This work enables accelerated fluid recovery in oil and gas reservoirs by automatically controlling fluid height and bottomhole pressure in wells. Several literature studies show significant increase in recovered oil by determining a target bottomhole pressure but rarely consider how to control to that value. This work enables those benefits by maintaining bottomhole pressure or fluid height. Moving Horizon Estimation (MHE) determines uncertain well parameters using only common surface measurements. A Model Predictive Controller (MPC) adjusts the stroking speed of a sucker rod pump to maintain fluid height. Pump boundary conditions are simulated with Mathematical Programs with Complementarity Constraints (MPCCs) and a nonlinear programming solver finds a solution in near real-time. A combined rod string, well, and reservoir model simulate dynamic well conditions, and are formulated for simultaneous optimization by large-scale solvers. MPC increases cumulative oil production vs. conventional pump off control by maintaining an optimal fluid level height.

Optimization has enabled automated applications in chemical manufacturing such as advanced control and scheduling. These applications have demonstrated enormous benefit over the last few decades and continue to be researched and refined. However, these applications have been developed separately with uncoordinated objectives. This dissertation investigates the unification of scheduling and control optimization schemes. The current practice is compared to early-concept, light integrations, and deeper integrations. This quantitative comparison of economic impacts encourages further investigation and tighter integration. A novel approach combines scheduling and control into a single application that can be used online. This approach implements the discrete-time paradigm from the scheduling community, which matches the approach of the control community. The application is restricted to quadratic form, and is intended as a replacement for systems with linear control. A novel approach to linear time-scaling is introduced to demonstrate the value of including scaled production rates, even with simplified equation forms. The approach successfully demonstrates significant benefit. Finally, the modeling constraints are lifted from the discrete-time approach. Time dependent constraints and parameters (like time-of-day energy pricing) are introduced, enabled by the discrete-time approach, and demonstrate even greater economic value. The more difficult problem calls for further exploration into the relaxation of integer variables and initialization techniques for faster, more reliable solutions. These applications are also capable of replacing both scheduling and control simultaneously. A generic CSTR application is used throughout as a case study on which the integrated optimization schemes are implemented. CSTRs are a common model for applications in most chemical engineering industries, from food and beverage, to petroleum and pharmaceuticals. In the included case study results, segregated control and scheduling schemes are shown to be 30+% less profitable than fully unified approaches during operational periods around severe disturbances. Further, inclusion of time-dependent parameters and constraints improved the open-loop profitability by an additional 13%.

Robots have been a revolutionizing force in manufacturing in the 20th and 21st century but have proven too dangerous around humans to be used in many other fields including medicine. We describe a new control algorithm for robots developed by the Brigham Young University Robotics and Dynamics and Robotics Laboratory that has shown potential to make robots less dangerous to humans and suitable to work in more applications. We analyze the computational complexity of this algorithm and find that it could be a feasible control for even the most complicated robots. We also show conditions for a system which guarantee local stability for this control algorithm.

Soft robotic platforms are becoming increasingly popular as they are generally safer, lighter, and easier to manufacture than their more rigid, heavy, traditional counterparts. These soft platforms, while inherently safer, come with significant drawbacks. Their compliant components are more difficult to model, and their underdamped nature makes them difficult to control. Additionally, they are so lightweight that a payload of just a few pounds has a significant impact on the manipulator dynamics. This thesis presents novel methods for addressing these issues. In previous research, Model Predictive Control has been demonstrably useful for joint angle control for these soft robots, using a rigid inverted pendulum model for each link. A model describing the dynamics of the entire arm would be more desirable, but with high Degrees of Freedom it is computationally expensive to optimize over such a complex model. This thesis presents a method for simplifying and linearizing the full-arm model (the Coupling-Torque method), and compares control performance when using this method of linearization against control performance for other linearization methods. The comparison shows the Coupling-Torque method yields good control performance for manipulators with seven or more Degrees of Freedom. The decoupled nature of the Coupling-Torque method also makes adaptive control, of the form described in this thesis, easier to implement. The Coupling-Torque method improves performance when the dynamics are known, but when a payload of unknown mass is attached to the end effector it has a significant impact on the dynamics. Adaptive Control is needed at this point to compensate for the model's poor approximation of the system. This thesis presents a method of layering Model Reference Adaptive Control in concert with Model Predictive Control that improves control performance in this scenario. The adaptive controller modifies dynamic parameters, which are then delivered to the optimizer, which then returns inputs for the system that take all of this information into account. This method has been shown to reduce step input tracking error by 50% when implemented on the soft robot.

