In this dissertation, we seek to improve the dynamic modeling and control of autonomous underwater vehicles (AUVs). We address nonlinear hydrodynamic modeling, simplifying modeling assumptions, and robust control for AUVs. In the literature, various hydrodynamic models exist with varying model complexity and with no universally accepted model. We compare various hydrodynamic models traditionally employed to predict the motion of AUVs by estimating model coefficients using least-squares and adaptive identifier techniques. Additionally, we derive several dynamic models for an AUV employing varying sets of simplifying assumptions. We experimentally assess the efficacy of invoking typical assumptions to simplify the equations of motion. For robust control design, we develop a procedure for designing robust attitude controllers based on loop-shaping ideas. We specifically address the challenge of adjusting the desired actuator bandwidth in a loop-shaping design framework. Finally, we present a novel receding horizon H-infinity control algorithm to improve the control of autonomous vehicle systems working in high-disturbance environments, employing a Markov jump linear system framework to model the stochastic and non-stationary disturbances experienced by the vehicle. Our main results include a new Bounded Real Lemma for stability analysis and an output feedback H-infinity control synthesis algorithm. This work uses numerical simulations and extensive field trials of autonomous underwater vehicles to identify and verify dynamic models and to validate control algorithms developed herein. / Ph. D. / In this dissertation, we seek to improve the dynamic modeling and control of autonomous underwater vehicles (AUVs). We compare different models employed to predict the motion of AUVs, and we derive several dynamic models for an AUV employing varying sets of simplifying assumptions. We experimentally assess the efficacy of invoking typical assumptions to simplify the equations of motion. For robust control design, we develop a procedure for designing robust controllers that do not produce excessive fin movements. Finally, we present a novel robust control algorithm to improve the control of autonomous vehicle systems working in high-disturbance environments. This work uses numerical simulations and extensive field trials of autonomous underwater vehicles to identify and verify dynamic models and to validate control algorithms developed herein.

Given a full-range simply-invariant shift-invariant subspace <i>M</i> of the vector-valued <i>L²</i> space on the unit circle, the classical Beurling-Lax-Halmos (BLH) theorem obtains a unitary operator-valued function <i>W</i> so that <i>M</i> may be represented as the image of of the Hardy space <i>H²</i> on the disc under multiplication by <i>W</i>. The work of Ball-Helton later extended this result to find a single function representing a so-called dual shift-invariant pair of subspaces <i>(M,M^Ã)</i> which together form a direct-sum decomposition of <i>L²</i>. In the case where the pair <i>(M,M^Ã)</i> are finite-dimensional perturbations of the Hardy space <i>H²</i> and its orthogonal complement, Ball-Gohberg-Rodman obtained a transfer function realization for the representing function <i>W</i>; this realization was parameterized in terms of zero-pole data computed from the pair <i>(M,M^Ã)</i>. Later work by Ball-Raney extended this analysis to the case of nonrational functions <i>W</i> where the zero-pole data is taken in an infinite-dimensional operator theoretic sense. The current work obtains analogues of these various results for arbitrary dual shift-invariant pairs <i>(M,M^Ã)</i> of the <i>L²</i> spaces on the real line; here, shift-invariance refers to invariance under the translation group. These new results rely on recent advances in the understanding of continuous-time infinite-dimensional input-state-output linear systems which have been codified in the book by Staffans. / Ph. D.

