Ingeniero Civil Electricista / En la actualidad, existe un inmenso interés internacional por el desarrollo de tecnologías de generación con fuentes renovables. Particularmente, la tecnología de concentración solar ha alcanzado suficiente madurez para ser incluida en el mercado de generación y se espera que dentro de los próximos años sea una tecnología presente en la matriz energética de Chile debido principalmente a las condiciones privilegiadas de radiación en el norte del país y también por los incentivos por parte del Estado y también de privados para invertir en esta tecnología. Debido a que en una planta solar no es posible manipular la fuente de energía primaria (radiación solar), el objetivo general del presente trabajo consiste en el diseño de una estrategia de control predictivo basado en modelos (MPC) a nivel supervisor para una planta con colectores cilíndrico parabólicos, que permita aprovechar al máximo el recurso disponible. El trabajo se ha realizado utilizando el simulador de planta solar ACUREX, desarrollado en Matlab-Simulink, el cual representa el comportamiento del lazo de colectores de la plataforma solar de Almería, España. Los modelos predictivos de la planta desarrollados consisten en modelos lineales ARX (autorregresivo con entrada exógena) y ARIX (autorregresivo integral con entrada exógena); y un modelo no lineal difuso de T&S (Takagi & Sugeno), los cuales consideran como variable de salida a la temperatura de salida del aceite del campo colector y como variable de entrada su valor de referencia. Debido a que los modelos ARIX y de T&S presentan los mejores resultados, se propone el diseño de una estrategia de control predictivo a nivel supervisor en base a ambos modelos. Los resultados obtenidos muestran que su implementación reduce el error de seguimiento en 1%, se evita sobreimpulso en el sistema y se limita los cambios en la energía de control, haciendo el funcionamiento del sistema más seguro. En base a pruebas por simulación, se concluye que el MPC difuso supervisor basado en el modelo de T&S mejora el desempeño de la planta frente a cambios en la referencia y en condiciones anormales de operación en comparación con el control regulatorio original. Como líneas de investigación futuras se propone entregar robustez al sistema por medio de la integración de incertidumbre paramétrica en el modelo, incluir criterios económicos de operación e implementar el sistema de control en una futura planta solar en Chile.

Batch and semi-batch processes provide needed flexibility for multi-product plants, especially when products change frequently and production quantities are small. However, challenges occur when trying to implement reliable control systems in batch processes due to some unavoidable inherent characteristics such as the presence of time-varying and nonlinear batch process dynamics and a host of unmeasured disturbances. The most typical control strategy employed in batch process operations does not use utilise online measurements of variables directly related to the product quality and as such is bound to produce off specification products even when the specified control objective has been met. Work done in this thesis is concerned with the design of a supervisory control scheme that takes into consideration the online status of the quality variable of interest from the beginning to the end of the batch process. A novel control methodology is proposed which combines the speed and flexibility of Near-Infrared (NIR) spectroscopic measurements as quality feedback variables within a multiple model predictive control (MPC) framework. In particular the multivariate NIR spectral data is pre-processed for feedback using a statistical model based on Independent Component Analysis (ICA). The proposed controller is tested on a benchmark simulated batch reactor using several case studies and is demonstrated to bring significant improvement in control performance when contrasted with other inferential and direct quality controllers.

