Este trabalho apresenta os resultados da aplicação de três técnicas de controle utilizadas no projeto e implementação do controle de um manipulador subatuado planar de três juntas em série e de elos rígidos, projetado e construído pela Universidade Carnegie Mellon, EUA. Devido ao alto grau de não-linearidade deste sistema, seria muito difícil implementar um controlador H2, H&#8734 ou H2/H&#8734 que atuasse sozinho. Assim, propõe-se a utilização de um método de controle combinado: torque computado/H2, H&#8734 ou H2/H&#8734. No controle combinado, a porção correspondente ao torque computado lineariza e pré-compensa a dinâmica do modelo da planta nominal, enquanto a porção correspondente ao controle H2, H&#8734 ou H2/H&#8734 realiza uma pós-compensação dos erros residuais, que não foram completamente eliminados pelo método torque computado. Testes de acompanhamento de trajetória e testes de robustez são realizados aqui para comprovar a eficiência destes controladores, com resultados de implementação bastante satisfatórios. / This work presents the application results of three control techniques used for the control design and implementation of a serial planar underactuated manipulator with three joints and rigid links, designed and built by the Carnegie Mellon University, USA. Due to the high non-linearity degree of this system, it would be very difficult to implement an H2, H&#8734 or H2/ H&#8734 control which would actuate on the system by itself. Therefore, it is proposed a combined control method: computed torque/ H2, H&#8734 or H2/H&#8734. In the combined control, the portion corresponding to the computed torque linearizes and pre-compensates the dynamics of the nominal model, while the portion corresponding to the H2, H&#8734 or H2/H&#8734 control realizes a pos-compensation of the residual errors, not completely removed by the computed torque method. Trajetory tracking and robustness tests are performed here to demonstrate the efficiency of these controllers, with very satisfatory implementation results.

Orientador: Celso Pascoli Bottura / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-16T02:56:28Z (GMT). No. of bitstreams: 1 Silva_MarcusPantojada_M.pdf: 634546 bytes, checksum: 9c15ab5bd5cf1715bf9313a7d802d180 (MD5) Previous issue date: 2010 / Resumo:Este trabalho propõe uma metodologia para o controle robusto descentralizado de sistemas não lineares com incertezas variantes no tempo limitadas em norma utilizando um procedimento alternativo ao S-Procedure proposto por Yakubovich. Com base em nosso procedimento, desenvolvemos também uma metodologia de projeto de observadores robustos descentralizados para uma classe de sistemas não lineares variantes no tempo limitados em norma. Com o estado estimado pelo observador descentralizado projetamos um controlador descentralizado por realimentação de saída que estabiliza o sistema. Outro problema que é resolvido nesse trabalho é o projeto de controlador robusto H1 descentralizado para uma classe de sistemas não lineares variantes no tempo limitados em norma sujeitos a distúrbios externos. Exemplos que ilustram aplicações das metodologias propostas são apresentados / Abstract: This work proposes a methodology for decentralized robust control of nonlinear systems with time-varying uncertainties norm bounded using an alternative procedure to the S-Procedure proposed by Yakubovich. Based on our procedure a decentralized robust observer design methodology for a class of time-varying norm bounded nonlinear systems is also developed. With the state estimated by the decentralized observer we design an output feedback decentralized controller that stabilizes the system. Another problem that is solved in this work is the design of a decentralized H1 robust controller for a class of time-varying norm bounded nonlinear systems subject to external disturbances. Examples illustrating the application of the proposed methodologies are presented / Mestrado / Automação / Mestre em Engenharia Elétrica

