An embedded model predictive controller for velocity control of trucks is developed and tested. By using a simple model of a heavy duty vehicle and knowledge about the slope of the road ahead, the fuel consumption while traveling near a set speed is diminished by almost 1% on an example road compared to a rule based speed control system. The problem is formulated as a look-ahead optimization problem were fuel consumption and total trip time have to be minimized. To find the optimal solution dynamic programming is used, and the whole code is designed to run on a Scania gearbox ECU in parallel with all the current software. Simulations were executed in a Simulink environment, and two test rides were performed on the E4 motorway. / En algoritm för hastighetsstyrning baserad på modell-prediktiv reglering har utvecklats och testats på befintlig styrsystem i ett Scania lastbil. Genom att använda en enkel modell av fordonet och kunskap om lutningen på vägen framför den kunde man sänka bränsleförbrukningen med nästan 1% i vissa sträckor, jämfört med en regelbaserad farthållare. Problemet är formulerat som en optimerings-problem där bränsleförbrukning och total restid måste minimeras. För att hitta den optimala lösningen användes dynamisk programmering och hela koden är skriven så att den kan exekveras på en Scania styrenehet. Koden är kan köras parallellt med den mjukvara som är installerad på styrenheten. Simuleringar utfördes i en miljö utvecklad i Simulink. Två test-körningar på E4 motorvägen utfördes.

Esse trabalho apresenta uma contribuição para o projeto de um controlador MPC robusto quanto à estabilidade baseado na realimentação da saída e admitindo restrições nas entradas e incertezas no modelo da planta. Ele estende a abordagem existente para o projeto de um MPC considerando o caso particular de um modelo em espaço de estados, onde o estado é lido diretamente da planta, sendo aplicado para a situação em que o sistema escolhido de entradas possa ficar saturado ou que o processo seja representado por um modelo diferente do modelo considerado na função objetivo do controlador. Para isso, o MPC se propõe a resolver o problema de otimização em dois estágios: No estágio off-line, vários controladores sem restrição são obtidos a partir de um problema de otimização onde inequações de Lyapunov são acrescentadas ao problema como restrições de forma a garantir a contração do estado (estabilidade). Esses controladores, representados por uma matriz de ganhos, correspondem a todas configurações possíveis de saturação das variáveis manipuladas para um dado conjunto possível de variáveis controladas. Nessas combinações, incluídas como restrições no controlador, todos os modelos previstos para o processo são considerados. Dessa forma, perdendo-se uma entrada, o subconjunto de saídas controladas pode ser alterado.Na versão anterior do método proposto por Rodrigues & Odloak (2005), esse estágio off-line envolve um observador de estados o que dificulta a solução do problema de otimização do MPC robusto, consumindo grande tempo computacional. Além disso, requer uma solução inicial viável que nem sempre é trivial. Com a versão proposta do sistema de modelo espaço estado, o estimador de estado torna-se desnecessário pois o estado passa a ser medido. Na etapa on-line do projeto do controlador, uma lei ótima de controle é obtida a partir da combinação convexa das configurações de controle que correspondem ao conjunto de variáveis manipuladas disponíveis (não saturadas). Também nessa etapa é considerada a incerteza do modelo utilizado pelo controlador. O controlador proposto é testado com alguns exemplos simulados a partir de modelos obtidos na indústria de processo. / In this work, it is presented a contribution to the design of a robust MPC with output feedback, input constraints and uncertain model. This work extends existing approaches by considering a particular non-minimal state space model, which transforms the output feedback strategy into a state feedback strategy. The controller is developed to the case in which the system inputs may become saturated and the model is uncertain. We follow a two stages approach: In the off-line stage, a series of unconstrained robust MPCs is obtained by including in the control optimization problem, inequality constraints that force the state of the closed-loop system to contract along the time. Each of these controllers, represented by a gain matrix, is associated to particular sets of manipulated inputs and controlled outputs. When one manipulated input becomes saturated, we may need to reduce the set of controlled variables. In the existing version of the method, the closed loop system involves a state observer that makes the solution to the robust MPC optimization problem a time consuming step. The problem also requires an initial solution that may not be trivial to find. With the adopted version of the system state space model, the state filter becomes trivial and the state can be considered measured. In the on-line step of the proposed controller design, a sub optimal control law is obtained by combining control configurations that correspond to particular subsets of available manipulated inputs. The method is illustrated with simulation examples of the process industry.