Stochastic model predictive control (MPC) is a fascinating field for research and of increasing practical importance since optimal control techniques have been intensively investigated in modern control system design. With the development of computer technologies and communication networks, networked control systems (NCSs) or cyber-physical systems (CPSs) have become an interest of research due to the comprehensive integration of physical systems, such as sensors, actuators and plants, with intricate cyber components, possessing information communication and computation. In CPSs, advantages of low installation cost, high reliability, flexible modularity, improved efficiency, and greater autonomy can be obtained by the tight coordination of physical and cyber components. Several sectors, including robotics, transportation, health care, smart buildings, and smart grid, have witnessed the successful application of CPSs design. The integration of extensive cyber capability and physical plants with ubiquitous uncertainties also introduces concerns over communication efficiency, robustness and stability of the CPSs. Thus, to achieve satisfactory performance metrics of efficiency, robustness and stability, a detailed investigation into control synthesis of CPSs under the stochastic model predictive control framework is of importance. The stochastic model predictive control synthesis plays a vital role in CPSs design since the multivariable stochastic system subject to probabilistic constraints can be controlled in an optimized way. On the other hand, aperiodically sampled, or event-based, model predictive control has also been applied to CPSs extensively to improve communication efficiency. In this thesis, the control synthesis and analysis of aperiodically sampled stochastic model predictive control for CPSs is considered. Chapter 1 provides an introductory literature review of the current development of stochastic MPC, distributed stochastic MPC and event-based MPC. Chapter 2 presents a stochastic self-triggered model predictive control scheme for linear systems with additive uncertainty and with the states and inputs being subject to chance constraints. In the proposed control scheme, the succeeding sampling time instant and current control inputs are computed online by solving a formulated optimization problem. Chapter 3 discusses a stochastic self-triggered model predictive control algorithm with an adaptive prediction horizon. The communication cost is explicitly considered by adding a damping factor in the cost function. Sufficient conditions are provided to guarantee closed-loop chance constraints satisfactions. Furthermore, the recursive feasibility of the algorithm is analyzed, and the closed-loop system is shown to be stable. Chapter 4 proposes a distributed self-triggered stochastic MPC control scheme for CPSs under coupled chance constraints and additive disturbances. Based on the assumptions on stochastic disturbances, both local and coupled probabilistic constraints are transformed into the deterministic form using the tube-based method, and improved terminal constraints are constructed to guarantee the recursive feasibility of the control scheme. Theoretical analysis has shown that the overall closed-loop CPSs are quadratically stable. Numerical examples illustrate the efficacy of the proposed control method in terms of data transmission reductions. Chapter 5 concludes the thesis and suggests some promising directions for future research. / Graduate / 2022-01-15

Traditional robots, while capable of being efficient and effective for the task they were designed, are dangerous when operating in unmodeled environments or around humans. The field of soft robotics attempts to increase the safety of robots thus enabling them to operate in environments where traditional robots should not operate. Because of this, soft robots were developed with different goals in mind than traditional robots and as such the traditional metrics used to evaluate standard robots are not effective for evaluating soft robots. New metrics need to be developed for soft robots so that effective comparison and evaluations can be made. This dissertation attempts to lay the groundwork for that process through a survey on soft robot metrics. Additionally we propose six soft robot actuator metrics that can be used to evaluate and compare characteristics and performance of soft robot actuators. Data from eight different soft robot rotational actuators (five distinct designs) were used to evaluate these soft robot actuator metrics and show their utility. New models, control methods and estimation methods also need to be developed for soft robots. Many of the traditional methods and assumptions for modeling and controlling robotic systems are not able to provide the fidelity that is needed for soft robots to effectively complete useful tasks. This dissertation presents specific developments in each of these areas of soft robot metrics, modeling, control and estimation. We show several incremental improvements to soft robot dynamic models as well as how they were used in control methods for more precise control. We also demonstrate a method for linearizing high degree of freedom models so it can be simplified for use in faster control methods for better performance. Lastly, we present an improved continuum joint configuration estimation method that uses a linear combination of length measurements. All these developments combine to help build the "fundamental engineering framework" that is needed for soft robotics as well as helping to move robots out of their confined spaces and bring them into new unmodeled/unstructured environments.

Collision avoidance is an essential capability for autonomous and assisted-driving ground vehicles. In this work, we developed a novel model predictive control based intelligent collision avoidance (CA) algorithm for a multi-trailer industrial ground vehicle implemented on a General Purpose Graphical Processing Unit (GPGPU). The CA problem is formulated as a multi-objective optimal control problem and solved using a limited look-ahead control scheme in real-time. Through hardware-in-the-loop-simulations and experimental results obtained in this work, we have demonstrated that the proposed algorithm, using NVIDA’s CUDA framework and the NVIDIA Jetson TX2 development platform, is capable of dynamically assisting drivers and maintaining the vehicle a safe distance from the detected obstacles on-thely. We have demonstrated that a GPGPU, paired with an appropriate algorithm, can be the key enabler in relieving the computational burden that is commonly associated with model-based control problems and thus make them suitable for real-time applications.

In this paper, the use of reinforcement learning (RL) in control systems is investigated using a rotatory inverted pendulum as an example. The control behavior of an RL controller is compared to that of traditional LQR and MPC controllers. This is done by evaluating their behavior under optimal conditions, their disturbance behavior, their robustness and their development process. All the investigated controllers are developed using MATLAB and the Simulink simulation environment and later deployed to a real pendulum model powered by a Raspberry Pi. The RL algorithm used is Proximal Policy Optimization (PPO). The LQR controller exhibits an easy development process, an average to good control behavior and average to good robustness. A linear MPC controller could show excellent results under optimal operating conditions. However, when subjected to disturbances or deviations from the equilibrium point, it showed poor performance and sometimes instable behavior. Employing a nonlinear MPC Controller in real time was not possible due to the high computational effort involved. The RL controller exhibits by far the most versatile and robust control behavior. When operated in the simulation environment, it achieved a high control accuracy. When employed in the real system, however, it only shows average accuracy and a significantly greater performance loss compared to the simulation than the traditional controllers. With MATLAB, it is not yet possible to directly post-train the RL controller on the Raspberry Pi, which is an obstacle to the practical application of RL in a prototyping or teaching setting. Nevertheless, RL in general proves to be a flexible and powerful control method, which is well suited for complex or nonlinear systems where traditional controllers struggle.