Thesis (MSc)--Stellenbosch University, 2011. / ENGLISH ABSTRACT: In several numerical approaches to PDEs shifted linear systems of the form (zI - A)x = b, need to be solved for several values of the complex scalar z. Often, these linear systems are large and sparse. This thesis investigates efficient numerical methods for these systems that arise from a contour integral approximation to PDEs and compares these methods with direct solvers. In the first part, we present three model PDEs and discuss numerical approaches to solve them. We use the first problem to demonstrate computations with a dense matrix, the second problem to demonstrate computations with a sparse symmetric matrix and the third problem for a sparse but nonsymmetric matrix. To solve the model PDEs numerically we apply two space discrerization methods, namely the finite difference method and the Chebyshev collocation method. The contour integral method mentioned above is used to integrate with respect to the time variable. In the second part, we study a Hessenberg reduction method for solving shifted linear systems with a dense matrix and present numerical comparison of it with the built-in direct linear system solver in SciPy. Since both are direct methods, in the absence of roundoff errors, they give the same result. However, we find that the Hessenberg reduction method is more efficient in CPU-time than the direct solver. As application we solve a one-dimensional version of the heat equation. In the third part, we present efficient techniques for solving shifted systems with a sparse matrix by Krylov subspace methods. Because of their shift-invariance property, the Krylov methods allow one to obtain approximate solutions for all values of the parameter, by generating a single approximation space. Krylov methods applied to the linear systems are generally slowly convergent and hence preconditioning is necessary to improve the convergence. The use of shift-invert preconditioning is discussed and numerical comparisons with a direct sparse solver are presented. As an application we solve a two-dimensional version of the heat equation with and without a convection term. Our numerical experiments show that the preconditioned Krylov methods are efficient in both computational time and memory space as compared to the direct sparse solver. / AFRIKAANSE OPSOMMING: In verskeie numeriese metodes vir PDVs moet geskuifde lineêre stelsels van die vorm (zI − A)x = b, opgelos word vir verskeie waardes van die komplekse skalaar z. Hierdie stelsels is dikwels groot en yl. Hierdie tesis ondersoek numeriese metodes vir sulke stelsels wat voorkom in kontoerintegraalbenaderings vir PDVs en vergelyk hierdie metodes met direkte metodes vir oplossing. In die eerste gedeelte beskou ons drie model PDVs en bespreek numeriese benaderings om hulle op te los. Die eerste probleem word gebruik om berekenings met ’n vol matriks te demonstreer, die tweede probleem word gebruik om berekenings met yl, simmetriese matrikse te demonstreer en die derde probleem vir yl, onsimmetriese matrikse. Om die model PDVs numeries op te los beskou ons twee ruimte-diskretisasie metodes, naamlik die eindige-verskilmetode en die Chebyshev kollokasie-metode. Die kontoerintegraalmetode waarna hierbo verwys is word gebruik om met betrekking tot die tydveranderlike te integreer. In die tweede gedeelte bestudeer ons ’n Hessenberg ontbindingsmetode om geskuifde lineêre stelsels met ’n vol matriks op te los, en ons rapporteer numeriese vergelykings daarvan met die ingeboude direkte oplosser vir lineêre stelsels in SciPy. Aangesien beide metodes direk is lewer hulle dieselfde resultate in die afwesigheid van afrondingsfoute. Ons het egter bevind dat die Hessenberg ontbindingsmetode meer effektief is in terme van rekenaartyd in vergelyking met die direkte oplosser. As toepassing los ons ’n een-dimensionele weergawe van die hittevergelyking op. In die derde gedeelte beskou ons effektiewe tegnieke om geskuifde stelsels met ’n yl matriks op te los, met Krylov subruimte-metodes. As gevolg van hul skuifinvariansie eienskap, laat die Krylov metodes mens toe om benaderde oplossings te verkry vir alle waardes van die parameter, deur slegs een benaderingsruimte voort te bring. Krylov metodes toegepas op lineêre stelsels is in die algemeen stadig konvergerend, en gevolglik is prekondisionering nodig om die konvergensie te verbeter. Die gebruik van prekondisionering gebasseer op skuif-en-omkeer word bespreek en numeriese vergelykings met direkte oplossers word aangebied. As toepassing los ons ’n twee-dimensionele weergawe van die hittevergelyking op, met ’n konveksie term en daarsonder. Ons numeriese eksperimente dui aan dat die Krylov metodes met prekondisionering effektief is, beide in terme van berekeningstyd en rekenaargeheue, in vergelyking met die direkte metodes.