Research in soft robot hardware has led to the development of platforms that allow for safer performance when working in uncertain or dynamic environments. The potential of these platforms is limited by the lack of proper dynamic models to describe or controllers to operate them. A common difficulty associated with these soft robots is a representation for torque, the common electromechanical relation seen in motors does not apply. In this thesis, several different torque models are presented and used to construct linear state-space models. The control limitations on soft robots are induced by natural compliance inherent to the hardware. This inherent compliance results in soft robots that are commonly underdamped and present significant oscillations when accelerated quickly. These oscillations can be mitigated through model-based controllers which can anticipate these oscillations. In this thesis, multiple model predictive controllers are implemented with the torque models produced and results are presented for an inflatable single-DoF pneumatically actuated soft robot. Larger, multi-DoF, soft robots present additional issues with control, where flexibility in one joint impacts control in others. In this thesis a preliminary method and results for controlling multiple joints on an inflatable multi-DoF pneumatically actuated soft robot are presented. While model predictive controllers are capable, their control commands are defined by solving an optimization constrained by model dynamics. This optimization relies on minimizing the cost of a user-defined objective function. This objective function contains a series of weights, which allow the user to tune the importance of each component in the objective function. As there are no calculations that can be performed to tune model predictive controllers to achieve superior control performance, they often need to be tuned tediously by a skilled operator. In this thesis, a method for automated discrete performance identification and model predictive controller weight tuning is presented. This thesis constructs multiple state-space models for single- and multi-DoF underdamped, antagonistic, pneumatically actuated soft robots and shows that these models can be used with model predictive control, tuned for performance, to achieve accurate joint position control.

Este trabalho tem como objetivo estudar as características de uma coluna depropanizadora e propor uma estrutura de controle para esta planta. Essa coluna está localizada na unidade de craqueamento catalítico da Refinaria Presidente Bernardes, em Cubatão. O objetivo de controle é a especificação de um valor máximo de butano e componentes mais pesados (C4+) na corrente de topo e um valor máximo de propano e componentes mais leves (C3-) na corrente de fundo. Uma simulação da planta foi construída por meio do simulador de processos AspenOne® e os modelos referentes a vários pontos de operação e duas composições de carga distintas foram obtidos através da simulação integrada entre o Aspen® e o Simulink®. O software Matlab(TM) foi utilizado para executar o algoritmo de controle. O controlador aqui proposto é um IHMPC (Infinite Horizon Model Predictive Control) adaptado para sistemas com tempo morto e com faixas nas variáveis controladas. As incertezas na modelagem foram representadas por um conjunto de modelos lineares. Adicionalmente o controlador contém, na mesma camada, um componente de otimização econômica linear com o objetivo de minimizar o gasto energético do sistema ou até mesmo maximizar a pureza do destilado. As simulações permitiram que as estratégias de controle pudessem ser testadas e seus resultados discutidos. A análise dos testes mostra que o IHMPC aqui proposto é capaz de controlar a planta nos possíveis pontos de operação com um bom desempenho. / The objective of this work is to study the characteristics of a depropanizer column and to propose a predictive control structure for this plant. This column is located at the fluid catalytic cracking unit of the Presidente Bernardes Refinery, in Cubatão. The control objective of these columns is the specification of a maximum value of butane and heavier components (C4+) in the top stream and the maximum value of propane and lighter components (C3-) in the bottom stream. The plant was represented through the process simulator AspenOne® and the models for several operating points and two different feed compositions were obtained through the integrated simulation of Aspen® and Simulink®. The software Matlab(TM) was used to run the control algorithm. The controller proposed here is based on the IHMPC (Infinite Horizon Model Predictive Control) that was extended to time delayed systems and zone control. The model uncertainties are approximated by a set of linear models. In addition, the controller contains, in the same layer, an economic objective, which aims to minimize the energy contents of the operation and to maximize the purity of the distillate. The simulation allowed that the control strategies could be tested and the results discussed. The analysis of the tests showed that the proposed IHMPC is able to control the plant with acceptable performance.

Exploiting high altitude wind energy using power kites is an emerging topic in the field of renewable energy. The claimed advantages of power kites over traditional wind power technologies are the lower construction costs, less land occupation and more importantly, the possibility of efficiently harvesting wind energy at high altitudes, where more dense and steady wind power exists. One of the most challenging issues to bring the power kite concept to real industrialization is the controller design. While traditional wind turbines can be inherently stabilized, the airborne nature of kites causes a strong instability of the systems. This thesis aims to develop a novel economic model predictive path-following control (EMPFC) framework to tackle the path-following control of power kites, as well as provide insightful stability analysis of the proposed control scheme. Chapter 3 is focused on the stability analysis of EMPFC. We proceed with a sampled-data EMPC scheme for set-point stabilization problems. An extended definition of dissipativity is introduced for continuous-time systems, followed by giving sufficient stability conditions. Then, the EMPFC scheme for output path-following problems is proposed. Sufficient conditions that guarantee the convergence of the system to the optimal operation on the reference path are derived. Finally, an example of a 2-DoF robot is given. The simulation results show that under the proposed EMPFC scheme, the robot can follow along the reference path in forward direction with enhanced economic performance, and finally converges to its optimal steady state. In Chapter 4, the proposed EMPFC scheme is applied to a challenging nonlinear kite model. By introducing additional degrees of freedom in the zero-error manifold (i.e., the space where the output error is zero), a relaxation of the optimal operation is achieved. The effectiveness of the proposed control scheme is shown in two aspects. For a static reference path, the generated power is increased while the kite is stabilized in the neighborhood of the reference path. For a dynamic reference path, the economic performance can be further enhanced since parameters for the reference path are treated as additional optimization variables. The proposed EMPFC achieves the integration of path optimization and path-following, resulting in a better economic performance for the closed-loop system. Simulation results are given to show the effectiveness of the proposed control scheme. Finally, Chapter 5 concludes the thesis and future research topics are discussed. / Graduate / 2020-05-14

