A crescente demanda pela melhoria operacional dos processos aliada ao desenvolvimento da tecnologia da informação tornam a utilização de controladores preditivos baseados em modelos (MPC) uma prática comum na indústria. Estes controladores estimam, a partir dos dados de planta e de um modelo do processo, uma sequência de ações de controle que levam as variáveis ao valor desejado de forma otimizada. Dessa forma, dentre os parâmetros de configuração de um MPC, a baixa qualidade do modelo é, indiscutivelmente, a mais importante fonte de degradação de seu desempenho. Este trabalho propõe uma série de metodologias para a avaliação da qualidade do modelo do controlador preditivo, as quais consideram sua velocidade em malha fechada. Tais metodologias são baseadas na filtragem dos erros de simulação a partir função nominal de sensibilidade, e possuem a capacidade de informar o impacto dos problemas de modelagem no desempenho do sistema, além de localizar as variáveis controladas que estão com tais problemas e se os mesmos são provenientes de uma discrepância no modelo ou de um distúrbio não medido. As técnicas ainda possuem a vantagem de serem independentes do setpoint, o que as torna flexível de também serem utilizadas em controladores nos quais as variáveis são controladas por faixas. A abordagem proposta foi testada em dois estudos de caso simulados, sendo eles: a Fracionadora de Óleo Pesado da Shell e a Planta de Quatro tanques Cilíndricos. As técnicas também foram avaliadas em dados de processo da Unidade de Coqueamento Retardado de uma refinaria. Os resultados indicam que as mesmas apresentam resultados coerentes, corroborando seu elevado potencial de aplicação industrial. / The growing demand for operational improvement and the development of information technology make the use of model predictive controllers (MPCs) a common practice in industry. This kind of controller uses past plant data and a process model to estimate a sequence of control actions to lead the variables to a desired value following an optimal policy. Thus, the model quality is the most important source of MPC performance degradation. This work proposes a series of methods to investigate the controller model quality taking into account its closed loop performance. The methods are based on filtering the simulation errors using the nominal sensitivity function. They are capable detect the impact of modeling problems in the controller performance, and also to locate the controlled variables that have such problems and if it is caused by a model-plant mismatch or unmeasured disturbance. The techniques have the advantage to be setpoint independent, making them flexible to be also used in MPCs with controlled variables working by range. The proposed approach was tested in two simulated case studies The Shell Heavy Oil Fractionator Process and The Quadruple-tanks Process. The methods are also evaluated in process data of the Delayed Coking Unit of a Brazilian refinery. Results indicate that the method is technically coherent and has high potential of industrial application.

Recently, photovoltaic (PV) generation is becoming increasingly popular in industrial applications. As a renewable and alternative source of energy they feature superior characteristics such as being clean and silent along with less maintenance problems compared to other sources of the energy. In PV generation, employing a Maximum Power Point Tracking (MPPT) method is essential to obtain the maximum available solar energy. Among several proposed MPPT techniques, the Perturbation and Observation (P&O) and Model Predictive Control (MPC) methods are adopted in this work. The components of the MPPT control system which are P&O and MPC algorithms, PV module and high gain DC-DC boost converter are simulated in MATLAB Simulink. They are evaluated theoretically under rapidly and slowly changing of solar irradiation and temperature and their performance is shown by the simulation results, finally a comprehensive comparison is presented.