Ce travail de thèse porte, en premier lieu et principalement, sur le diagnostic à base de modèle d’une classe de SLC (Systèmes Linéaires à Commutations). Une problématique récurrente dans ce type de problème concerne la prise en considération de façon explicite les deux aspects, continu et discret, constituant un SLC. Dans ce cadre, nous avons proposé une méthodologie de détection et de localisation de défauts qui combine les outils initialement dédiés au diagnostic des systèmes continus et d’autres spécifiques aux SED (Systèmes à Evénement Discrets). L’approche proposée est conçue autour de trois modules : deux types de générateurs de résidus (issus de l’Automatique continue) et un estimateur en-ligne de l’état discret, appelé diagnostiqueur (issu de l’Automatique événementielle). Notre diagnostiqueur utilise les deux types de résidus, provenant de la partie continue, afin d’identifier le mode de fonctionnement du SLC et d’isoler les défauts de capteurs. Les résidus utilisés pour la localisation des défauts de capteurs sont générés à travers un générateur développé autour d’un schéma DOS (Dedicated Observer Scheme) à base d’observateurs hybrides,à la fois robustes vis-à-vis des entrées inconnues et sensibles aux défauts de capteurs. En second lieu, sur la base des résultats obtenus à l’aide de l’approche de diagnostic développée, nous avons proposé une approche préliminaire de synthèse de lois de commande tolérantes aux défauts de capteurs stabilisante via un retour d’état. Cette approche permet de préserver les performances nominales du système (situation non défaillante)en présence d’un défaut de capteurs. L’idée consiste à reconfigurer le retour d’état en remplaçant le vecteur d’état estimé à partir d’une sortie en défaut par un autre estimé à partir d’une sortie saine. La redondance des estimations est assurée dans cette approche par un banc d’observateurs hybrides robustes qui fournit plusieurs estimations correctes des vecteurs d’état et de sorties. / This thesis focuses, in first and foremost, on the model-based diagnosis of a class of SLC (Switched Linear Systems). The basic idea is to consider the continuous and discrete aspects, forming an SLC, explicitly.In this context, we proposed a methodology for detecting and locating faults that combines the tools originally dedicated to the continuous systems and the DES (discrete event systems) diagnosis. The proposed approach is designed around three modules: two types of residual generators (from the continuous Automatic) and anon-line estimator of the discrete state, called diagnoser (from the event Automatic). Our diagnoser uses the residual generators issue from the continuous part to identify the SLC mode and isolate sensor faults.Residues used for fault location sensors are generated through a generator developed around a scheme DOS(Dedicated Observer Scheme) based on hybrid observers. These observers are robust vis-à-vis the unknown input and sensitive to sensor faults. Secondly, based on the obtained results using the previous diagnosis approach, we proposed a preliminary approach for fault-tolerant state-feedback control law synthesis. This approach preserves the nominal performance of the system (as non-defaulting) in the presence of defective sensors. The idea is to reconfigure the state feedback by replacing the state vector estimated from defected output by another estimated from non-defected one. Redundancy estimates is provided in this approach by a bank of robust hybrid observer that provides several accurate estimates of state vectors and outputs.

Neste trabalho foi estudado um problema de controle de sistemas lineares com saltos Markovianos sem observação da variável de salto, que pode ser escrito como um problema de otimização de considerável complexidade. As contribuições para a área estão divididas em três aspectos. Um dos avanços foi a elaboração de um contraexemplo para a conjectura de que há somente um mínimo local isolado para o problema. Além disso, foi estudado o problema de otimização intermediário, que consiste em fixar todas as variáveis do problema exceto duas matrizes de ganhos, e os resultados indicam que, com uma pequena alteração na formulação, este é um problema biquadrático. Por fim, novos algoritmos foram elaborados a partir de um método disponível na literatura, chamado de método Variacional, adaptando-o para atualizar os ganhos aos pares, levando a problemas intermediários biquadráticos. Três métodos foram implementados para a resolução destes problemas: dois métodos clássicos de descida, Newton e Gradiente, e uma adaptação do próprio método Variacional. Para a análise dos resultados foram utilizados exemplos gerados aleatoriamente a partir do Gerador de SLSM, que pode ser encontrado na literatura, e o método Variacional como referência para comparação com os métodos propostos / This work addresses a control problem arising in linear systems with Markov jumps without observation of the jump variable