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Novel methods of system identification are developed in this dissertation. First set of methods are designed to realize time varying linear dynamical system models from input-output experimental data. The preliminary results obtained in a recent paper by the author are extended to establish a new algorithm called the Time Varying Eigensystem Realization Algorithm (TVERA). The central aim of this algorithm is to obtain a linear, time varying, discrete time model sequence of the dynamic system directly from the input-output data. Important results relating to concepts concerning coordinate systems for linear time varying systems are developed (discrete time theory) and an intuitive understanding of equivalent realizations is provided. A procedure to develop first few time step models is detailed, providing a unified solution to the time varying identification problem. The practical problem of identifying the time varying generalized Markov parameters required for TVERA is presented as the next result. In the process, we generalize the classical time invariant input output AutoRegressive model with an eXogenous input (ARX) models to the time varying case and realize an asymptotically stable observer as a byproduct of the calculations. It is further found that the choice of the generalized time varying ARX model (GTV-ARX) can be set to realize a time varying dead beat observer. Methods to use the developed algorithm(s) in this research are then considered for application to the identification of system models that are bilinear in nature. The fact that bilinear plant models become linear for constant inputs is used in the development of an algorithm that generalizes the classical developments of Juang. An intercept problem is considered as a candidate for application of the time varying identification scheme, where departure motion dynamics model sequence is calculated about a nominal trajectory with suboptimal performance owing to the presence of unstructured perturbations. Control application is subsequently demonstrated. The dynamics of a particle in a rotating tube is considered next for identification using the time varying eigensystem realization algorithm. Continuous time bilinear system identification method is demonstrated using the particle example and the identification of an automobile brake model.

This dissertation studies the model reduction and distributed control problems for interconnected systems, i.e., systems that consist of multiple interacting agents/subsystems. The study of the analysis and synthesis problems for interconnected systems is motivated by the multiple applications that can benefit from the design and implementation of distributed controllers. These applications include automated highway systems and formation flight of unmanned aircraft systems. The systems of interest are modeled using arbitrary directed graphs, where the subsystems correspond to the nodes, and the interconnections between the subsystems are described using the directed edges. In addition to the states of the subsystems, the adopted frameworks also model the interconnections between the subsystems as spatial states. Each agent/subsystem is assumed to have its own actuating and sensing capabilities. These capabilities are leveraged in order to design a controller subsystem for each plant subsystem. In the distributed control paradigm, the controller subsystems interact over the same interconnection structure as the plant subsystems. The models assumed for the subsystems are linear time-varying or linear parameter-varying. Linear time-varying models are useful for describing nonlinear equations that are linearized about prespecified trajectories, and linear parameter-varying models allow for capturing the nonlinearities of the agents, while still being amenable to control using linear techniques. It is clear from the above description that the size of the model for an interconnected system increases with the number of subsystems and the complexity of the interconnection structure. This motivates the development of model reduction techniques to rigorously reduce the size of the given model. In particular, this dissertation presents structure-preserving techniques for model reduction, i.e., techniques that guarantee that the interpretation of each state is retained in the reduced order system. Namely, the sought reduced order system is an interconnected system formed by reduced order subsystems that are interconnected over the same interconnection structure as that of the full order system. Model reduction is important for reducing the computational complexity of the system analysis and control synthesis problems. In this dissertation, interior point methods are extensively used for solving the semidefinite programming problems that arise in analysis and synthesis. / Ph. D. / The work in this dissertation is motivated by the numerous applications in which multiple agents interact and cooperate to perform a coordinated task. Examples of such applications include automated highway systems and formation flight of unmanned aircraft systems. For instance, one can think of the hazardous conditions created by a fire in a building and the benefits of using multiple interacting multirotors to deal with this emergency situation and reduce the risks on humans. This dissertation develops mathematical tools for studying and dealing with these complex systems. Namely, it is shown how controllers can be designed to ensure that such systems perform in the desired way, and how the models that describe the systems of interest can be systematically simplified to facilitate performing the tasks of mathematical analysis and control design.