No description available.

Due to the rapid and prosperous development of information technology, data centers are widely used in every aspect of social life, such as industry, economy or even our daily life. This work considers the idea of developing a data-driven model based model predictive control (MPC) to regulate temperature for a class of single-rack data centers (DCs). An auto-regressive exogenous (ARX) model is identified for our DC system using partial least square (PLS) to predict the behavior of multi-inputs-single-output (MISO) thermal system. Then an MPC controller is designed to control the temperature inside IT rack based on the identified ARX model. Moreover, fuzzy c-means (FCM) is employed to cluster the measured data set. Based on the clustered data sets, PLS is adopted to identify multiple locally linear ARX models which will be combined by appropriate weights in order to capture the nonlinear behavior of the highly-nonlinear thermal system inside the IT rack. The effectiveness of the proposed method is illustrated through experiments on our single-rack DC and it is also compared with proportional-integral (PI) control. / Thesis / Master of Applied Science (MASc)

There is a constant demand to use more advanced control methods in a wider field of applications. Model Predictive Control (MPC) is one such control method, based on recurrently solving an optimization problem for determining the optimal control signal. To solve an optimization problem can be a complex task, and it is difficult to determine beforehand how long time it will take. For a high-speed application with limited computational power, it is necessary to have an efficient algorithm to solve the optimization problem and an accurate estimation of the longest solution time. Recent research has given methods both to solve quadratic programs efficiently and to find an upper limit on the solution times. These methods are in this thesis applied to a control system based on linear MPC for the Crazyflie 2.0 nano quadcopter. The implementation is made completely online on the processor of the quadcopter, with limited computational power. A problem with the size of 36 optimization variables and 60 constraints is solved at a frequency of 100 Hz on the quadcopter. Apart from implementing MPC, a framework for computing an upper limit to the solution time has been tested. This gives a possibility to certify the formulation for real-time applications up to a well-defined maximum frequency. An implementation is shown where the framework has been used in practice to control a quadcopter flying with a real-time certified implementation of MPC. / Det finns en ständig efterfrågan för mer avancerade metoder för reglering. Modellprediktiv reglering (MPC) är en sådan avancerad metod som kräver att ett optimeringsproblem löses varje gång en ny styrsignal ska beräknas. Att lösa optimeringsproblem kan vara en komplicerad uppgift, och det är svårt att på förhand veta hur lång beräkningstid som krävs. För att MPC ska kunna användas i tillämpningar i hög hastighet och med begränsad beräkningskraft är det nödvändigt att ha en effektiv lösningsalgoritm, och även en korrekt uppskattning av den längsta lösningstiden som behövs. Aktuell forskning har gett metoder både för att effektivt lösa kvadratiska optimeringsproblem, samt för att kunna hitta en övre gräns på beräkningstiden. I den här rapporten appliceras dessa metoder på ett styrsystem baserat på MPC i en Crazyflie 2.0, vilket är en nanodrönare. Styrsystemet är implementerat helt och hållet på drönarens processor, med den begränsade datorkraft som det innebär. Ett problem med en storlek på 36 optimeringsvariabler och 60 bivillkor lösesmed en frekvens på 100 Hz. Förutom att implementera MPC har även en metod för att bestämma en övre gräns på beräkningstiden testats. Det ger en möjlighet att certifiera styrstytemetför att garanterat kunna beräkna en ny styrsignal inom den övre tiden, vilket i sin tur innebär att styrsytemet kan certificeras för realtidsanvändning i långsammare frekvenser än den övre gränsen. I rapporten visas en certifierad implementation, och data från flygning med en certifierad regulator finns med i resultatet.