and advances in three different aspects. First, it is presented a counterexample to the conjecture that states about the uniqueness of local minimum. Second, the intermediary optimization problem, which sets all the variables of the problem except two arrays of gains, was studied and the results suggested that a slight modification in the formulation makes the intermediary problem a biquadratic one. Finally, new algorithms were developed based on a method available in the literature, which is frequently referred to as the Variational method, adapting it to update the gains in pairs, leading to biquadratic intermediary problems. Three methods were implemented to solve these intermediary problems: two classical descent methods, Newton and Gradient, and an adaptation of the Variational method. To evaluate the performance of the proposed methods, randomly generated examples were used and the Variational method was set as reference for comparing the results

This thesis deals with discrete-time Markov jump linear systems (MJLS) with Markov chain in a general Borel space S. Several control issues have been addressed for this class of dynamic systems, including stochastic stability (SS), linear quadratic (LQ) optimal control synthesis, fllter design and a separation principle. Necessary and sffcient conditions for SS have been derived. It was shown that SS is equivalent to the spectral radius of an operator being less than 1 or to the existence of a solution to a \\Lyapunov-like\" equation. Based on the SS concept, the finite- and infinite-horizon LQ optimal control problems were tackled. The solution to the finite- (infinite-)horizon LQ optimal control problem was derived from the associated control S-coupled Riccati difference (algebraic) equations. By S-coupled it is meant that the equations are coupled via an integral over a transition probability kernel having a density with respect to a in-finite measure on the Borel space S. The design of linear Markov jump filters was analyzed and a solution to the finite- (infinite-)horizon filtering problem was obtained based on the associated filtering S-coupled Riccati difference (algebraic) equations. Conditions for the existence and uniqueness of a stabilizing positive semi-definite solution to the control and filtering S-coupled algebraic Riccati equations have also been derived. Finally a separation principle for discrete-time MJLS with Markov chain in a general state space was obtained. It was shown that the optimal controller for a partial information optimal control problem separates the partial information control problem into two problems, one associated with a filtering problem and the other associated with an optimal control problem with complete information. It is expected that the results obtained in this thesis may motivate further research on discrete-time MJLS with Markov chain in a general state space. / Esta tese trata de sistemas lineares com saltos markovianos (MJLS) a tempo discreto com cadeia de Markov em um espaço geral de Borel S. Vários problemas de controle foram abordados para esta classe de sistemas dinâmicos, incluindo estabilidade estocástica (SS), síntese de controle ótimo linear quadrático (LQ), projeto de filtros e um princípio da separação. Condições necessárias e suficientes para a SS foram obtidas. Foi demonstrado que SS é equivalente ao raio espectral de um operador ser menor que 1 ou à existência de uma solução para uma equação de Lyapunov. Os problemas de controle ótimo a horizonte finito e infinito foram abordados com base no conceito de SS. A solução para o problema de controle ótimo LQ a horizonte finito (infinito) foi obtida a partir das associadas equações a diferenças (algébricas) de Riccati S-acopladas de controle. Por S-acopladas entende-se que as equações são acopladas por uma integral sobre o kernel estocástico com densidade de transição em relação a uma medida in-finita no espaço de Borel S. O projeto de filtros lineares markovianos foi analisado e uma solução para o problema da filtragem a horizonte finito (infinito) foi obtida com base nas associadas equações a diferenças (algébricas) de Riccati S-acopladas de filtragem. Condições para a existência e unicidade de uma solução positiva semi-definida e estabilizável para as equações algébricas de Riccati S-acopladas associadas aos problemas de controle e filtragem também foram obtidas. Por último, foi estabelecido um princípio da separação para MJLS a tempo discreto com cadeia de Markov em um espaço de estados geral. Foi demonstrado que o controlador ótimo para um problema de controle ótimo com informação parcial separa o problema de controle com informação parcial em dois problemas, um deles associado a um problema de filtragem e o outro associado a um problema de controle ótimo com informação completa. Espera-se que os resultados obtidos nesta tese possam motivar futuras pesquisas sobre MJLS a tempo discreto com cadeia de Markov em um espaço de estados geral.

