Output feedback control design techniques are required in practice due to the limited number of sensors/measurements available for feedback. This thesis focuses on output feedback controller design techniques for nonlinear systems subject to different system restrictions. The problem of controlling the heart dynamics in a real time manner is formulated as an adaptive learning output-tracking problem. For a class of nonlinear dynamic systems with unknown nonlinearities and non-affine control input , a Lyapunov-based technique is used to develop a control law. An adaptive learning algorithm is exploited that guarantees the stability of the closed-loop system and convergence of the output tracking error to an adjustable neighborhood of the origin. In addition, good approximation of the unknown nonlinearities is also achieved by incorporating a per- sistent exciting signal in the parameter update law. The effectiveness of the proposed method is demonstrated by an application to a cardiac conduction system modelled by two coupled driven oscillators. An output feedback design technique is developed to achieve semi-global practical stabilization for a class of non-minimum phase nonlinear systems, subject to param- eter uncertainties. This work provides a constructive controller design method for an auxiliary system, whose existence is crucial, but is only assumed in (Isidori, 2000). The control design technique is used to regulate the benchmark van de Vusse reactor. Simulation results demonstrate satisfactory controller performance. The output feedback control design for a class of non-minimum phase nonlinear systems with unknown nonlinearities is studied. The proposed approach is able to combine the two previous design methods and provide a stabilizing output feedback control law. The performance of the proposed method is demonstrated by simulation results. / Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2009-12-22 15:47:05.884

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This dissertation addresses the control problem for the general class of control non-affine, non-standard singularly perturbed continuous-time systems. The problem of control for nonlinear multiple time scale systems is addressed here for the first time in a systematic manner. Toward this end, this dissertation develops the theory of feedback passivation for non-affine systems. This is done by generalizing the Kalman-Yakubovich-Popov lemma for non-affine systems. This generalization is used to identify conditions under which non-affine systems can be rendered passive. Asymptotic stabilization for non-affine systems is guaranteed by using these conditions along with well-known passivity-based control methods. Unlike previous non-affine control approaches, the constructive static compensation technique derived here does not make any assumptions regarding the control influence on the nonlinear dynamical model. Along with these control laws, this dissertation presents novel hierarchical control design procedures to address the two major difficulties in control of multiple time scale systems: lack of an explicit small parameter that models the time scale separation and the complexity of constructing the slow manifold. These research issues are addressed by using insights from geometric singular perturbation theory and control laws are designed without making any assumptions regarding the construction of the slow manifold. The control schemes synthesized accomplish asymptotic slow state tracking for multiple time scale systems and simultaneous slow and fast state trajectory tracking for two time scale systems. The control laws are independent of the scalar perturbation parameter and an upper bound for it is determined such that closed-loop system stability is guaranteed. Performance of these methods is validated in simulation for several problems from science and engineering including the continuously stirred tank reactor, magnetic levitation, six degrees-of-freedom F-18/A Hornet model, non-minimum phase helicopter and conventional take-off and landing aircraft models. Results show that the proposed technique applies both to standard and non-standard forms of singularly perturbed systems and provides asymptotic tracking irrespective of the reference trajectory. This dissertation also shows that some benchmark non-minimum phase aerospace control problems can be posed as slow state tracking for multiple time scale systems and techniques developed here provide an alternate method for exact output tracking.

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.

