B(u) = 0 where where B is a continuous functional on the nth order Sobolev space Hn[0.1J. If F:HnCO,l] —• L2[0,1] represents a 2 differential equation, define *(u) = 1/2 IIF < u) li and £(u) = 1/2 l!B(u)ll2. Steepest descent is applied to the functional 2 £ a * + £. Two special cases are considered. If f:lR —• R is C^(2), a Type I boundary condition is defined by B(u) = f(u(0),u(1)). Given K: [0,1}xR—•and g: [0,1] —• R of bounded variation, a Type II boundary condition is B(u) = ƒ1/0K(x,u(x))dg(x).

In this dissertation, applications of thermodynamics at the macroscopic and quantum levels of description are developed. Within the macroscopic level, an upper-level Sustainability Assessment Framework (SAF) is proposed for evaluating the sustainable and resilient synthesis/design and operation of sets of small renewable and non-renewable energy production technologies coupled to power production transmission and distribution networks via microgrids. The upper-level SAF is developed in accord with the four pillars of sustainability, i.e., economic, environmental, technical and social. A superstructure of energy producers with a fixed transmission network initially available is synthesized based on the day with the highest energy demand of the year, resulting in an optimum synthesis, design, and off-design network configuration. The optimization is developed in a quasi-stationary manner with an hourly basis, including partial-load behavior for the producers. Since sustainability indices are typically not expressed in the same units, multicriteria decision making methods are employed to obtain a composite sustainability index. Within the quantum level of description, steepest-entropy-ascent quantum thermodynamics (SEA-QT) is used to model the phenomenon of decoherence. The two smallest microscopic composite systems encountered in Nature are studied. The first of these is composed of two two-level-type particles, while the second one is composed of a two-level-type particle and an electromagnetic field. Starting from a non-equilibrium state of the composite and for each of the two different composite systems, the time evolution of the state of the composite as well as that of the reduced and locally-perceived states of the constituents are traced along their relaxation towards stable equilibrium at constant system energy. The modeling shows how the initial entanglement and coherence between constituents are reduced during the relaxation towards a state of stable equilibrium. When the constituents are non-interacting, the initial coherence is lost once stable equilibrium is reached. When they are interacting, the coherence in the final stable equilibrium state is only that due to the interaction. For the atom-photon field composite system, decoherence is compared with data obtained experimentally by the CQED group at Paris. The SEA-QT method applied in this dissertation provides an alternative and comprehensive explanation to that obtained with the "open system" approach of Quantum Thermodynamics (QT) and its associated quantum master equations of the Kossakowski-Lindblad-Gorini-Sudarshan type. / Ph. D.

In this research, a first-principles, non-equilibrium thermodynamic-ensemble approach applicable across all temporal and spatial scales is developed based on steepest-entropy-ascent quantum thermodynamics (SEAQT). The SEAQT framework provides an equation of motion consisting of both reversible mechanical dynamics and irreversible relaxation dynamics, which is able to describe the evolution of any state of any system, equilibrium or non-equilibrium. Its key feature is that the irreversible dynamics is based on a gradient dynamics in system state space instead of the microscopic mechanics of more traditional approaches. System energy eigenstructure and density operator (or ensemble probability distribution) describe the system and system thermodynamic state, respectively. Extensive properties (i.e., energy, entropy, and particle number) play a key role in formulating the equation of motion and in describing non-equilibrium state evolutions. All the concepts involved in this framework (i.e., eigentstructure, density operator, and extensive properties) are well defined at all temporal and spatial scales leading to the extremely broad applicability of SEAQT. The focus of the present research is that of developing non-equilibrium thermodynamic models based specifically on the irreversible part of the equation of motion of SEAQT and applying these to the study of pure relaxation processes of systems in non-equilibrium states undergoing chemical reactions and heat and mass diffusion. As part of the theoretical investigation, the new concept of hypo-equilibrium state is introduced and developed. It is able to describe any non-equilibrium state going through a pure relaxation process and is a generalization of the concept of stable equilibrium of equilibrium thermodynamics to the non-equilibrium realm. Using the concept of hypo-equilibrium state, it is shown that non-equilibrium intensive properties can be fundamentally defined throughout the relaxation process. The definition of non-equilibrium intensive properties also relies on various ensemble descriptions of system state. In this research, three SEAQT ensemble descriptions, i.e., the canonical, grand canonical, and isothermal-isobaric, are derived corresponding, respectively, to the definition of temperature, chemical potential, and pressure. To computationally and not just theoretically permit the application of the SEAQT framework across all scales, a density of states method is developed, which is applicable to solving the SEAQT equation of motion for all types of non-equilibrium relaxation processes. In addition, a heterogeneous multiscale method (HMM) algorithm is also applied to extend the application of the SEAQT framework to multiscale modeling. Applications of this framework are given for systems involving chemical kinetics, the heat and mass diffusion of indistinguishable particles, power cycles, and the complex, coupled reaction-diffusion pathways of a solid oxide fuel cell (SOFC) cathode. / Ph. D.