Este trabalho versa sobre técnicas de estimação de derivadas de forma não assintótica conforme abordagem algébrica de Michel Fliess, e sua aplicação na determinação quase instantânea do estado interno de um sistema dinâmico, cria-se assim estimadores de estado que não se baseiam no observador de Luenberger. Resumo No desenvolvimento do trabalho demonstramos algumas características destes estimadores e apresentamos uma contribuição teórica para viabilizar a implementação destes estimadores em sistemas de controle de tempo real. Posteriormente, um sistema mecânico de dinâmica não linear foi construído e permitiu ensaios em laboratório que atestam, através dos resultados experimentais encontrados, a funcionalidade deste tipo de estimador de estados. / This work is about derivative estimation technique based on a algebraic and non-asymptotically approach, as devised by Michel Fliess, applied on quasi-instantaneous determination of the internal state of a dynamical system, using state estimators that aren\'t based on the Luenberger observer. Abstract Over this work we present some particularities of these estimators and a theoretical contribution that will able to implement these algebraic estimators in a real time control system. After that, a non-linear mechanical system was built to verify the functionality of these state estimators.

In this work we study the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noises. We consider the multiperiod and finite time horizon optimization of a mean-variance cost function under a new criterion. In this new problem, we apply a constraint on the total output variance weighted by its risk parameter while maximizing the expected output. The optimal control law is obtained from a set of interconnected Riccati difference equations, extending previous results in the literature. The application of our results is exemplified by numerical simulations of a portfolio of stocks and a risk-free asset. / Neste trabalho, estudamos o problema do controle ótimo estocástico de sistemas lineares em tempo discreto sujeitos a saltos Markovianos e ruídos multiplicativos. Consideramos a otimização multiperíodo, com horizonte de tempo finito, de um funcional da média-variância sob um novo critério. Neste novo problema, maximizamos o valor esperado da saída do sistema ao mesmo tempo em que limitamos a sua variância total ponderada pelo seu parâmetro de risco. A lei de controle ótima é obtida através de um conjunto de equações de diferenças de Riccati interconectadas, estendendo resultados anteriores da literatura. São apresentadas simulações numéricas para uma carteira de investimentos com ações e um ativo de risco para exemplificarmos a aplicação de nossos resultados.

Neste trabalho foi estudado um problema de controle de sistemas lineares com saltos Markovianos sem observação da variável de salto, que pode ser escrito como um problema de otimização de considerável complexidade. As contribuições para a área estão divididas em três aspectos. Um dos avanços foi a elaboração de um contraexemplo para a conjectura de que há somente um mínimo local isolado para o problema. Além disso, foi estudado o problema de otimização intermediário, que consiste em fixar todas as variáveis do problema exceto duas matrizes de ganhos, e os resultados indicam que, com uma pequena alteração na formulação, este é um problema biquadrático. Por fim, novos algoritmos foram elaborados a partir de um método disponível na literatura, chamado de método Variacional, adaptando-o para atualizar os ganhos aos pares, levando a problemas intermediários biquadráticos. Três métodos foram implementados para a resolução destes problemas: dois métodos clássicos de descida, Newton e Gradiente, e uma adaptação do próprio método Variacional. Para a análise dos resultados foram utilizados exemplos gerados aleatoriamente a partir do Gerador de SLSM, que pode ser encontrado na literatura, e o método Variacional como referência para comparação com os métodos propostos / This work addresses a control problem arising in linear systems with Markov jumps without observation of the jump variable and advances in three different aspects. First, it is presented a counterexample to the conjecture that states about the uniqueness of local minimum. Second, the intermediary optimization problem, which sets all the variables of the problem except two arrays of gains, was studied and the results suggested that a slight modification in the formulation makes the intermediary problem a biquadratic one. Finally, new algorithms were developed based on a method available in the literature, which is frequently referred to as the Variational method, adapting it to update the gains in pairs, leading to biquadratic intermediary problems. Three methods were implemented to solve these intermediary problems: two classical descent methods, Newton and Gradient, and an adaptation of the Variational method. To evaluate the performance of the proposed methods, randomly generated examples were used and the Variational method was set as reference for comparing the results