Submitted by Felipe Fakir null (zafakir@yahoo.com.br) on 2017-06-27T07:01:28Z No. of bitstreams: 1 FFAKIR Dissertação vFinalFichaCataAta.pdf: 2064786 bytes, checksum: 158a935a636b9dbf9e59618a35b4c8ef (MD5) / Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-06-28T19:39:58Z (GMT) No. of bitstreams: 1 fakir_f_me_bauru.pdf: 2064786 bytes, checksum: 158a935a636b9dbf9e59618a35b4c8ef (MD5) / Made available in DSpace on 2017-06-28T19:39:58Z (GMT). No. of bitstreams: 1 fakir_f_me_bauru.pdf: 2064786 bytes, checksum: 158a935a636b9dbf9e59618a35b4c8ef (MD5) Previous issue date: 2017-06-01 / A tecnologia de comunicação wireless vem se tornando parte fundamental do cotidiano das indústrias de processos, onde o uso de transmissores wireless aplicados à monitoração e controle já é uma realidade. A arquitetura de Sistema de Controle via Rede Sem Fio (WNCS) possui vantagens em relação às arquiteturas tradicionais ponto-a-ponto e às arquiteturas de redes cabeadas devido à facilidade de instalação, configuração e manutenção. No entanto, a evolução desta tecnologia introduziu novos desafios para a implementação da malha de controle fechada por um instrumento wireless como as não linearidades, perda de pacote de dados e restrições da comunicação de dados nas redes sem fio. Outro fator crítico relacionado à implementação de WNCSs é a fonte de energia limitada destes transmissores, que possuem vida útil dependente da quantidade de acessos e dados transmitidos. Este trabalho apresenta o estudo e o desenvolvimento de um controlador preditivo multi-rate como alternativa para melhorar a eficiência energética em aplicações industriais de WNCSs. A estratégia proposta não necessita receber constantemente os valores reais das variáveis do processo transmitidos pelos transmissores wireless, pois o controlador preditivo baseado em modelo (MPC) se utiliza do submodelo interno das variáveis de processo para estimar os valores das variáveis quando estas não são transmitidas. Dessa forma, uma diminuição da frequência de transmissão de dados na rede sem fio pode ser obtida e, consequentemente uma redução do consumo energético dos dispositivos sem fio. Resultados de simulações em diferentes condições de operação de um WNCS multivariável de controle de tanques acoplados demonstram que o MPC multi-rate possui características de robustez e é efetivo para aplicações de WNCS, garantindo requisitos de controle e estabilidade mesmo com a diminuição da frequência de transmissão de dados de realimentação na rede sem fio. Adicionalmente, resultados do consumo energético dos dispositivos do WNCS mostraram que o MPC multi-rate proporciona uma economia de energia de até 20% das baterias dos transmissores wireless. Uma análise da eficiência energética do WNCS é apresentada através do estudo dos limites operacionais do controlador MPC multi-rate considerando a relação de compromisso entre o período de amostragem dos dispositivos sem fio e o desempenho de controle do WNCS. / Wireless communication technology has become a fundamental part of the everyday life of process industries, where the use of wireless transmitters for monitoring and control is already a reality. The architecture of Wireless Networked Control Systems (WNCSs) has advantages over point-to-point and wired networks architectures due to the ease of installation, configuration and maintenance. However, the evolution of this technology has introduced new challenges to the implementation of the closed loop control with a wireless instrument as nonlinearities, packet losses and data communication constraints in the wireless networks. Another critical factor related to implementation of WNCSs is the energy source of these transmitters, which have limited lifetime dependent on the amount of access and data transmitted. This work presents the study and the development of a multi-rate predictive controller as an alternative to improve energy efficiency in industrial applications of WNCSs. The proposed strategy does not need to frequently receive updated process variables transmitted by wireless transmitters, because the model predictive controller (MPC) uses the internal submodel of the process variables to estimate the variables values when they are not transmitted. Thus, a decrease in the frequency of data transmission on the wireless network can be obtained and consequently a reduction of energy consumption of wireless devices. Simulation results for different operating conditions of a multivariable WNCS of coupled tanks shows that the multi-rate MPC provides robustness and it is effective for WNCS applications, ensuring control and stability requirements even with the reduction of the transmission frequency of the feedback data in the wireless network. In addition, energy consumption results from the WNCS devices showed that MPC multi-rate provides 20% of energy economy as it is effective in saving the energy expenditure of the wireless transmitter’s battery. An energy efficiency analysis of the WNCS is presented by studying the operating limits of the multi-rate MPC controller considering the compromise relationship between the sampling period of the wireless devices and the control performance of the WNCS.