Although the finite element method is often applied to analyze the dynamics of structures, its application to large, complex structures can be time-consuming and errors in the modeling process may negatively affect the accuracy of analyses based on the model. System identification techniques attempt to circumvent these problems by using experimental response data to characterize or identify a system. However, identification of structures that are time-varying or nonlinear is problematic because the available methods generally require prior understanding about the equations of motion for the system. Nonlinear system identification techniques are generally only applicable to nonlinearities where the functional form of the nonlinearity is known and a general nonlinear system identification theory is not available as is the case with linear theory. Linear time-varying identification methods have been proposed for application to nonlinear systems, but methods for general time-varying systems where the form of the time variance is unknown have only been available for single-input single-output models. This dissertation presents several general linear time-varying methods for multiple-input multiple-output systems where the form of the time variance is entirely unknown. The methods use the proper orthogonal decomposition of measured response data combined with linear system theory to construct a model for predicting the response of an arbitrary linear or nonlinear system without any knowledge of the equations of motion. Separate methods are derived for predicting responses to initial displacements, initial velocities, and forcing functions. Some methods require only one data set but only promise accurate solutions for linear, time-invariant systems that are lightly damped and have a mass matrix proportional to the identity matrix. Other methods use multiple data sets and are valid for general time-varying systems. The proposed methods are applied to linear time-invariant, time-varying, and nonlinear systems via numerical examples and experiments and the factors affecting the accuracy of the methods are discussed. / Ph. D.

Orientadores: Pedro Luis Dias Peres, Ricardo Coração de Leão Fontoura de Oliveira / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-25T15:07:40Z (GMT). No. of bitstreams: 1 Lacerda_MarcioJunior_D.pdf: 1206868 bytes, checksum: c5abb83323581461eb40c95f27c95097 (MD5) Previous issue date: 2014 / Resumo: Este trabalho apresenta novas condições na forma de desigualdades matriciais lineares para o projeto de filtros H-infinito de ordem completa em três diferentes contextos: i) sistemas lineares incertos discretos com um atraso variante no tempo afetando os estados; ii) sistemas lineares com parâmetros variantes no tempo, contínuos e discretos, sujeitos a incertezas nas medições dos parâmetros; iii) sistemas não lineares quadráticos contínuos e discretos no tempo. Para cada contexto, o objetivo é projetar filtros: i) com termos atrasados nos estados; ii) dependentes dos parâmetros incertos medidos; iii) com termos quadráticos. Em cada um dos casos, o ponto de partida é a existência de uma função de Lyapunov que assegure estabilidade e um limitante para a norma H-infinito do sistema aumentado, ou seja, o sistema original conectado com o filtro de ordem completa. As condições de projeto são obtidas impondo-se uma determinada estrutura para as variáveis de folga, resultando em desigualdades matriciais com parâmetros escalares. A eficácia das condições apresentadas é ilustrada por meio de comparações numéricas utilizando exemplos da literatura / Abstract: This work presents new conditions in the form of linear matrix inequalities for full order H-infinity filter design in three different contexts: i) uncertain linear discrete-time systems with a time-varying delay affecting the states ii) linear parameter-varying systems, continuous and discrete-time, subject to inexactly measured parameters; iii) continuous and discrete-time nonlinear quadratic systems. For each context, the aim is to design filters: i) with state-delayed terms; ii) dependent upon the inexactly measured parameters; iii) with quadratic terms. In each case, the starting point is the existence of a Lyapunov function that assures stability and a bound to the H-infinity norm of the augmented system, that is, the original system conected with the full order filter. The design conditions are obtained by imposing a given structure to the slack variables, resulting in matrix inequalities with scalar parameters. The effectiveness of the proposed conditions is illustrated by means of numerical comparisons and benchmark examples from the literature / Doutorado / Automação / Doutor em Engenharia Elétrica