Orientador: Nancy Lopes Garcia / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-19T13:37:17Z (GMT). No. of bitstreams: 1 Moreira_Lucas_D.pdf: 778475 bytes, checksum: 78934b9baf39cc234f800623a7af5cdf (MD5) Previous issue date: 2012 / Resumo: Inspirados em Collet, Galves e Leonardi (2008), a motivação original deste texto é responder a seguinte questão: é possível recuperar a árvore de contextos de uma cadeia de alcance variável através de uma amostra perturbada da cadeia? Inicialmente, consideramos cadeias binárias de ordem infinita nas quais um dos símbolos pode ser modificado com uma probabilidade pequena e fixada. Provamos que as probabilidades de transição da cadeia perturbada estão uniformemente próximas das probabilidades de transição correspondentes da cadeia original se a probabilidade de contaminação é suficientemente pequena. Por meio deste resultado, fomos capazes de responder afirmativamente à pergunta inicial deste trabalho, ou seja, é possível recuperar a árvore de contextos do processo original mesmo utilizando uma amostra contamina no procedimento de estimação. Com isso, mostramos que o estimador da árvore de contextos utilizado é robusto. Em seguida, consideramos o seguinte modelo: dadas duas cadeias de alcance variável, tomando valores num mesmo alfabeto finito, a cada instante do tempo, o novo processo escolhe aleatoriamente um dos dois processos originais com uma probabilidade grande e fixa. A cadeia obtida dessa maneira pode então ser vista como uma perturbação estocástica da cadeia que está sendo escolhida com probabilidade maior. Para esse modelo, obtivemos resultados semelhantes aos obtidos para o modelo inicial / Abstract: Inspired by Collet, Galves and Leonardi (2008), the original motivation of this paper is to answer the following question: Is it possible to recover the context tree of a length variable chain range through a disturbed sample of chain? Initially consider binary chains of infinite order in which one of the symbols can be modified with a small and fixed probability. We prove that the transition probabilities of the perturbed chain are uniformly close to the corresponding transition probabilities of the original chain if the probability of contamination is small enough. Through this result, we were able to answer affirmatively to the initial question of this work, i.e., it is possible to recover the context tree of the original process using a sample contaminates the estimation procedure. With this, we show that the estimator of the context tree used is robust. Next, consider the following model: given two length variable chains, taking values in the same finite alphabet, at each instant of time, the new process randomly chooses one of the two processes with a large and fixed probability. The chain obtained with greater probability can be seen as a stochastic disturbance of the original chain. For this model, we obtained similar results to the those obtained for the initial model / Doutorado / Estatistica / Doutor em Estatística

This thesis investigates Bicoprime Factorisations (BCFs) and their possible uses in robust control theory. BCFs are a generalisation of coprime factorisations, which have been well known and widely used by the control community over the last few decades. Though they were introduced at roughly the same time as coprime factorisations, they have been largely ignored, with only a very small number of results derived in the literature. BCFs are first introduced and the fundamental theory behind them is developed. This includes results such as internal stability in terms of BCFs, parametrisation of the BCFs of a plant and state space constructions of BCFs. Subsequently, a BCF uncertainty structure is proposed, that encompasses both left and right coprime factor uncertainty. A robust control synthesis procedure is then developed with respect to this BCF uncertainty structure. The proposed synthesis method is shown to be advantageous in the following two aspects: (1) the standard assumptions associated with H-infinity control synthesis are directly fulfilled without the need of loop shifting or normalisation of the generalised plant and (2) any or all of the plant's unstable dynamics can be ignored, thus leading to a reduction in the dimensions of the Algebraic Riccati Equations (AREs) that need to be solved to achieve robust stabilisation. Normalised BCFs are then defined, which are shown to provide many advantages, especially in the context of robust control synthesis. When using a normalised BCF of the plant, lower bounds on the achievable BCF robust stability margin can be easily and directly computed a priori, as is the case for normalised coprime factors. Although the need for an iterative procedure is not completely avoided when designing an optimal controller, it is greatly simplified with the iteration variable being scalar. Unlike coprime factorisations where a single ARE needs to be solved to achieve normalisation, two coupled AREs must be satisfied for a BCF to be normalised. Two recursive methods are proposed to solve this problem. Lastly, an example is presented where the theory developed is used in a practical scenario. A quadrotor Unmanned Aerial Vehicle (UAV) is considered and a normalised BCF controller is designed which in combination with feedback linearisation is used to control both the attitude and position of the vehicle.

Abstract In this thesis a set of new algorithms is introduced for Iterative Learning Control (ILC) and Repetitive Control (RC). Both areas of study are relatively new in control theory, and the common denominator for them is that they concentrate on controlling systems that include either reference signals or disturbances which are periodic. This provides opportunities for using past information or experience so that the control system learns the control action that results in good performance in terms of reference tracking or disturbance rejection. The first major contribution of the thesis is the algebraic analysis of ILC systems. This analysis shows that in the discrete-time case ILC algorithm design can be considered as designing a multivariable controller for a multivariable static plant and the reference signal that has to be tracked is a multivariable step function. Furthermore, the algebraic analysis reveals that time-varying algorithms should be used instead of time-invariant ones in order to guarantee monotonic convergence of the error in norm. However, from the algebraic analysis it is not clear how to select the free parameters of a given ILC algorithm. Hence in this thesis optimisation methods are used to automate this design phase. Special emphasis is placed on the so called Norm-Optimal Iterative Learning Control (NOILC) that was originally developed in (Amann:1996) as a new result it is shown that a convex modification of the existing predictive algorithm will result in a considerable improvement in convergence speed. Because the NOILC algorithm is computationally quite complex, a new set of Parameter-Optimal ILC algorithms are derived that converge under certain assumptions on the original plant. Three of these new algorithms will result in monotonic convergence to zero tracking error for an arbitrary discrete-time, linear, time-invariant plant. This a very strong property that has been earlier reported for only a small number of ILC algorithms. In the RC case it is shown that an existing RC algorithm that has been widely analysed and used in the research literature is in fact highly unrobust if the algorithm is implemented using sampled-data processing. Consequently, in this thesis a new optimality based discrete-time RC algorithm is derived, which converges to zero tracking error asymptotically for an arbitrary linear, time-invariant discrete-time plant under mild controllability and observability conditions.