A utilização de controladores preditivos multivariáveis na indústria de processos cresceu significativamente nos últimos anos principalmente devido à versatilidade e a confiabilidade que essa ferramenta proporciona em termos de controle avançado. No entanto, aspectos relacionados à aplicação prática do que vem sendo desenvolvido no meio acadêmico possui pouca difusão na indústria devido às configurações clássicas adotadas, como sistemas quadrados (com o mesmo número de variáveis controladas e manipuladas), modelos lineares, utilização de setpoint e etc. diferindo daquilo que está disponível e é amplamente utilizado industrialmente: sistemas não-quadrados (geralmente com mais variáveis controladas do que manipuladas), modelos não-lineares e utilização de soft-constraints. Esse trabalho propõe uma metodologia para ajuste dos parâmetros presentes em um controlador preditivo, atento à variedade de algoritmos presentes na indústria de processos. Essa metodologia se baseia na obtenção do melhor desempenho alcançável para cada cenário de um modelo global do processo, atendendo as restrições de Número de Desempenho Robusto relativo (rRPN), Máxima Sensibilidade e restrições nas ações de controle. Baseado em um desempenho que é alcançável, o modelo linear global é escalonado (assim como a planta não-linear) e os pesos que levam o sistema para a melhor condição operacional são estimados. Essa técnica abrange controladores operando em faixas e/ou setpoint e configurações não-quadradas, i.e., com mais variáveis controladas do que manipuladas. A abordagem proposta foi testada em uma planta de quatro tanques esféricos com aquecimento apresentando resultados coerentes, corroborando seu potencial de aplicação industrial. / Due to their versatility and reliability, Model Predictive Controllers (MPCs) are the standard solution for advanced process control in the process industry. However, there is a gap between the academic developments and the actual industrial applications, since the most academic studies focus on systems working with set-points and same number of manipulated and controlled variables, whereas the industrial application cope with non-squared configuration usually with several controlled variables in ranging and a reduced number of manipulated variables. This work proposes a tuning methodology for the countable parameters presents in a typical industrial predictive controller, considering the variety of the algorithms presents commercially in the process industry. This methodology is based on the estimation of the best attainable performance for each scenario of the global model of the process, constrained by the relative Robust Performance Number (rRPN), Maximal Sensitivity and restrictions in the control actions. Based on a performance that is attainable, the linear global model is scaled (and the non-linear) and the weights that lead the system to the best operational condition are estimated. This technique covers controllers operating in zones of control and/or set-point tracking and non-square configurations, i.e., with more controlled variables than manipulated. The proposed approach was tested in a Quadruple-Spherical tanks heating system presenting coherent results indicating its potential for industrial applications.

À medida que se apresentam grandes avanços tecnológicos nas áreas de instrumentação, controle e acionamento, se torna cada vez mais difundida a utilização de sistemas robóticos para a execução dos mais variados tipos de tarefas, como na exploração de petróleo ou mesmo no transporte de cargas. Desse modo, diversas são as situações em que se torna necessário o uso de sistemas subatuados, despertando o interesse da comunidade científica, quer seja pela variadas situações em que se pode utilizar esse tipo de robô ou mesmo pelo desafio que se apresenta o desenvolvimento de estratégias de controle de tais sistemas. Nesta tese propõe-se um modelo de robô bracejador, juntamente com o desenvolvimento dos modelos matemáticos que descrevem o comportamento cinemático e dinâmico desse robô e a respectiva análise desses modelos. Além disso, o presente trabalho tem por objetivo apresentar uma estrutura de controle em malha fechada que seja capaz de fazer com que o robô se desloque ao longo de uma linha horizontal. Diferentes estratégias de controle já foram apresentadas para o controle de robôs bracejadores, mas, em sua maioria, possuem limitações quanto ao tipo de bracejamento que o robô pode executar, além de não considerarem nenhuma restrição no sistema. Dessa maneira, emprega-se a estratégia de controle preditivo, com horizonte de predição deslizante, a qual permite que, para o cálculo da lei de controle, sejam consideradas restrições às variáveis de estado e de entrada durante a solução do problema de otimização. A partir da definição do objetivo e da abordagem de controle a ser utilizada, várias simulações são realizadas com o intuito de validar a aplicação do controlador preditivo para o controle do robô bracejador, sendo o robô capaz de executar diferentes tipos de bracejamento (tanto bracejamento único quanto bracejamento contínuo, do tipo underswing e over