Este trabalho apresenta algumas contribuições para métodos de controle baseados em dados obtidos em apenas um experimento, a fim de torná-los mais atrativos quanto à aplicação em processos industriais. A partir de dados obtidos em experimentos no processo, os métodos baseados em dados estimam os parâmetros de um controlador de estrutura fixa por meio da minimização do erro entre a saída do sistema real em malha fechada e uma saída desejada, dada por um modelo de referência. O método Virtual Reference Feedback Tuning - VRFT é o método mais expressivo na literatura que estima os parâmetros do controlador usando apenas uma batelada de dados, mas o mesmo apresenta alguns inconvenientes em sua formulação que limitam a sua aplicação. Neste trabalho, o método VRFT é modificado de forma que se obtém um método VRFT flexível, o qual minimiza um critério flexível, pelo qual são estimados tanto os parâmetros do controlador quanto os parâmetros do numerador do modelo de referência; assim, caso a planta que se deseja controlar seja de fase não-mínima, o critério é capaz de estimar esses zeros e os mesmos devem ser incluídos no modelo de referência que será utilizado no projeto do controlador. Além disso, em sistemas com ruído, o método VRFT necessita de uma variável instrumental para que a estimativa dos parâmetros do controlador seja não-polarizada. Para eliminar a necessidade de usar variáveis instrumentais, um novo método de controle baseado em dados é proposto, o qual é descrito sob a ótica de identificação. Este método pode ser visto como a identificação de um sistema, no qual a função de transferência do processo é reparametrizada em função do controlador ideal e do modelo de referência. Além disso, estende-se a teoria de projeto de experimento com solução baseada em restrições LMI para o caso da identificação do controlador ótimo. Todas essas contribuições são ilustradas através de simulações. / This work presents some contributions to data-based control methods where data is obtained in only one experiment, in order to make them more attractive to industrial process applications. Using data from experiments on the process, data-based methods estimate the parameters of fixed structure controller, through the minimization of the error between the closed loop response of the system and the desired response, given by a reference model. The Virtual Reference Feedback Tuning - VRFT method is the most expressive method in the literature that estimates the controller parameters using only one batch of data, but this method presents some inconveniences in its formulation which limit its application. In this work, the VRFT method is modified in order to obtain a flexible VRFT method, which minimizes a flexible criterion, and then obtains the controller parameters together with the parameters related to the reference model numerator; thus, if the plant which we want to control is non-minimum phase, then the criterion is able to estimate these zeros and they need to be included in the reference model that will be used in the control design. Besides, when dealing with noisy systems, the VRFT method needs an instrumental variable so the controller parameters estimate is unbiased. In order to eliminate this necessity, a new data-based control is proposed in this work, which is formulated using identification theory. This method can be seen as the identification of a system, where the process transfer function is reparameterized as a function of the ideal controller and the reference model. Besides, we extend the experimental design theory where the problem is solved using LMI constraints to the case of the optimal controller identification method. All these contributions are illustrated through simulations.

Este trabalho apresenta algumas contribuições para métodos de controle baseados em dados obtidos em apenas um experimento, a fim de torná-los mais atrativos quanto à aplicação em processos industriais. A partir de dados obtidos em experimentos no processo, os métodos baseados em dados estimam os parâmetros de um controlador de estrutura fixa por meio da minimização do erro entre a saída do sistema real em malha fechada e uma saída desejada, dada por um modelo de referência. O método Virtual Reference Feedback Tuning - VRFT é o método mais expressivo na literatura que estima os parâmetros do controlador usando apenas uma batelada de dados, mas o mesmo apresenta alguns inconvenientes em sua formulação que limitam a sua aplicação. Neste trabalho, o método VRFT é modificado de forma que se obtém um método VRFT flexível, o qual minimiza um critério flexível, pelo qual são estimados tanto os parâmetros do controlador quanto os parâmetros do numerador do modelo de referência; assim, caso a planta que se deseja controlar seja de fase não-mínima, o critério é capaz de estimar esses zeros e os mesmos devem ser incluídos no modelo de referência que será utilizado no projeto do controlador. Além disso, em sistemas com ruído, o método VRFT necessita de uma variável instrumental para que a estimativa dos parâmetros do controlador seja não-polarizada. Para eliminar a necessidade de usar variáveis instrumentais, um novo método de controle baseado em dados é proposto, o qual é descrito sob a ótica de identificação. Este método pode ser visto como a identificação de um sistema, no qual a função de transferência do processo é reparametrizada em função do controlador ideal e do modelo de referência. Além disso, estende-se a teoria de projeto de experimento com solução baseada em restrições LMI para o caso da identificação do controlador ótimo. Todas essas contribuições são ilustradas através de simulações. / This work presents some contributions to data-based control methods where data is obtained in only one experiment, in order to make them more attractive to industrial process applications. Using data from experiments on the process, data-based methods estimate the parameters of fixed structure controller, through the minimization of the error between the closed loop response of the system and the desired response, given by a reference model. The Virtual Reference Feedback Tuning - VRFT method is the most expressive method in the literature that estimates the controller parameters using only one batch of data, but this method presents some inconveniences in its formulation which limit its application. In this work, the VRFT method is modified in order to obtain a flexible VRFT method, which minimizes a flexible criterion, and then obtains the controller parameters together with the parameters related to the reference model numerator; thus, if the plant which we want to control is non-minimum phase, then the criterion is able to estimate these zeros and they need to be included in the reference model that will be used in the control design. Besides, when dealing with noisy systems, the VRFT method needs an instrumental variable so the controller parameters estimate is unbiased. In order to eliminate this necessity, a new data-based control is proposed in this work, which is formulated using identification theory. This method can be seen as the identification of a system, where the process transfer function is reparameterized as a function of the ideal controller and the reference model. Besides, we extend the experimental design theory where the problem is solved using LMI constraints to the case of the optimal controller identification method. All these contributions are illustrated through simulations.

