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.

Neste trabalho é abordado o problema de controle e de otimização de unidades industriais de hidrotratamento de diesel (UHDT) por controladores MPC (Model Predictive Control). É apresentado um breve histórico dos controladores MPC convencionais e de horizonte infinito (IHMPC), bem como uma breve descrição do processo de Hidrotratamento de Diesel e das particularidades da aplicação do controle de processos a este tipo de planta industrial. Em seguida foi gerado, passo a passo, um algoritmo de controle que sumarizou e agregou características de vários controladores MPC disponíveis na literatura aberta, em especial os que foram desenvolvidos ao longo dos últimos anos pelo laboratório de simulação e controle da USP (LSCP), a fim de se obter um algoritmo adequado para a solução do problema de controle abordado. Em ambiente computacional de simulação, o algoritmo resultante possibilitou controlar e otimizar simultaneamente processos contínuos, sendo capaz de estabilizar a planta industrial de forma robusta e, ao mesmo tempo, aumentar a lucratividade de sua operação. Para tanto, foi desenvolvida uma função objetivo econômica que aumentou a conversão da carga bruta em produtos hidrotratados e minimizou o consumo de insumos, sendo que essa correlação foi agregada ao algoritmo de controle. As simulações permitiram que as estratégias de controle previamente discutidas pudessem ser testadas e seus resultados apresentados e debatidos. / This work addresses the control and optimization problem of industrial diesel hydrotreating units (UHDT) by MPC controllers (Model Predictive Control). It is presented a brief historical of conventional MPC controllers and infinite horizon controllers (IHMPC), as well as a brief description of the Diesel Hydrotreating process and the particulars of the application of process control for this type of industrial plant. It was then generated, step by step, one algorithm that summarized and aggregated control characteristics of various MPC controllers available in the open literature, in particular those that have been developed over the past few years by USPs laboratory of simulation and control of (LSCP), in order to obtain an algorithm suitable for solving the addressed control problem. In a computational simulation environment, the resulting algorithm allowed to simultaneously control and optimize continuous processes, being able to robustly stabilize the industrial plant and at the same time increase the profitability of its operation. For this purpose, an \"objective function\" was developed which increased the economic conversion of crude feed to hydrotreated product and minimized the consumption of raw materials, and this correlation was added to the control algorithm. The simulations allowed that the previously discussed control strategies could be tested and the results presented and discussed.

O objetivo do trabalho é apresentar dois algoritmos para a solução de problemas de controle ótimo quadrático robusto com restrições, dentro de um contexto de controladores preditivos (MPC do inglês Model Predictive Control). Inicialmente apresentamos uma breve introdução aos algoritmos MPC, com ênfase na abordagem do controlador linear quadrático. Em seguida são apresentados os dois algoritmos de interesse, que utilizam técnicas de otimização LMI. Dessa forma as restrições e as incertezas podem ser colocadas em formas computacionalmente tratáveis. Por fim são realizadas simulações e comparações entre esses algoritmos, bem como com técnicas de MPC encontradas na literatura atual. / The goal of the work is to present two algorithms for the solution of robust quadratic optimal control problems with restrictions, within a model predictive control (MPC) setup. Initially we present a brief introduction of the MPC algorithms, emphasizing the linear quadratic controller approach. Next the two algorithms of interest, using LMI optimization techniques, are presented. By using this technique the restrictions and uncertainties can be written in a computational way. Finally some simulations and comparisons between these algorithms, as well as with MPC techniques found in the current literature, are performed.

A Identificação de Sistemas é uma tarefa significativa em termos de tempo e custo no trabalho de implementação de sistemas de controle que usam Controle Preditivo baseado em Modelos (MPC). Após a implementação, o controlador tende a permanecer com o mesmo modelo por muito tempo, ignorando mudanças que tenham ocorrido com o processo, perdendo qualidade e podendo até ser abandonado. Este trabalho propõe uma metodologia simples e eficaz para se proceder à reidentificação de uma planta industrial que use MPC mantendo o processo em malha fechada. Os principais aspectos deste problema são discutidos, e as escolhas que foram feitas para a realização dos experimentos e obtenção dos modelos são explicadas. Apresenta-se um caso em Matlab sobre um sistema 2x2 cobrindo diferentes situações, e é feita uma comparação de identificação realizada através de sinais PRBS e de testes com degraus, sempre em malha fechada. Aplica-se a metodologia a um controlador industrial, e os modelos identificados são introduzidos no controlador. O princípio básico desta metodologia consiste em efetuar perturbações multivariáveis nos set-points ou restrições ativas das controladas e determinar o modelo através da estrutura ARX. Entre as vantagens da metodologia proposta, estão a facilidade de automatizar a identificação do processo e a garantia de manter o processo sob controle durante os testes. / System identification is a major task in the process of implementing Model-based Predictive Control (MPC) algorithms in industrial applications. Once the controller is working, there is a tendency to leave it with the original model for a long time, neglecting changes to the process during this time, leading to performance degradation. This work proposes a simple and effective methodology to re-identify plants under MPC in closed loop. The main issues concerning this problem are discussed, and choices for experiments are made. A Matlab case involving a 2x2 problem is presented, covering a range of different situations, and a comparison between identification using PRBS reference signals and standard step tests is shown. An industrial case is studied, applying the proposed method to a real situation, re-identifying an existing MPC model and reconfiguring it afterwards. This methodology is based on the application of multivariable perturbations on the controlled variables set-points or active restrictions, obtaining an ARX model structure. It uses an automatic process identification proceeding, keeping the process under control along the tests.

