No description available.

In this work, we introduce several novel approaches to feedback controller design, known collectively as the “Robust Bode” methods, which adapt classical control principles to a modern robust control (H∞) framework. These methods, based on specially modified Bode diagrams extend familiar frequency-domain controller design techniques to linear and nonlinear, single–input/single– output (SISO) and multi–input/multi–output (MIMO) systems with parametric and/or unstructured uncertainties. In particular, we introduce the Contoured Robust Controller Bode (CRCBode) plots which show contours (level-sets) of a robust metric on the Bode magnitude and phase plots of the controller. An iterative loop shaping design procedure is then employed in an attempt to eliminate all intersections of the controller frequency response with certain forbidden regions indicating that a robust stability and performance criteria is satisfied. For SISO systems a robust stability and performance criterion is derived using Nyquist arguments leading to the robust metric used in the construction of the CRCBode plots. For open-loop unstable systems and for non-minimum phase systems the Youla parametrization of all internally stabilizing controllers is used to develop an alternative Robust Bode method (QBode). The Youla parametrization requires the introduction of state-space methods for coprime factorization, and these methods lead naturally to an elegant connection between linear-quadratic Gaussian (LQG) optimal control theory and Robust Bode loop-shaping controller design. Finally, the Robust Bode approach is extended to MIMO systems. Utilizing a matrix norm based robustness metric on the MIMO CRCBode plots allows cross-coupling between all input/output channels to be immediately assessed and accounted for during the design process, making sequential MIMO loop-shaping controller design feasible.

No description available.

This dissertation examined how complexity theory might offer insight into the behavior of a population of large-scale networked organizational groups. Academic medical centers (AMCs), a large-scale social and policy system that plays a key role in the education of physicians, the conduct of research, and the provision of specialized clinical care, were chosen as an example to demonstrate the enhanced understanding that can be obtained from the application of complexity theory. Graphical and nonlinear mathematical tools were chosen to place this research study in contrast to studies that metaphorically apply the concepts of complexity theory to social systems. Complexity science suggests that AMCs will demonstrate both nonlinearity and the emergence of patterned behaviors characteristic of self-organization in complex adaptive systems. Changes in the fiscal environment of AMCs, influenced by federal policy and the health care delivery market, were hypothesized to be among the factors that mediated changes in AMCs' activities and organizational relationships during a twenty-year period. The collection and examination of multiple indicators within the framework of a study model allowed development of a rich description of the AMC system and identification of patterned behaviors. Graphical analysis was used to identify underlying periodic and chaotic attractors in the AMC system. A logistic equation was used to confirm the presence of nonlinearity. The presence of nonlinearity and the emergence of patterned behavior within schools in different managed care market groups suggested that it is appropriate to treat the population of AMCs as a complex adaptive system. The results of this research study also showed that AMCs have responded to the rise of managed care in the health care delivery marketplace by leveraging their institutional strengths. Identification of nonlinear properties offers a new perspective for understanding the behavior of a population of networked organizations, the management of large-scale systems, strategic planning, and policy formulation. Until researchers and managers recognize the coexistence of nonlinear and linear processes in social systems, they will make decisions on the basis of incomplete information. / Ph. D.

<p>In this Master's thesis, Maximum Likelihood-based parametric identification methods for discrete-time SISO Hammerstein models from perturbed observations on both input and output, are investigated. </p><p>Hammerstein models, consisting of a static nonlinear block followed by a dynamic linear one, are widely applied to modeling nonlinear dynamic systems, i.e., dynamic systems having nonlinearity at its input. </p><p>Two identification methods are proposed. The first one assumes a Hammerstein model where the input signal is noise-free and the output signal is perturbed with colored noise. The second assumes, however, white noises added to the input and output of the nonlinearity and to the output of the whole considered Hammerstein model. Both methods operate directly in the time domain and their properties are illustrated by a number of simulated examples. It should be observed that attention is focused on derivation, numerical calculation, and simulation corresponding to the first identification method mentioned above.</p>

In this Master's thesis, Maximum Likelihood-based parametric identification methods for discrete-time SISO Hammerstein models from perturbed observations on both input and output, are investigated. Hammerstein models, consisting of a static nonlinear block followed by a dynamic linear one, are widely applied to modeling nonlinear dynamic systems, i.e., dynamic systems having nonlinearity at its input. Two identification methods are proposed. The first one assumes a Hammerstein model where the input signal is noise-free and the output signal is perturbed with colored noise. The second assumes, however, white noises added to the input and output of the nonlinearity and to the output of the whole considered Hammerstein model. Both methods operate directly in the time domain and their properties are illustrated by a number of simulated examples. It should be observed that attention is focused on derivation, numerical calculation, and simulation corresponding to the first identification method mentioned above.

The signal structure is a partial structure representation for dynamic systems. It characterizes the causal relationship between manifest variables and is depicted in a weighted graph, where the weights are dynamic operators. Earlier work has defined signal structure for linear time-invariant systems through dynamical structure function. This thesis focuses on the search for the signal structure of nonlinear systems and proves that the signal structure reduces to the linear definition when the systems are linear. Specifically, this work: (1) Defines the complete computational structure for nonlinear systems. (2) Provides a process to find the complete computational structure given a state space model. (3) Defines the signal structure for dynamic systems in general. (4) Provides a process to find the signal structure for a class of dynamic systems from their complete computational structure.

The thesis deals with dynamic simulation of real bell behavior over time. The model is created according to principles of physical discretization as a spring in the FyDiK3D software. In order for the model to be declared as relevant, it is necessary to prove the behavior of the structures used in the elementary tasks of the mechanics. It shows the correlation between the stiffness of normal and diagonal springs. Describes how to use software import tools to create a model. The resulting model approaches its real bell behavior.