Este trabalho apresenta algumas contribuições para métodos de controle baseados em dados obtidos em apenas um experimento, a fim de torná-los mais atrativos quanto à aplicação em processos industriais. A partir de dados obtidos em experimentos no processo, os métodos baseados em dados estimam os parâmetros de um controlador de estrutura fixa por meio da minimização do erro entre a saída do sistema real em malha fechada e uma saída desejada, dada por um modelo de referência. O método Virtual Reference Feedback Tuning - VRFT é o método mais expressivo na literatura que estima os parâmetros do controlador usando apenas uma batelada de dados, mas o mesmo apresenta alguns inconvenientes em sua formulação que limitam a sua aplicação. Neste trabalho, o método VRFT é modificado de forma que se obtém um método VRFT flexível, o qual minimiza um critério flexível, pelo qual são estimados tanto os parâmetros do controlador quanto os parâmetros do numerador do modelo de referência; assim, caso a planta que se deseja controlar seja de fase não-mínima, o critério é capaz de estimar esses zeros e os mesmos devem ser incluídos no modelo de referência que será utilizado no projeto do controlador. Além disso, em sistemas com ruído, o método VRFT necessita de uma variável instrumental para que a estimativa dos parâmetros do controlador seja não-polarizada. Para eliminar a necessidade de usar variáveis instrumentais, um novo método de controle baseado em dados é proposto, o qual é descrito sob a ótica de identificação. Este método pode ser visto como a identificação de um sistema, no qual a função de transferência do processo é reparametrizada em função do controlador ideal e do modelo de referência. Além disso, estende-se a teoria de projeto de experimento com solução baseada em restrições LMI para o caso da identificação do controlador ótimo. Todas essas contribuições são ilustradas através de simulações. / This work presents some contributions to data-based control methods where data is obtained in only one experiment, in order to make them more attractive to industrial process applications. Using data from experiments on the process, data-based methods estimate the parameters of fixed structure controller, through the minimization of the error between the closed loop response of the system and the desired response, given by a reference model. The Virtual Reference Feedback Tuning - VRFT method is the most expressive method in the literature that estimates the controller parameters using only one batch of data, but this method presents some inconveniences in its formulation which limit its application. In this work, the VRFT method is modified in order to obtain a flexible VRFT method, which minimizes a flexible criterion, and then obtains the controller parameters together with the parameters related to the reference model numerator; thus, if the plant which we want to control is non-minimum phase, then the criterion is able to estimate these zeros and they need to be included in the reference model that will be used in the control design. Besides, when dealing with noisy systems, the VRFT method needs an instrumental variable so the controller parameters estimate is unbiased. In order to eliminate this necessity, a new data-based control is proposed in this work, which is formulated using identification theory. This method can be seen as the identification of a system, where the process transfer function is reparameterized as a function of the ideal controller and the reference model. Besides, we extend the experimental design theory where the problem is solved using LMI constraints to the case of the optimal controller identification method. All these contributions are illustrated through simulations.

Microwave lenses support low-phase error, wideband, wide-angle scanning, and true-time delay (TTD) beam forming. They provide ideal performance for applications such as satellites, remote-piloted vehicles, collision-avoidance radars and ultra-wideband communications systems. The emerging printed lenses in recent years have facilitated the advancement of designing high performance but low-profile, light-weight, and small-size beam-forming networks (BFNs). The microwave lens adopts a few beam ports to illuminate the prescribed receiving ports that feed energy into radiating antennas. Multi-beam patterns can be achieved by exciting multiple beam ports at a time. The design process starts with path-length equations from a limited number of beam-port foci assumptions. This constraint does not take into account the amplitude information; however, it allows an initial lens geometry to be solved. The resulted scanning angle of microwave lens is limited by the beam port contour, as such ± 90 degrees. In this dissertation, three contributions are made from the aspects of minimized phase errors, accurate and efficient simulation algorithms, and 360-degree scanning range extension. First, a minimum-phase-error, non-focal lens design method is proposed. It does not require a specific number of foci along the beam contour; however, minimum phase errors for all beam ports are able to be achieved. The proposed method takes into account flexible prescribed geometrical design parameters, and adopts numerical optimization algorithms to perform phase error minimization. Numerical results compared with the published tri-focal and quadru-focal lenses demonstrate the merits of the proposed method. Second, an accurate and fast simulation method for the microwave lens has been developed to predict the phase, amplitude, array factor, and power efficiency performance. The proposed method is compared to both full-wave simulation and measurement. Comparable results have been achieved. Third, a novel method for a 360-degree scanning microwave lens is proposed. This concept uses the beam ports and the receive ports in an interleaving sequence such that adjacent ports alternate beam and receive functions. The result is a lens that produces scanned beams on opposite sides of the structure resulting in a 360-degree scanning range. The structure can use multiple opposing facets or continuous circular-port and radiating-element contours. To prove the concept, a four-facet microstrip lens has been designed, simulated, fabricated, and tested. The comparison between full-wave simulation and measurement has demonstrated good agreement. / Ph. D.