La plupart des applications des avions drones sont liées à l'observation d'événements au sol. En particulier, les suivi de cibles terrestres mobiles, qu'elles soient statiques, lentes ou rapides, est une tâche essentielle pour un drone. L'objectif global de la thèse est de proposer des méthodes qui permettent à un drone de suivre une cible terrestre, dans les conditions suivantes: - Le drone est de type voilure fixe équipé d'une caméra monoculaire. - Présence d'obstacles qui occultent la visibilité de zones au sol. - Existence de zones d'exclusion aérienne qui limitent le mouvement aérien. - Restrictions sur le champ de vue du capteur qui assure le suivi (caméra) - Différents comportements de la cible : elle peut évoluer librement ou sous contraintes dynamiques (cas d'une voiture par exemple), et peut être neutre ou évasive~: dans ce dernier cas, elle peut exploiter la présence d'obstacles pour éviter d'être perçue par le drone. Trois approches pour aborder ce problème sont proposées dans la thèse : - Une méthode basée aux lois de contrôle et de la navigation, - Une méthode basée sur la prédiction des déplacements de la cible, - Et une approche basée sur la théorie des jeux. Des résultats obtenus par des simulations réalistes et avec un drone sont présentés, pour évaluer et comparer les avantages et inconvénients de chacune des approches. Des extensions au cas "multi-drones" sont aussi proposées.

<p>This thesis has been conducted at Saab Bofors Dynamics AB. The purpose was to investigate if a non-linear missile model could be stabilized when the optimal control signal is computed considering constraints on the control input. This is particularly interesting because the missile is controlled with rudders that have physical bounds. This strategy is called Model Predictive Control. Simulations are conducted to compare this strategy with others; firstly simulations with step responses and secondly simulations when the missile is supposed to hit a moving target. The latter is performed to show that the missile can be stabilized in its whole area of operation. The simulations show that the controller indeed can stabilize the missile for the given scenarios. However, this control strategy does not show any obvious improvements in comparison with alternative ones.</p>

<p>The number of MPC installations in industry is growing as a reaction to demands of increased efficiency. An MPC controller uses an internal plant model to run real-time predictive optimization of future inputs. If a discrepancy between the internal plant model and the plant exists, control performance will be affected. As time from commissioning increases the model accuracy tends to deteriorate. This is natural as the plant changes over time. It is important to detect these changes and re-identify the plant model to maintain control performance over time. A method for identifying Model Plant Mismatch for MPC applications is developed. Focus has been on developing a method that is simple to implement but still robust. The method is able to run in parallel with the process in real time. The efficiency of the method is demonstrated via representative simulation examples.An extension to detection of nonlinear mismatch is also considered, which is important since linear plant models often are used within a small operating range. Since most processes are nonlinear this discrepancy is inevitable and should be detected.</p> / <p>Ökade krav på effektivitet gör att industrin söker efter mer avancerad processtyrning. MPC har växt fram som en kandidat. En MPC regulator änvänder en modell av systemet för att samtidigt som systemet körs utföra en optimering av framtida styrsignaler. Om modellen innehåller felaktigheter kan reglerprestandan påverkas. En modell försämras normalt då tiden från idrifttagning växer eftersom systemet förändras med tiden. Det är av största vikt att upptäcka dessa förändringar och sedan uppdatera modellen för att reglerprestandan inte ska påverkas. Avsikten är att utveckla en metod för att upptäcka modellfel med fokus på att den ska vara enkel att implementera. Det ska även vara möjligt att använda metoden parallellt med en process. För att utvärdera metoden så körs den på ett antal representativa simuleringsexempel. Det har även varit en avsikt att utveckla en metod för detektion av ickelinjära modellfel. Motivet till det är att linjära modeller används för att beskriva ickelinjära processer och då är modellfel naturliga.</p>