hand) ao longo da linha de sustentação. São desenvolvidas duas versões para o controlador proposto, uma baseada em modelo não-linear da dinâmica para ser utilizado no horizonte de predição e outra considerando uma versão linearizada para o modelo da dinâmica. Os resultados obtidos pelas diferentes simulações mostram que a solução proposta para o problema de movimentar o robô bracejador atingiu seus objetivos de modo bastante eficiente, possiblitando, inclusive, a realização de simulações que atendessem a requisitos de tempo real. / As we are observing, the fast technological development in the fields of instrumentation, control and actuation are increasing the employment of robotic systems for the execution of a large variety of tasks, for example, oil exploration or load transportation. In this way, there are many situations where it is necessary to use underactuated systems, which are getting attention of the scientific community due to the different applications of underactuated robots or even due to the challenge to design control strategies for such systems. In this thesis we propose a brachiation robot model, with the derivation of mathematical models for its kinematics and dynamics and analyse such models. Moreover, the aim of this work is to propose a closed loop control architecture that will drive the robot to move along the horizontal line. Many different control schemes have already been proposed in the literature to control brachiation robots, however, most of such schemes are limited concerning the way the robot executes the brachiation movement. Moreover those control strategies are not able to deal with constraints on the state and/or control variables. Thus, we present in this thesis a control scheme based on the predictive control strategy, with receding horizon, which can take into account such constraints during the solution of the optimization problem for the control input computation. After defining the task to be executed and the control strategy to be used, we have simulated different situations of the robot aiming the validation the employment of the predictive control approach for the brachiation robot. The robot is able to execute different types of brachiation (only one cicle or continuously motion, with under-swing and over hand motion) along the supporting line. We have developed two versions to the proposed controller, the first one considering a nonlinear dynamic model during the prediction horizong and the second considering a linearized dynamic model for prediction. The results from the different simulation show that the solution presented in this work for the brachiation robot motion was successful, making the robot able to move from one position to a forwarded position in the line. Furthermore, simulations have indicated the overall system can be executed under real time requirements.

The proposed dissertation research investigates Optimization and Control for Microgrid and Power Electronic Converters. The research has two major parts: i- Microgrid Operation and Control, ii- Power Electronic Converter Control and Optimization. In the first part, three focuses are investigated. First, a completely distributed algorithm is developed for dc optimal power flow problem for power distribution systems as one of the necessary functions considered in unit-commitment problem in day-ahead markets. This method is derived based upon the partial primal-dual representation of the economic dispatch problem, which is finally translated to DC-OPF problem. Second, the optimal interaction between the utility and communities will be studied, due to its improtance in real-time markets. The objective of this section will be to develop an iterative agent-based algorithm for optimal utility-community control. The algorithm will consider the AC power system constraints to maintain power system stability. In this algorithm, a simplified model of microgrid is considered. In the third focus, a comprehensive model of microgrid is taken into account. The optimal operation of the microgrid considering energy storage systems and renewable energy resources is investigated. The interaction of such microgrids with the main grid to define the optimal operation of the entire embedded system is studied through two iterative methods. In the microgrid's internal problem, a moving-horizon algorithm is considered to define the optimal dispatch of all distributed energy resources while considering the time-correlated constraints of energy storage systems. A thorough analysis of the effects of the size of storage systems on energy and reserve market parameters are also performed. In the second part, the focus of research is to develop optimal control strategies for Power Electronic Converters. A Model Predictive Control (MPC) switching method is proposed for Modular Multilevel Converters (MMC). The optimal solution of MPC problem is then represented as an optimization problem. Due to lack of efficient algorithms to seek the optimal solution, a fast algorithm will be proposed in this research. The method proposed reduces the number of possible solutions and computation efforts dramatically.