Smart building's benefits range from improving comfort of occupant, increased productivity, reduction in energy consumption and operating costs, lower CO2 emission, to improved life cycle of utilities, efficient operation of building systems, etc. [65]. Hence, modern building owners are turning towards smart buildings. However, the current smart buildings mostly are not capable of achieving the objectives they are designed for and they can improve a lot better [22]. Therefore, a new technology called, Internet of Things, or IoT, is combined with the smart buildings to improve their performance [23]. IoT is the inter-networking of things embedded with electronics, software, sensors, actuators, and network connectivity to collect and exchange data, and things in this definition is anything and everything around us and even ourselves. Using this technology, e.g. a door can be a thing and can sense how many people have passed it's sensor to enter a space and let the lighting system know to prepare appropriate amount of light, or the HVAC (Heating Ventilation Air Conditioning) system to provide desirable temperature. IoT will provide a lot of useful information that before that accessibility to it was impossible, e.g., condition of water pipes in winter, which helps avoiding damages like frozen or broken pipes. However, despite all the benefits, IoT suffers from being vulnerable to cyber attacks. Examples have been provided later in Chapter 1. In this project among building systems, HVAC system is chosen to be automated with a new control method called MPC (Model Predictive Control). This method is fast, very energy efficient and has a lower than 0.001 rate of error for regulating the space temperature to any temperature that the occupants desire according to the results of this project. Furthermore, a PID (Proportional–Integral–Derivative) controller has been designed for the HVAC system that in the exact same cases MPC shows a much better performance. To design controllers for HVAC system and set the temperature to the desired value a method to automate balancing the heat flow should be found, therefore a thermal model of building should be available that using this model, the amount of heat, flowing in and out of a space in the building disregarding the external weather would be known to estimate. To automate the HVAC system using the programming languages like MATLAB, there is a need to convert the thermal model of the building to a mathematical model. This mathematical model is unique for each building depending on how many floors it has, how wide it is, and what materials have been used to construct the building. This process is needs a lot of effort and time even for buildings with 2 floors and 2 rooms on each floor and at the end the engineer might have done it with error. In this project you will see a software that will do the conversion of thermal model of buildings in any size to their mathematical model automatically, which helps improving the HVAC controllers to set temperature to the value occupants desire and avoid errors and time loss which is put both into calculations and troubleshooting. In addition, a test environment has been designed and constructed as a cyber physical system that allows us to test the IoT- enabled control systems before implementing them on real buildings, observe the performance, and decide if the system is satisfying or not. Also, all cyber threats can be explored and the solutions to those attacks can be evaluated. Even for the systems that are already out there, there is an opportunity to be assessed on this testbed and if there is any vulnerability in case of cyber security, solutions would be evaluated and help the existing systems improve. / Master of Science / Buildings function as shelters more than any thing else, and this has allowed humans to use it as a space to store important things like private and important information. Therefore, this space should be safe and secure from any vulnerabilities for occupants and their information. Smart buildings, have made a great difference in increasing the comfort level of occupants, but they haven’t been greatly successful achieving their objectives [50]. Therefore, a new technology called, Internet of Things, or IoT, is combined with the smart buildings to improve their performance [23]. IoT is the inter-networking of things embedded with electronics, software, sensors, actuators, and network connectivity to collect and exchange data, and things in this definition is anything and everything around us and even ourselves. Internet of Things (IoT) has helped improving the smart buildings and getting a considerable amount of energy efficiency [27]. But adding Internet of Things has added a network of things connected to internet, which gives the cyber hackers an opportunity to hack the buildings, and get access to the information stored inside the building or put even occupants lives in danger. Therefore, in this thesis the following items have been contributed: • Designing and programming a novel control system for HVAC system of the buildings (Model Predictive Control): This is a new method to control HVAC system of buildings and in comparison with the methods available in the market, it is the most energy efficient, it is faster, and it has a lower error rate in following the desired temperature of the occupants. • Design and construction of IoT- enabled smart building testbed: Since cyber attacks make buildings vulnerable, the author believes it is better to build a test environment to simulate the buildings and the control methods that are used inside the buildings, and try to evaluate performance of the control methods before implementing them on real buildings. Also, by installing IoT sensors inside the test environment, the engineers can perform some cyber attack tests, and also evaluate the solutions for each attack on the testbed. • Design and program a software to convert thermal model of buildings to mathematical model : In designing a new control method for HVAC system of buildings, the first required information is the thermal model of the buildings. Eventually, there is a need to program. Thus, the thermal model should be converted to a mathematical model. However, there is a heavy manual calculation behind it that is really overwhelming, tiring, with a high possibility of error, and time-consuming even for a very small sized building. Therefore, automating this process in terms of a software that takes the information of thermal model of buildings as an input and giving the output of the mathematical model of building is a considerable achievement.