Submitted by Maria Cristina (library@lncc.br) on 2018-06-27T12:31:36Z No. of bitstreams: 1 Dissertacao_final_Fortia.pdf: 758629 bytes, checksum: 6b31d1df1ed8f464b298cce7e1ee4180 (MD5) / Approved for entry into archive by Maria Cristina (library@lncc.br) on 2018-06-27T12:31:54Z (GMT) No. of bitstreams: 1 Dissertacao_final_Fortia.pdf: 758629 bytes, checksum: 6b31d1df1ed8f464b298cce7e1ee4180 (MD5) / Made available in DSpace on 2018-06-27T12:32:06Z (GMT). No. of bitstreams: 1 Dissertacao_final_Fortia.pdf: 758629 bytes, checksum: 6b31d1df1ed8f464b298cce7e1ee4180 (MD5) Previous issue date: 2017-02-24 / Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) / Stochastic differential equations with Markovian jump parameters constitute one of the most important class of hybrid dynamical systems, which has been extensively used for the modeling of dynamical systems which are subject to abrupt changes in their structure. The abrupt changes can be due, for instance, to abrupt environmental disturbances, component failure, volatility in economic systems, changes in subsystems interconnections, abrupt changes in the operation of a nonlinear plant, etc. This can be found, for instance, in aircraft control systems, robot systems, large flexible structure for space station, etc. We shall be particularly interested in the linear class which is dubbed in the literature as the class of Markov jump linear systems (MJLS). The jump mechanism is modeled by a Markov process, which is also known in the literature as the operation mode. The dissertation address the filtering problem of the operation mode for the class of MJLS. Previous result in the literature on this problem has been obtained by Wonham, which has shown the existence of an optimal nonlinear filter for this problem. The main hindrance with Wonham’s result, in the context of the control problem with partial observation of operation mode, is that it introduces a great deal of nonlinearity in the Hamilton-Jacobi- Belman equation, which makes it difficult to get an explicit closed solution for the control problem. Motivated by this, the main contribution of this dissertation is to devise an optimal linear filter for the mode operation, which we believe could be more favorable in the solution of the control problem with partial observations. In addition, relying on Murayama’s stochastic numerical method and the results of Yuan and Mao, we carry out simulation of Wonham’s filter, and the one devised in the dissertation, in order to compare their performances. / As equações diferenciais estocáticas com salto Markoviano constituem uma das clases de sistemas dinâmicos híbridos mais importantes, e tem sido muito usados para modelar sistemas sujeitos a mudanças abruptas na sua estructura. Essas mudanças podem ser devido a, por exemplo, perturbações ambientais, falhas em componentes, volatilidade em sistemas econômicos, mudanças em interconexões de subsistemas, mudanças abruptas em operações de plantas não lineares, etc. Estas falhas podem ser encontradas em sistemas de controle para aeronaves, sistemas robóticos, estructuras grandes e flexíveis em estações espaciais, etc. Nós estamos especialmente interessados na clase de sistemas lineares que é referenciada na literatura como sistemas lineares com salto Markoviano (SLSM). O mecanismo de salto é modelado por um processo de Markov, que é conhecido na literatura como modo de operação do sistema. Essa dissertação visa o problema de filtragem para o modo de operação do sistema linear com salto. Na literatura pode-se encontrar resultados já obtidos para esse problema como é o caso do filtro ótimo não linear deduzido por Wonham. Mas no contexto de controle ótimo com observações parciais do modo de operação, o filtro de Wonham introduz não linearidades na equação de Hamilton-Jacobi-Belman, fazendo com que seja muito complexo obter uma solução fechada para o problema de controle. A principal motivação desta dissertação é deduzir o filtro ótimo linear para o modo de operação, já que esta pode ser uma solução mais favorável para o problema de controle ótimo. Finalmente, usando o método numérico para equações diferenciais estocásticas de Euler-Murayama e o resultado de Yuan e Mao, realizamos a simulação do filtro de Wonham tal como o filtro deduzido neste trabalho, com o objetivo de comparar as respectivas performances.