Abstract Image appearance descriptors are needed for different computer vision applications dealing with, for example, detection, recognition and classification of objects, textures, humans, etc. Typically, such descriptors should be discriminative to allow for making the distinction between different classes, yet still robust to intra-class variations due to imaging conditions, natural changes in appearance, noise, and other factors. The purpose of this thesis is the development and analysis of photometric descriptors for the appearance of real life images. The two application areas included in this thesis are face recognition and texture classification. To facilitate the development and analysis of descriptors, a general framework for image description using statistics of quantized filter bank responses modeling their joint distribution is introduced. Several texture and other image appearance descriptors, including the local binary pattern operator, can be presented using this model. This framework, within which the thesis is presented, enables experimental evaluation of the significance of each of the components of this three-part chain forming a descriptor from an input image. The main contribution of this thesis is a face representation method using distributions of local binary patterns computed in local rectangular regions. An important factor of this contribution is to view feature extraction from a face image as a texture description problem. This representation is further developed into a more precise model by estimating local distributions based on kernel density estimation. Furthermore, a face recognition method tolerant to image blur using local phase quantization is presented. The thesis presents three new approaches and extensions to texture analysis using quantized filter bank responses. The first two aim at increasing the robustness of the quantization process. The soft local binary pattern operator accomplishes this by making a soft quantization to several labels, whereas Bayesian local binary patterns make use of a prior distribution of labelings, and aim for the one maximizing the a posteriori probability. Third, a novel method for computing rotation invariant statistics from histograms of local binary pattern labels using the discrete Fourier transform is introduced. All the presented methods have been experimentally validated using publicly available image datasets and the results of experiments are presented in the thesis. The face description approach proposed in this thesis has been validated in several external studies, and it has been utilized and further developed by several research groups working on face analysis.

Networked control systems (NCSs) are known as distributed control systems (DCSs) which are based on traditional feedback control systems but closed via a real-time communication channel. In an NCS, the control and feedback signals are exchanged among the system’s components in the form of information packages through the communication channel. The research of NCSs is important from the application perspective due to the significant advantages over the traditional point-to-point control. However, the insertion of the communication links would also bring challenges and constraints such as the network-induced delays, the missing packets, and the inter symbol interference (ISI) into the system design. In order to tackle these issues and move a step further toward industry applications, two important design problems are investigated in the control areas: Tracking Control (Chapter 2–Chapter 5) and Signal Estimation (Chapter 6–Chapter8). With the fact that more than 90% of control loops in industry are controlled by proportional-integral-derivative (PID) controllers, the first work in this thesis aims to propose the design algorithm on PID controllers for NCSs. Such a design will not require the change or update of the existing industrial hardware, and it will enjoy the advantages of the NCSs. The second motivation is that, due to the network-induced constraints, there is no any existing work on tuning the PID gains for a general NCS with a state-space model. In Chapter 2, the PID tracking control for multi-variable NCSs subject to time-varying delays and packet dropouts is exploited. The H_infty control is employed to attenuate the load disturbance and the measurement noise. In Chapter 3, the probabilistic delay model is used to design the delay-scheduling tracking controllers for NCSs. The tracking control strategy consists of two parts: (1) the feedforward control can enhance the transient response, and (2) the feedback control is the digital PID control. In order to compensate for the delays on both communication links, the predictive control scheme is adopted. To make full use of the delay information, it is better to use the Markov chain to model the network-induced delays and the missing packets. A common assumption on the Markov chain model in the literature is that the probability transition matrix is precisely known. However, the assumption may not hold any more when the delay is time-varying in a large set and the statistics information on the delays is inadequate. In Chapter 4, it is assumed that the transition matrices are with partially unknown elements. An observer-based robust energy-to-peak tracking controller is designed for the NCSs. In Chapter 5, the step tracking control problem for the nonlinear NCSs is in- vestigated. The nonlinear plant is represented by Takagi-Sugeno (T-S) fuzzy linear model. The control strategy is a modified PI control. With an augmentation technique, the tracking controller design problem is converted into an H_infty optimization problem. The controller parameters can be obtained by solving non-iterative linear matrix inequality conditions. The state estimation problem for networked systems is explored in Chapter 6. At the sensor node, the phenomenon of multiple intermittent measurements is considered for a harsh sensing environment. It is assumed that the network-induced delay is time- varying within a bounded interval. To deal with the delayed external input and the non-delayed external input, a weighted H_infty performance is defined. A Lyapunov- based method is employed to deal with the estimator design problem. When the delay is not large, the system with delayed state can be transformed into delay-free systems. By using the probabilistic delay model and the augmentation, the H_infty filter design algorithm is proposed for networked systems in Chapter 7. Considering the phenomenon of ISI, the signals transmitted over the communication link would distort, that is, the output of the communication link is not the same with the input to the communication link. If the phenomenon occurs in the NCSs, it is desired to reconstruct the signal. In Chapter 8, a robust equalizer design algorithm is proposed to reconstruct the input signal, being robust against the measurement noise and the parameter variations. Finally, the conclusions of the dissertation are summarized and future research topics are presented. / Graduate