Orientador: Gilmar Barreto / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-26T06:42:54Z (GMT). No. of bitstreams: 1 Battaglin_PauloDavid_D.pdf: 3180685 bytes, checksum: 5e1e9893bb97a4df42116a4c0d8b10d6 (MD5) Previous issue date: 2014 / Resumo: Este trabalho mostrará a aplicação do filtro de Kalman a um processo computacional discreto, o qual será representado por um modelo matemático que é um sistema de equações lineares, multivariáveis, discretas, estocásticas e variantes no tempo. As contribuições desta pesquisa evidenciam a construção de um modelo matemático apropriado de observabilidade instantânea para representar sistemas que variam rapidamente no tempo; a construção dos fundamentos teóricos do filtro de Kalman a ser aplicado em sistemas lineares, multivariáveis, discretos, estocásticos e variantes no tempo; bem como a construção deste filtro neste contexto e sua aplicação a um processo computacional discreto. Neste trabalho propomos um método para determinar: a matriz de observabilidade instantânea, o vetor de estimação de estado interno, a matriz de covariâncias de erros de estimação de estado interno e a latência de um processo computacional discreto, quando as medidas na saída do computador são conhecidas. Aqui mostramos que quando a propriedade observabilidade instantânea do sistema é verificada, a latência de um processo computacional pode ser estimada. Esta é uma vantagem comparada com os métodos de observabilidade usual, os quais são baseados em cenários estáticos. A aplicação potencial dos resultados deste trabalho é na predição de congestionamentos em processos que variam no tempo e acontecem em computadores digitais. Em uma perspectiva mais ampla, o método da observabilidade instantânea pode ser aplicado na identificação de patologias, na previsão de tempo, em navegação e rastreamento no solo, na água e no ar; no mercado de ações e em muitas outras áreas / Abstract: This work will show the application of the Kalman filter to a discrete computational process, which will be represented by a mathematical model: a system of linear, multivariable, discrete, stochastic and time-varying equations. The contributions of this research show the construction of an appropriate mathematical model of instantaneous observability to represent systems that vary quickly in time; the construction of the theoretical foundations of the Kalman filter to be applied to a linear, multivariable, discrete, stochastic and time-varying system; the construction of this filter in this context and its application to a discrete computational process. In this research we propose a method to determine: the instantaneous observability matrix, the internal state vector estimation, Covariance matrix of internal state estimation error and the latency of a digital computational process, when the measures on the computer output are known. Here we show that when the instantaneous observability property of the system comes true, a computing process latency can be estimated. This is an advantage compared to usual observability methods, which are based on static scenarios. The potential application of the results of this work is to predict bottlenecks in time-varying processes which happen inside the discrete computers. In a broader perspective, the instantaneous observability method can be applied on identification of a pathology, weather forecast, navigation and tracking on ground, in the water and in the air; in stock market prediction and many other areas / Doutorado / Automação / Doutor em Engenharia Elétrica

Orientador: Juan Francisco Camino dos Santos / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-22T01:28:47Z (GMT). No. of bitstreams: 1 Rodrigues_LuisAntonio_M.pdf: 1882720 bytes, checksum: efb662d3473168965c88de41357ef029 (MD5) Previous issue date: 2012 / Resumo: O presente trabalho investiga os problemas de estabilidade assintótica e desempenho H'INFINITO' de sistemas lineares a parâmetros variantes discretos no tempo. São fornecidas condições suficientes para análise de estabilidade, análise de desempenho H'INFINITO' e síntese de controladores estáticos de realimentação de saída robustos e por ganho escalonado. Além disso, é proposto um método de parametrização polinomial homogênea de sistemas LPV afins. Assume-se que a matriz do sistema tem dependência polinomial homogênea de grau arbitrário sobre os parâmetros que variam dentro de um politopo com conhecidos limitantes sobre suas taxas de variação. As propriedades geométricas do domínio politópico são exploradas para se obter um conjunto finito de desigualdades matriciais lineares que levam em consideração os limitantes sobre as taxas de variação dos parâmetros. As condições LMIs são obtidas usando uma função de Lyapunov quadrática nos estados com dependência polinomial homogênea dos parâmetros variantes em instantes sucessivos de tempo. As condições fornecidas são aplicadas no modelo LPV de um sistema vibroacústico. Comparações com resultados numéricos encontrados na literatura mostram os benefícios das técnicas propostas / Abstract: This work investigates stability and H'INFINITE' performance of discrete-time linear parameter varying systems. Sufficient conditions for stability analysis, H'INFINITE' performance analysis and synthesis of both robust and gain-scheduled static output feedback controller are provided. It is assumed that the system matrices have a homogeneous polynomial dependence of arbitrary degree on the time-varying parameters. Thus, a homogeneous-polynomially parametrization method for affine LPV systems is proposed. The parameters are assumed to vary inside a polytope and to have known bounds on their rates of variation. The geometric properties of the polytopic domain are exploited to derive a finite set of LMIs that take into account the bounds on the rates of variation of the scheduling parameters. The LMI conditions are obtained using a quadratic in the state Lyapunov function with a homogeneous polynomial dependence on the scheduling parameters at successive instants of time. The proposed techniques are applied to an LPV model of a vibroacoustic setup. Comparisons with numerical results found in literature show the benefits of the proposed approach / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestre em Engenharia Mecânica