Power distribution networks (PDNs) are conducting structures employed in semiconductor systems with the aim of providing circuits with reliable and constant operating voltage. This network has non-neglible electrical parasitics. Consequently, when digital circuits inside the chip switch, the supply voltage delivered to them does not remain ideal and exhibits spatial and temporal voltage fluctuations. These fluctuations in the supply voltage, known as the power-supply noise (PSN), can affect the functionality and the performance of modern microprocessors. The design of this PDN in the chip is an important part in ensuring power integrity. Modeling and simulation of the PSN in on-chip PDNs is important to reduce the cost of processors. These PDNs have irregular geometries, which affect the PSN. As a result, they have to be modeled. The problem sizes encountered in this simulation are usually large (on the order of millions), necessitating computationally efficient simulation approaches. Existing approaches for this simulation do not guarantee at least one of the following three required properties: computationally efficiency, accuracy, and numerically robustness. Therefore, there is a need to develop accurate, numerically robust, and efficient algorithms for this simulation. For many interconnects (e.g., transmission lines, board connectors, package PDNs), only their frequency responses and SPICE circuits (e.g., nonlinear switching drivers, equivalent circuits of interconnects) terminating them are known. These frequency responses are usually available only up to a certain maximum frequency. Simulating the electrical behavior of these systems is important for the reliable design of microprocessors and for their faster time-to-market. Because terminations can be nonlinear, a transient simulation is required. There is a need for a transient simulation of band-limited frequency-domain data characterizing a multiport passive system with SPICE circuits. The number of ports can be large (greater than or equal to 100 ports). In this simulation, unlike in traditional circuit simulators, normal properties like stability and causality of transient results are not automatically met and have to be ensured. Existing techniques for this simulation do not guarantee at least one of the following three required properties: computationally efficiency for a large number of ports, causality, and accuracy. Therefore, there is a need to develop accurate and efficient time-domain techniques for this simulation that also ensure causality. The objectives of this Ph.D. research are twofold: 1) To develop accurate, numerically robust, and computationally efficient time-domain algorithms to compute PSN in on-chip PDNs with irregular geometries. 2) To develop accurate and computationally efficient time-domain algorithms for the causal cosimulation of band-limited frequency-domain data with SPICE circuits.

Robustness of the closed-loop system has repercussions on both stability and performance, making the study of robustness very important. Fundamentally, the performance and stability of closed-loop systems utilizing state-feedback are tied to that of the observers. The primary goal of this thesis is to develop a robust nonlinear observer and closely examine the usefulness of the observer in the presence of non-collocation and parametric uncertainty and as an integral component in closed-loop control. The usefulness of the observer being investigated depends on robustness, accuracy, computational burden, tunability, ease of design, and ease of implementation on an actual flexible motion system. The design and subsequent integration of the Kalman filter, an optimal observer, into a closed-loop system is well known and systematic. However, there are shortcomings of the Kalman filter in the presence of model uncertainty which are highlighted in this work. Simulation studies are conducted using the Simulation Module in National Instruments LabVIEW 8.5 and experiments are conducted on a physical system consisting of a single flexible link with non-collocation of actuators and sensors using LabVIEW Real Time 8.5. Simulations serve as a means to analyze the performance of the optimal observer and the robust observer by analyzing their dynamic behavior as well as that of the closed-loop system with each observer in place. The focus of experiments is on investigating implementation of the robust observer, including initialization and tuning of observer design parameters off-line and on-line. Simulations verify the robustness properties of the sliding mode observer while experiments show that the robust observer can be implemented at fast control rates and that replacing the Kalman filter with a robust observer has direct ramifications on closed-loop performance.