This thesis is about a robust filtering method known as the interval Kalman filter (IKF), an extension of the Kalman filter (KF) to the domain of interval mathematics. The key limitation of the KF is that it requires precise knowledge of the system dynamics and associated stochastic processes. In many cases however, system models are at best, only approximately known. To overcome this limitation, the idea is to describe the uncertain model coefficients in terms of bounded intervals, and operate the filter within the framework of interval arithmetic. In trying to do so, practical difficulties arise, such as the large overestimation of the resulting set estimates owing to the over conservatism of interval arithmetic. This thesis proposes and demonstrates a novel and effective way to limit such overestimation for the IKF, making it feasible and practical to implement. The theory developed is of general application, but is applied in this work to the heading estimation of the Springer unmanned surface vehicle, which up to now relied solely on the estimates from a traditional KF. However, the IKF itself simply provides the range of possible vehicle headings. In practice, the autonomous steering system requires a single, point-valued estimate of the heading. In order to address this requirement, an innovative approach based on the use of machine learning methods to select an adequate point-valued estimate has been developed. In doing so, the so called weighted IKF (wIKF) estimate provides a single heading estimate that is robust to bounded model uncertainty. In addition, in order to exploit low-cost sensor redundancy, a multi-sensor data fusion algorithm compatible with the wIKF estimates and which additionally provides sensor fault tolerance has been developed. All these techniques have been implemented on the Springer platform and verified experimentally in a series of full-scale trials, presented in the last chapter of the thesis. The outcomes demonstrate that the methods are both feasible and practicable, and that they are far more effective in providing accurate estimates of the vehicle’s heading than the conventional KF when there is uncertainty in the system model and/or sensor failure occurs.

This dissertation forms part of the ongoing process at UP to model and control the electric arc furniture process. Previous work focused on modelling the furnace process from empirical thermodynamic principles as well as fitting the model to actual plant data. Automation of the process mainly focused on subsystems of the process, for example the electric subsystem and the off-gas subsystem. The modelling effort, especially the model fitting resulted in parameter values that are described with confidence intervals, which gives rise to uncertainty in the model, because the parameters can potentially lie anywhere in the confidence interval space. Robust model predictive control is used in this dissertation, because it can explicityly take the model uncertainty into account as part of the synthesis process. Nominal model predictive control – not taking model uncertainty into account – is also applied in order to determine if robust model predictive control provides any advantages over the nominal model predictive control. This dissertation uses the process model from previous wok together with robust model predictive control to determine the feasibility of automating the process with regards to the primary process variables. Possible hurdles that prevent practical implementation are identified and studied. / Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / MEng / unrestricted

This dissertation forms part of the ongoing process at UP to model and control the electric arc furniture process. Previous work focused on modelling the furnace process from empirical thermodynamic principles as well as fitting the model to actual plant data. Automation of the process mainly focused on subsystems of the process, for example the electric subsystem and the off-gas subsystem. The modelling effort, especially the model fitting resulted in parameter values that are described with confidence intervals, which gives rise to uncertainty in the model, because the parameters can potentially lie anywhere in the confidence interval space. Robust model predictive control is used in this dissertation, because it can explicityly take the model uncertainty into account as part of the synthesis process. Nominal model predictive control – not taking model uncertainty into account – is also applied in order to determine if robust model predictive control provides any advantages over the nominal model predictive control. This dissertation uses the process model from previous wok together with robust model predictive control to determine the feasibility of automating the process with regards to the primary process variables. Possible hurdles that prevent practical implementation are identified and studied. / Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / MEng / unrestricted