Une vallée hydroélectrique est constituée d'un nombre important de centrales interconnectées du fait de l'utilisation de la même ressource en eau. Pour pouvoir optimiser en temps réel sa production, il a été proposé dans cette thèse d'utiliser les méthodes associées aux systèmes à grande échelle pour développer les outils nécessaires. Cette étude de la commande globale du système a été orientée vers l'utilisation des méthodes de décomposition-coordination. Ces méthodes ont été examinées et appliquées à un cas d'étude simplifié (une partie de la vallée hydraulique) mis à disposition par EDF. Plus particulièrement, les méthodes de décomposition-coordination par les prix, ou encore par les prédictions des interactions, s'appuyant sur des commandes MPC, ont été considérées et comparées avec une commande centralisée. En vue d'une implémentation temps-réel, nous nous sommes intéressés à exprimer les problèmes d'optimisation comme des problèmes QP, pour ensuite obtenir des solutions explicites en utilisant une méthodologie de caractérisation géométrique. Nous avons proposé des formulations complètement explicites (niveau coordinateur et sous-systèmes) pour les deux méthodes. Des résultats de simulation avec des données réelles mises à disposition par EDF sont présentés. Afin de valider les méthodes conçues, une première phase d'implantation sur la plate-forme Supervision NG d'EDF permettant la communication avec un modèle de la vallée hydroélectrique (basé sur les équations de Saint Venant et la bathymétrie de la rivière), est enfin incluse dans ce mémoire. / This study is mainly about the hydroelectric production problem. What we aim to do, is to develop optimization tools for a chain of hydroelectric plants, using appropriate control methodologies. A hydroelectric valley is a large scale system, made up of interconnected plants. The study of the global control system has been focused to the use of decomposition-coordination methods. Those methods have been examined and applied to a simplified case study (a part of a hydroelectric valley) given by EDF. To be more specific, the price decomposition - coordination method and the interactions prediction method, based on MPC controls, have been considered and compared to a centralized control. Because of the need of implementation in real time, we have expressed the optimization problems as QP problems, so as to obtain explicit solutions using the geometric characterization methodology. We have proposed a completely explicit formulation (both at the coordinator level and at the subsystems level) for both methods. Simulation results with real data information given by EDF are also presented. To verify and validate the designed methods, a first step of implementation on the supervision platform NG by EDF, that allows the communication with a model of the hydroelectric valley (based on the equations of Saint Venant and on the river bathymetry) is finally also included in this thesis.

Fundação de Amparo a Pesquisa do Estado de Minas Gerais / The industry's need to improve aspects of production such as quality and eciency meant that techniques and devices for more ecient control were adopted in view of the dierences that occur in systems. most systems are not only characterized by the continuous dynamics usually applied to describe this behavior but an association with this dynamic elements with discrete characteristics (logical). For this systems relates to the expression hybrid systems whose dynamics processes characterize the behavior continuous real-time system associated with a discrete event. The control of hybrid systems requires answers in accordance with the presence of discrete event and continuous interacting that makes control strategies increasingly sophisticated are developed. In this work, was adopted the hybrid MLD formalism (Mixed Logical Dynamical ) for the representation of hybrid systems using dynamic logical propositions that express such a system in the form of linear constraints of binary variables interacting with the continuous behavior of dierential equations. In this context, the predictive controller based on an optimal model developed from the selected formalism is suggested as an option for the control of hybrid systems. In this work, the qualitative modeling of discrete systems using the MLD formalism is presented for which it is developed to check these systems to evaluate the hybrid modeling. These techniques were applied to simple problems in chemical engineering as systems of three tanks connected in series and a CSTR reactor with heating. In each of the systems were made simulations coupled to qualitative models in various scenarios and compared to the continuous model of dierential equations and their discretized version. The action of the MPC controller for hybrid systems was studied also, developing it from the MLD model and presenting several simulation scenarios to investigate the eects of control over the system. Such control is developed to the l1 