Platooning is an interesting area which involve the possibility of decreasing the fuel consumption of heavy-duty vehicles. By reducing the inter-vehicle spacing in the platoon we can reduce air drag, which in turn reduces fuel consumption. Two fuel-efficient model predictive controllers for HDVs in a platoon has been formulated in this master thesis, both utilizing road grade preview information. The first controller is based on linear programming (LP) algorithms and the second on quadratic programming (QP). These two platooning controllers are compared with each other and with generic controllers from Scania. The LP controller proved to be more fuel-efficient than the QP controller, the Scania controllers are however more fuel-efficient than the LP controller.

O objetivo do trabalho é apresentar dois algoritmos para a solução de problemas de controle ótimo quadrático robusto com restrições, dentro de um contexto de controladores preditivos (MPC do inglês Model Predictive Control). Inicialmente apresentamos uma breve introdução aos algoritmos MPC, com ênfase na abordagem do controlador linear quadrático. Em seguida são apresentados os dois algoritmos de interesse, que utilizam técnicas de otimização LMI. Dessa forma as restrições e as incertezas podem ser colocadas em formas computacionalmente tratáveis. Por fim são realizadas simulações e comparações entre esses algoritmos, bem como com técnicas de MPC encontradas na literatura atual. / The goal of the work is to present two algorithms for the solution of robust quadratic optimal control problems with restrictions, within a model predictive control (MPC) setup. Initially we present a brief introduction of the MPC algorithms, emphasizing the linear quadratic controller approach. Next the two algorithms of interest, using LMI optimization techniques, are presented. By using this technique the restrictions and uncertainties can be written in a computational way. Finally some simulations and comparisons between these algorithms, as well as with MPC techniques found in the current literature, are performed.

A Identificação de Sistemas é uma tarefa significativa em termos de tempo e custo no trabalho de implementação de sistemas de controle que usam Controle Preditivo baseado em Modelos (MPC). Após a implementação, o controlador tende a permanecer com o mesmo modelo por muito tempo, ignorando mudanças que tenham ocorrido com o processo, perdendo qualidade e podendo até ser abandonado. Este trabalho propõe uma metodologia simples e eficaz para se proceder à reidentificação de uma planta industrial que use MPC mantendo o processo em malha fechada. Os principais aspectos deste problema são discutidos, e as escolhas que foram feitas para a realização dos experimentos e obtenção dos modelos são explicadas. Apresenta-se um caso em Matlab sobre um sistema 2x2 cobrindo diferentes situações, e é feita uma comparação de identificação realizada através de sinais PRBS e de testes com degraus, sempre em malha fechada. Aplica-se a metodologia a um controlador industrial, e os modelos identificados são introduzidos no controlador. O princípio básico desta metodologia consiste em efetuar perturbações multivariáveis nos set-points ou restrições ativas das controladas e determinar o modelo através da estrutura ARX. Entre as vantagens da metodologia proposta, estão a facilidade de automatizar a identificação do processo e a garantia de manter o processo sob controle durante os testes. / System identification is a major task in the process of implementing Model-based Predictive Control (MPC) algorithms in industrial applications. Once the controller is working, there is a tendency to leave it with the original model for a long time, neglecting changes to the process during this time, leading to performance degradation. This work proposes a simple and effective methodology to re-identify plants under MPC in closed loop. The main issues concerning this problem are discussed, and choices for experiments are made. A Matlab case involving a 2x2 problem is presented, covering a range of different situations, and a comparison between identification using PRBS reference signals and standard step tests is shown. An industrial case is studied, applying the proposed method to a real situation, re-identifying an existing MPC model and reconfiguring it afterwards. This methodology is based on the application of multivariable perturbations on the controlled variables set-points or active restrictions, obtaining an ARX model structure. It uses an automatic process identification proceeding, keeping the process under control along the tests.