This dissertation advances tools for the certification of unmanned aircraft system (UAS) flight controllers. We develop two thrusts to this goal: (1) the validation and improvement of an uncertain UAS framework based on integral quadratic constraint (IQC) theory and (2) the development of novel IQC theorems which allow the analysis of uncertain systems having time-varying characteristics. Pertaining to the first thrust, this work improves and implements an IQC-based robustness analysis framework for UAS. The approach models the UAS using a linear fractional transformation on uncertainties and conducts robustness analysis on the uncertain system via IQC theory. By expressing the set of desired UAS flight paths with an uncertainty, the framework enables analysis of the uncertain UAS flying about any level path whose radius of curvature is bounded. To demonstrate the versatility of this technique, we use IQC analysis to tune trajectory-tracking and path-following controllers designed via H2 or H-infinity synthesis methods. IQC analysis is also used to tune path-following PID controllers. By employing a non-deterministic simulation environment and conducting numerous flight tests, we demonstrate the capability of the framework in predicting loss of control, comparing the robustness of different controllers, and tuning controllers. Finally, this work demonstrates that signal IQCs have an important role in obtaining IQC analysis results which are less conservative and more consistent with observations from flight test data. With regards to the second thrust, we prove a novel theorem which enables robustness analysis of uncertain systems where the nominal plant and the IQC multiplier are linear time-varying systems and the nominal plant may have a non-zero initial condition. When the nominal plant and the IQC multiplier are eventually periodic, robustness analysis can be accomplished by solving a finite-dimensional semidefinite program. Time-varying IQC multipliers are beneficial in analysis because they provide the possibility of reducing conservatism and are capable of expressing uncertainties that have unique time-domain characteristics. A number of time-varying IQC multipliers are introduced to better describe such uncertainties. The utility of this theorem is demonstrated with various examples, including one which produces bounds on the UAS position after an aggressive Split-S maneuver. / Doctor of Philosophy / This work develops tools to aid in the certification of unmanned aircraft system (UAS) flight controllers. The forthcoming results are founded on robust control theory, which allows the incorporation of a variety of uncertainties in the UAS mathematical model and provides tools to determine how robust the system is to these uncertainties. Such a foundation provides a complementary perspective to that obtained with simulations. Whereas simulation environments provide a probabilistic-type analysis and are oftentimes costly, the following results provide worst-case guarantees—for the allowable disturbances and uncertainties—and require far less computational resources. Here we take two approaches in our development of certification tools for UAS. First we validate and improve on an uncertain UAS framework that relies on integral quadratic constraint (IQC) theory to analyze the robustness of the UAS in the presence of uncertainties and disturbances. Our second approach develops novel IQC theorems that can aid in providing bounds on the UAS state during its flight trajectory. Though the applications in this dissertation are focused on UAS, the theory can be applied to a wide variety of physical and nonphysical problems wherein uncertainties in the mathematical model cannot be avoided.

Ce mémoire est dédié à l’étude de la synthèse de l’estimation d’état en temps fini par une approche algébrique (les techniques développées au sein de l’équipe ALIEN) pour les systèmes linéaires à paramètres invariant dans le temps (LTI) sujets à des perturbations extérieures inconnues, les systèmes linéaires à paramètres variant dans le temps (LTV) et les systèmes linéaires à commutation en temps continu (SLC). Pour les systèmes LTI et LTV, une expression formelle de l’état en fonction des intégrales itérées des sorties et de l’entrée a été donnée. Pour les systèmes linéaires à commutation, en combinant les résultats de l’estimation d’état pour les systèmes LTI et de la détection de l’instant de commutation en temps réel présentée dans le chapitre 4, nous donnons la démarche principale de l’estimation en temps réel du mode courant et l’état continu du système. Pour ce faire, on applique certains outils mathématiques : la transformation de Laplace, les outils issus du calcul opérationnel et la théorie des distributions / This PhD thesis is dedicated to the synthesis of the state estimation in a finite time by an algebraic approach (the techniques developed within the ALIEN group) for the linear time-invariant systems (LTI) subject to the external unknown disturbances, the linear time-varying systems (LTV) and the switched linear systems (SLC) in continuous time. For the LTI and LTV systems, a formal expression of state as a function of iterated integrals of the output and the input is obtained. For switched linear systems, combining the results of state estimation for LTI systems and switch instant detection presented in Chapter 4, we give the main approach of current mode estimation and the continuous state estimation in real time. To do this, one applies some mathematical tools: Laplace transforms, the operational calculus and the theory of distribution