and l2 norms, enabling a comparison between the two control options and the comparison between these controllers with classics like PI. The results for the model in the MLD formalism are satisfactory and consistent with the real behavior of the system. The control problems developed show intervention MPC controller more ecient than the classical PI controller associated with the control on/o mainly by the choice of variables to be manipulated. The comparison between controllers l1-MPC and l2-MPC gives results that indicate similar control actions for each of these controllers depending on the location of the optimal point found in solving the optimization model for the controlled system. / A necessidade da indústria de aprimorar aspectos da produção como qualidade e eficiência fizeram com que técnicas e dispositivos de controle mais eficientes fossem adotados atendendo às diversidades que ocorrem nos sistemas. Porém, a maioria dos sistemas não caracteriza-se apenas pela dinâmica contínua geralmente aplicada a descrição de seu comportamento mas por uma associação desta dinâmica com elementos com características discretas (lógicas). A este tipo de sistemas relaciona-se a expressão sistemas híbridos que caracteriza processos cuja dinâmica associa o comportamento contínuo em tempo real de um sistema com eventos discretos. O controle de sistemas híbridos exige respostas condizentes com a presença de eventos discretos e contínuos interagindo entre si, o que faz com que estratégias de controle cada vez mais sofisticadas sejam desenvolvidas. Neste trabalho, adotou-se o formalismo híbrido MLD Mixed Logical Dynamical para a representação de sistemas híbridos dinâmicos utilizando proposições lógicas que expressam tal sistema sob a forma de restrições lineares de variáveis binárias interagindo com o comportamento contínuo de equações diferenciais. Neste contexto, o controlador preditivo baseado em um modelo desenvolvido a partir do formalismo selecionado é sugerido como uma opção para o controle de sistemas híbridos. Nesta dissertação, a modelagem qualitativa dos sistemas discretos utilizando o formalismo MLD é apresentada para a qual desenvolve-se a verificação para tais sistemas com o objetivo de avaliar a modelagem híbrida. Essas técnicas foram aplicadas em problemas da Engenharia Química como sistemas de três tanques conectados em série e um reator CSTR com aquecimento. Em cada um dos processos estudados foram feitas as simulações acopladas aos modelos qualitativos em diversos cenários e comparados ao modelo contínuo de equações diferenciais e sua versão discretizada. Estudou-se ainda a ação do controlador MPC para sistemas híbridos, desenvolvendo-o a partir do modelo MLD e apresentando diversos cenários de simulação para investigação dos efeitos do controle sobre o sistema. Tal controle é desenvolvido para as normas l 1 e l 2, possibilitando uma comparação entre as duas opções de controle além da comparação entre estes com controladores clássicos como o PI. Os resultados para o modelo no formalismo MLD são satisfatórios e condizem com o comportamento real do sistema. Os problemas de controle desenvolvidos apresentam a intervenção do controlador MPC mais eficiente que o controlador clássico PI associado ao controle liga/desliga principalmente pela possibilidade de escolha das variáveis a ser manipulada. A comparação com entre os controladores l 1-MPC e l 2- MPC permite resultados que indicam ações de controle semelhantes para cada um destes controladores em função da localização do ponto ótimo encontrado na resolução do modelo de otimização para o sistema de controlados / Mestre em Engenharia Química

A crescente demanda pela melhoria operacional dos processos aliada ao desenvolvimento da tecnologia da informação tornam a utilização de controladores preditivos baseados em modelos (MPC) uma prática comum na indústria. Estes controladores estimam, a partir dos dados de planta e de um modelo do processo, uma sequência de ações de controle que levam as variáveis ao valor desejado de forma otimizada. Dessa forma, dentre os parâmetros de configuração de um MPC, a baixa qualidade do modelo é, indiscutivelmente, a mais importante fonte de degradação de seu desempenho. Este trabalho propõe uma série de metodologias para a avaliação da qualidade do modelo do controlador preditivo, as quais consideram sua velocidade em malha fechada. Tais metodologias são baseadas na filtragem dos erros de simulação a partir função nominal de sensibilidade, e possuem a capacidade de informar o impacto dos problemas de modelagem no desempenho do sistema, além de localizar as variáveis controladas que estão com tais problemas e se os mesmos são provenientes de uma discrepância no modelo ou de um distúrbio não medido. As técnicas ainda possuem a vantagem de serem independentes do setpoint, o que as torna flexível de também serem utilizadas em controladores nos quais as variáveis são controladas por faixas. A abordagem proposta foi testada em dois estudos de caso