Neste trabalho é abordado o problema de controle e de otimização de unidades industriais de hidrotratamento de diesel (UHDT) por controladores MPC (Model Predictive Control). É apresentado um breve histórico dos controladores MPC convencionais e de horizonte infinito (IHMPC), bem como uma breve descrição do processo de Hidrotratamento de Diesel e das particularidades da aplicação do controle de processos a este tipo de planta industrial. Em seguida foi gerado, passo a passo, um algoritmo de controle que sumarizou e agregou características de vários controladores MPC disponíveis na literatura aberta, em especial os que foram desenvolvidos ao longo dos últimos anos pelo laboratório de simulação e controle da USP (LSCP), a fim de se obter um algoritmo adequado para a solução do problema de controle abordado. Em ambiente computacional de simulação, o algoritmo resultante possibilitou controlar e otimizar simultaneamente processos contínuos, sendo capaz de estabilizar a planta industrial de forma robusta e, ao mesmo tempo, aumentar a lucratividade de sua operação. Para tanto, foi desenvolvida uma função objetivo econômica que aumentou a conversão da carga bruta em produtos hidrotratados e minimizou o consumo de insumos, sendo que essa correlação foi agregada ao algoritmo de controle. As simulações permitiram que as estratégias de controle previamente discutidas pudessem ser testadas e seus resultados apresentados e debatidos. / This work addresses the control and optimization problem of industrial diesel hydrotreating units (UHDT) by MPC controllers (Model Predictive Control). It is presented a brief historical of conventional MPC controllers and infinite horizon controllers (IHMPC), as well as a brief description of the Diesel Hydrotreating process and the particulars of the application of process control for this type of industrial plant. It was then generated, step by step, one algorithm that summarized and aggregated control characteristics of various MPC controllers available in the open literature, in particular those that have been developed over the past few years by USPs laboratory of simulation and control of (LSCP), in order to obtain an algorithm suitable for solving the addressed control problem. In a computational simulation environment, the resulting algorithm allowed to simultaneously control and optimize continuous processes, being able to robustly stabilize the industrial plant and at the same time increase the profitability of its operation. For this purpose, an \"objective function\" was developed which increased the economic conversion of crude feed to hydrotreated product and minimized the consumption of raw materials, and this correlation was added to the control algorithm. The simulations allowed that the previously discussed control strategies could be tested and the results presented and discussed.

Les travaux de cette thèse portent sur l’investigation d’approches de commande et de supervision permettant d’aborder la problématique de gestion d’énergie des réseaux électriques multi-sources. l’objectif souhaité était de proposer une démarche de conception de lois de commande pour ce type de système en vue de réguler la tension de sortie et de gérer d’une manière optimale les flux d’énergie entre les différentes sources et les consommateurs et au vu de minimiser la consommation d’hydrogène.A cette fin, deux configurations ont été envisagées :l’application d’approches à base d’un modèle statique et des stratégies à base d’un modèle dynamique de la PàC. Dans un premier temps, trois approches de gestion énergétique ont été appliquées au système visant à minimiser la consommation de masse d’hydrogène tout en respectant les contraintes physiques du système.Tout d’abord, l’optimisation est réalisée en utilisant une méthode d’optimisation hors ligne appelée la programmation dynamique. Deuxièmement, deux approches d’optimisation en ligne sont utilisées : stratégies ECMS et MPC. Une comparaison en termes de consommation d’hydrogène et de temps de calcul est réalisée.Dans un deuxième temps, une approche décentralisée de commande a été envisagée afin de tenir compte du modèle dynamique de la PàC dans la conception du superviseur. L’avantage de cette architecture réside dans sa capacité a aborder séparément chacune des problématiques dans l’optique de répondre aux différents objectifs de commande. Dans ce cadre, la régulation du système PàC et de l’état de charge de l’ESS est réalisée séparément avec deux contrôleurs différents, tous deux conçus en utilisant l’approche (MPC-LTV). Les troisième et quatrième niveaux de la structure de contrôle décentralisée consistent en des boucles de locale des courants de la PàC et de SC et un contrôle de tension du bus continu, conçu à l’aide de contrôleurs PI. La validation de la structure de contrôle est réalisée en simulation en utilisant un modèle non linéaire du système PàC.A la fin, les approches de commande conçues à base du modèle statique de la PàC (DP, ECMS et MPC) seront appliquées sur le modèle dynamique de cette dernière afin d’évaluer les performances de ces approches et d’en déduire des conclusions sur l’apport de l’intégration de la dynamique de la PàC dans la synthèse des lois de commande. / This thesis focuses on the investigation of control approaches to treat the issue of energy management of multi-source electrical networks. The considered electric motor supply system consists on a fuel cell as a main energy source and an additional element that supplies peak power and charges by regenerative braking. At first, three energy management strategies have been applied to the sypply system aiming to minimize the fuel cell hydrogen mass consumption while satisfying the system physical constraints. First, the optimization is realized using dynamic programming,an off-line optimization method that requires the knowledge of the entire power load profile. Secondly, twoon-line optimization approaches are used : ECMS and MPC strategies, for which only the current power demand is demanded.The second part of this thesis presents a decentralized control strategy applied to the power system. The dedicated control structure aims to assure an optimal operation of the FC system while respecting the compressor physical limits and to control the converter current sand network output voltage. To attain these objectives, a dynamic model of the FC system is used,in addition to the SSE and electric network dynamics. The FC system regulation and the control of the SSE state of energy are performed separately with two different controllers, both designed using (MPC-LTV) approach. The third and fourth levels of the decentralized control structure consists on inner control loops for fuel cell/supercapacitor currents and a DC bus voltage control loop, designed using PI controllers. The validation of the control structure is performed in simulation using a nonlinear models of the FC system and the SSE. To validate and compare the performance of different control methods based on a fuel cell static model, these approaches have been applied to the dynamic model of the FC and compared to the results obtained by applying the approched designed and based on an FC dynamic model. A comparison in terms of network efficiency and hydrogen consumption has been done.