Um dos aspectos regulatórios fundamentais para o mercado imobiliário no Brasil são os limites para obtenção de financiamento no Sistema Financeiro de Habitação. Esses limites podem ser definidos de forma a aumentar ou reduzir a oferta de crédito neste mercado, alterando o comportamento dos seus agentes e, com isso, o preço de mercado dos imóveis. Neste trabalho, propomos um modelo de formação de preços no mercado imobiliário brasileiro com base no comportamento dos agentes que o compõem. Os agentes vendedores têm comportamento heterogêneo e são influenciados pela demanda histórica, enquanto que os agentes compradores têm o seu comportamento determinado pela disponibilidade de crédito. Esta disponibilidade de crédito, por sua vez, é definida pelos limites para concessão de financiamento no Sistema Financeiro de Habitação. Verificamos que o processo markoviano que descreve preço de mercado converge para um sistema dinâmico determinístico quando o número de agentes aumenta, e analisamos o comportamento deste sistema dinâmico. Mostramos qual é a família de variáveis aleatórias que representa o comportamento dos agentes vendedores de forma que o sistema apresente um preço de equilíbrio não trivial, condizente com a realidade. Verificamos ainda que o preço de equilíbrio depende não só das regras de concessão de financiamento no Sistema Financeiro de Habitação, como também do preço de reserva dos compradores e da memória e da sensibilidade dos vendedores a alterações na demanda. A memória e a sensibilidade dos vendedores podem levar a oscilações de preços acima ou abaixo do preço de equilíbrio (típicas de processos de formação de bolhas); ou até mesmo a uma bifurcação de Neimark-Sacker, quando o sistema apresenta dinâmica oscilatória estável. / One of the fundamental regulatory aspects for the housing market in Brazil are the limits for obtaining a residential mortgage loan within the Sistema Financeiro de Habitação. These limits can be defined so as to increase or reduce credit supply in this market, changing its agents behavior and, therefore, the housing market price. In this work we propose a pricing model for the brazilian housing market based on the behavior of its agents. Sellers have heterogeneous behavior and are influenced by the historical demand, while buyers behavior is determined by credit availability. The availability of credit is, in its turn, defined by the regulatory limits for obtaining a residential mortgage loan. We have verified that the Markov process which describes the market price converges to a deterministic dynamical system as the number of agents increase, and we have analyzed the behavior of this emerging system. We show which family of random variables may represent the behavior of sellers so that the system has a nontrivial equilibrium price, consistent with reality. We have also verified that the equilibrium price depends not only on the regulatory limits for obtaing a loan, but also on buyers reserve price and on sellers memory and sensitivity to changes in the demand. Sellers memory and sensitivity to changes in the demand can result in price oscillations above or below the equilibrium level, which is typical in bubble formation processes; or even in a Neimark-Sacker bifurcation, when the price has a stable oscillatory dynamics.

Um dos aspectos regulatórios fundamentais para o mercado imobiliário no Brasil são os limites para obtenção de financiamento no Sistema Financeiro de Habitação. Esses limites podem ser definidos de forma a aumentar ou reduzir a oferta de crédito neste mercado, alterando o comportamento dos seus agentes e, com isso, o preço de mercado dos imóveis. Neste trabalho, propomos um modelo de formação de preços no mercado imobiliário brasileiro com base no comportamento dos agentes que o compõem. Os agentes vendedores têm comportamento heterogêneo e são influenciados pela demanda histórica, enquanto que os agentes compradores têm o seu comportamento determinado pela disponibilidade de crédito. Esta disponibilidade de crédito, por sua vez, é definida pelos limites para concessão de financiamento no Sistema Financeiro de Habitação. Verificamos que o processo markoviano que descreve preço de mercado converge para um sistema dinâmico determinístico quando o número de agentes aumenta, e analisamos o comportamento deste sistema dinâmico. Mostramos qual é a família de variáveis aleatórias que representa o comportamento dos agentes vendedores de forma que o sistema apresente um preço de equilíbrio não trivial, condizente com a realidade. Verificamos ainda que o preço de equilíbrio depende não só das regras de concessão de financiamento no Sistema Financeiro de Habitação, como também do preço de reserva dos compradores e da memória e da sensibilidade dos vendedores a alterações na demanda. A memória e a sensibilidade dos vendedores podem levar a oscilações de preços acima ou abaixo do preço de equilíbrio (típicas de processos de formação de bolhas); ou até mesmo a uma bifurcação de Neimark-Sacker, quando o sistema apresenta dinâmica oscilatória estável. / One of the fundamental regulatory aspects for the housing market in Brazil are the limits for obtaining a residential mortgage loan within the Sistema Financeiro de Habitação. These limits can be defined so as to increase or reduce credit supply in this market, changing its agents behavior and, therefore, the housing market price. In this work we propose a pricing model for the brazilian housing market based on the behavior of its agents. Sellers have heterogeneous behavior and are influenced by the historical demand, while buyers behavior is determined by credit availability. The availability of credit is, in its turn, defined by the regulatory limits for obtaining a residential mortgage loan. We have verified that the Markov process which describes the market price converges to a deterministic dynamical system as the number of agents increase, and we have analyzed the behavior of this emerging system. We show which family of random variables may represent the behavior of sellers so that the system has a nontrivial equilibrium price, consistent with reality. We have also verified that the equilibrium price depends not only on the regulatory limits for obtaing a loan, but also on buyers reserve price and on sellers memory and sensitivity to changes in the demand. Sellers memory and sensitivity to changes in the demand can result in price oscillations above or below the equilibrium level, which is typical in bubble formation processes; or even in a Neimark-Sacker bifurcation, when the price has a stable oscillatory dynamics.