simulados, sendo eles: a Fracionadora de Óleo Pesado da Shell e a Planta de Quatro tanques Cilíndricos. As técnicas também foram avaliadas em dados de processo da Unidade de Coqueamento Retardado de uma refinaria. Os resultados indicam que as mesmas apresentam resultados coerentes, corroborando seu elevado potencial de aplicação industrial. / The growing demand for operational improvement and the development of information technology make the use of model predictive controllers (MPCs) a common practice in industry. This kind of controller uses past plant data and a process model to estimate a sequence of control actions to lead the variables to a desired value following an optimal policy. Thus, the model quality is the most important source of MPC performance degradation. This work proposes a series of methods to investigate the controller model quality taking into account its closed loop performance. The methods are based on filtering the simulation errors using the nominal sensitivity function. They are capable detect the impact of modeling problems in the controller performance, and also to locate the controlled variables that have such problems and if it is caused by a model-plant mismatch or unmeasured disturbance. The techniques have the advantage to be setpoint independent, making them flexible to be also used in MPCs with controlled variables working by range. The proposed approach was tested in two simulated case studies The Shell Heavy Oil Fractionator Process and The Quadruple-tanks Process. The methods are also evaluated in process data of the Delayed Coking Unit of a Brazilian refinery. Results indicate that the method is technically coherent and has high potential of industrial application.

A utilização de controladores preditivos multivariáveis na indústria de processos cresceu significativamente nos últimos anos principalmente devido à versatilidade e a confiabilidade que essa ferramenta proporciona em termos de controle avançado. No entanto, aspectos relacionados à aplicação prática do que vem sendo desenvolvido no meio acadêmico possui pouca difusão na indústria devido às configurações clássicas adotadas, como sistemas quadrados (com o mesmo número de variáveis controladas e manipuladas), modelos lineares, utilização de setpoint e etc. diferindo daquilo que está disponível e é amplamente utilizado industrialmente: sistemas não-quadrados (geralmente com mais variáveis controladas do que manipuladas), modelos não-lineares e utilização de soft-constraints. Esse trabalho propõe uma metodologia para ajuste dos parâmetros presentes em um controlador preditivo, atento à variedade de algoritmos presentes na indústria de processos. Essa metodologia se baseia na obtenção do melhor desempenho alcançável para cada cenário de um modelo global do processo, atendendo as restrições de Número de Desempenho Robusto relativo (rRPN), Máxima Sensibilidade e restrições nas ações de controle. Baseado em um desempenho que é alcançável, o modelo linear global é escalonado (assim como a planta não-linear) e os pesos que levam o sistema para a melhor condição operacional são estimados. Essa técnica abrange controladores operando em faixas e/ou setpoint e configurações não-quadradas, i.e., com mais variáveis controladas do que manipuladas. A abordagem proposta foi testada em uma planta de quatro tanques esféricos com aquecimento apresentando resultados coerentes, corroborando seu potencial de aplicação industrial. / Due to their versatility and reliability, Model Predictive Controllers (MPCs) are the standard solution for advanced process control in the process industry. However, there is a gap between the academic developments and the actual industrial applications, since the most academic studies focus on systems working with set-points and same number of manipulated and controlled variables, whereas the industrial application cope with non-squared configuration usually with several controlled variables in ranging and a reduced number of manipulated variables. This work proposes a tuning methodology for the countable parameters presents in a typical industrial predictive controller, considering the variety of the algorithms presents commercially in the process industry. This methodology is based on the estimation of the best attainable performance for each scenario of the global model of the process, constrained by the relative Robust Performance Number (rRPN), Maximal Sensitivity and restrictions in the control actions. Based on a performance that is attainable, the linear global model is scaled (and the non-linear) and the weights that lead the system to the best operational condition are estimated. This technique covers controllers operating in zones of control and/or set-point tracking and non-square configurations, i.e., with more controlled variables than manipulated. The proposed approach was tested in a Quadruple-Spherical tanks heating system presenting coherent results indicating its potential for industrial applications.

The increasing reliance on oceans, rivers and waterways in a spectrum of human activities have demonstrated the large demand for advanced marine technologies that facilitate multifarious in-water services and tasks. The autonomous underwater vehicle (AUV) is a representative marine technology which has been contributing continuously to many ocean-related fields. An elaborate control system is essential to AUVs. However, AUVs present difficult control system design problems due to their nonlinear dynamics, the unpredictable environment and the poor knowledge about the hydrodynamic coupling of the vehicle degrees of freedom. When designing the motion controller, the practical constraints on the AUV system such as limited perceiving, computing and actuating capabilities should also be respected. The model predictive control (MPC) is an advanced control technology that leverages optimization to calculate the control command. Thanks to the optimization nature, MPC can conveniently handle the complex nonlinearity in system dynamics as well as the state and control constraints. MPC takes the receding horizon control paradigm which gains satisfactory robustness against model uncertainties and external disturbances. Therefore, MPC is an ideal candidate for solving the AUV motion control problems. On the other hand, since the optimization is solved by iterative numerical algorithms, the obtained control signal is an implicit function of the system state, which complicates the characterization of the closed-loop properties. Moreover, the nonlinear system dynamics makes the online optimization nonlinear programming (NLP) problems. The high computational complexity may cause an issue on the real-time control for embedded platforms with limited computing resources. In order to push the advanced MPC technology towards real-world AUV applications, this PhD dissertation is concerned with fundamental AUV motion control problems and attempts to address the aforementioned challenges and provide novel solutions. This dissertation proceeds with Chapter 1 by providing state-of-the-art introductions to related research areas. The mathematical model used for the AUV motion control is elaborated in Chapter 2. In Chapter 3, we consider the AUV navigation and control problem in constrained workspace. A unified receding horizon optimization framework consisting of the dynamic path planning and the nonlinear model predictive control (NMPC) tracking control is developed. Although the NMPC tracking controller well accommodates the practical constraints on the AUV system, it presents a brand new design philosophy compared with the existing control systems that are implemented on real AUVs. Since the existing AUV control systems are reliable controllers, AUV practitioners tend not to fully replace them but to improve the control performance by adding features. By considering this, in Chapter 4, we develop the Lyapunov-based model predictive control (LMPC) scheme which builds on the existing AUV control system and invoke online optimization to improve the control performance. Chapter 5 focuses on the path following (PF) problem. Unlike the trajectory tracking control which equally emphasizes the spatial and temporal control objectives, the PF control often prioritizes the path convergence over the speed assignment. To incorporate this objective prioritization into the controller design, a novel multi-objective model predictive control (MOMPC) scheme is developed. While the MPC technique provides several salient features (e.g., optimality, constraints handling, objective prioritization, robustness, etc.), those features come at a price: a computational bottleneck is formed by the heavy burden of solving online optimizations in real time. To explicitly address this issue, in Chapter 6, the computational complexity of the MPC algorithms is particularly emphasized. Two novel strategies which potentially alleviate the computational burden of the MPC-based AUV tracking control are proposed. In Chapter 7, some conclusive remarks are provided and a few avenues for future research are identified. / Graduate