Esse trabalho apresenta uma contribuição para o projeto de um controlador MPC robusto quanto à estabilidade baseado na realimentação da saída e admitindo restrições nas entradas e incertezas no modelo da planta. Ele estende a abordagem existente para o projeto de um MPC considerando o caso particular de um modelo em espaço de estados, onde o estado é lido diretamente da planta, sendo aplicado para a situação em que o sistema escolhido de entradas possa ficar saturado ou que o processo seja representado por um modelo diferente do modelo considerado na função objetivo do controlador. Para isso, o MPC se propõe a resolver o problema de otimização em dois estágios: No estágio off-line, vários controladores sem restrição são obtidos a partir de um problema de otimização onde inequações de Lyapunov são acrescentadas ao problema como restrições de forma a garantir a contração do estado (estabilidade). Esses controladores, representados por uma matriz de ganhos, correspondem a todas configurações possíveis de saturação das variáveis manipuladas para um dado conjunto possível de variáveis controladas. Nessas combinações, incluídas como restrições no controlador, todos os modelos previstos para o processo são considerados. Dessa forma, perdendo-se uma entrada, o subconjunto de saídas controladas pode ser alterado.Na versão anterior do método proposto por Rodrigues & Odloak (2005), esse estágio off-line envolve um observador de estados o que dificulta a solução do problema de otimização do MPC robusto, consumindo grande tempo computacional. Além disso, requer uma solução inicial viável que nem sempre é trivial. Com a versão proposta do sistema de modelo espaço estado, o estimador de estado torna-se desnecessário pois o estado passa a ser medido. Na etapa on-line do projeto do controlador, uma lei ótima de controle é obtida a partir da combinação convexa das configurações de controle que correspondem ao conjunto de variáveis manipuladas disponíveis (não saturadas). Também nessa etapa é considerada a incerteza do modelo utilizado pelo controlador. O controlador proposto é testado com alguns exemplos simulados a partir de modelos obtidos na indústria de processo. / In this work, it is presented a contribution to the design of a robust MPC with output feedback, input constraints and uncertain model. This work extends existing approaches by considering a particular non-minimal state space model, which transforms the output feedback strategy into a state feedback strategy. The controller is developed to the case in which the system inputs may become saturated and the model is uncertain. We follow a two stages approach: In the off-line stage, a series of unconstrained robust MPCs is obtained by including in the control optimization problem, inequality constraints that force the state of the closed-loop system to contract along the time. Each of these controllers, represented by a gain matrix, is associated to particular sets of manipulated inputs and controlled outputs. When one manipulated input becomes saturated, we may need to reduce the set of controlled variables. In the existing version of the method, the closed loop system involves a state observer that makes the solution to the robust MPC optimization problem a time consuming step. The problem also requires an initial solution that may not be trivial to find. With the adopted version of the system state space model, the state filter becomes trivial and the state can be considered measured. In the on-line step of the proposed controller design, a sub optimal control law is obtained by combining control configurations that correspond to particular subsets of available manipulated inputs. The method is illustrated with simulation examples of the process industry.

<p>To reduce greenhouse gas emissions, shifting the energy sources used in buildings, transportation, industry, etc., from fossil fuels to clean electricity is a trend. The increasing electricity demand stresses the existing electric grids. Buildings consume 73% of all U.S. electricity and are responsible for 30% of U.S. greenhouse gas emissions.  Residential and commercial buildings' space heating/cooling system consumes considerable electricity. Integrating thermal energy storage (TES) in building heating/cooling systems can mitigate the challenge of electric grids. Applying TES to existing air-source heat pump (ASHP) systems is the most studied for residential buildings. However, the high-quality thermal energy requirement for charging the TES tank results in low thermal performance of the ASHP system. Moreover, the failure of ASHP in cold climates requires a supplemental electric heater that significantly reduces the system efficiency and may lead to a higher annual peak for the grids.</p> <p>This study proposes integrating TES with ground-source heat pump (GSHP) systems as a more effective solution for building decarbonization due to the high efficiency of renewable-energy-based GSHPs year-round. This study focuses on proving the effectiveness of TES-integrated GSHPs for building decarbonization.  A dual-source heat pump (DSHP) with a hybrid TES and ground heat exchanger (GHE) named dual-purpose underground thermal battery (DPUTB) is investigated. The study uses modeling and experiments to verify the system's energy efficiency, decarbonization potential, and demand response capability. The modeling process involves developing various models, from component-level to system-level, and investigating advanced control strategies. A first-of-this-kind dynamic model of the DPUTB is developed to enable high-resolution system simulation for the GSHP system. The simulation is conducted using Modelica with rule-based control (RBC). A model predictive control (MPC) is also developed based on dynamic building envelope and heating, ventilation, and air conditioning (HVAC) system models. A cutting-edge co-simulation testbed integrates Modelica physical models with a MATLAB MPC controller model for advanced control evaluation. A prototype system of the DPUTB+DSHP is tested in a flexible research platform (FRP) at Oak Ridge National Laboratory (ORNL), which allows for component and system-level testing and remote automation controls. </p> <p>The study highlights the importance of proper insulation in the performance of the DPUTB, which consists of a TES tank enclosed by an outer tank functioning as a GHE. With appropriate insulation, a full-size DPUTB can store 1-ton cooling (3.5 kW) for four hours after eight hours of charging. Simulation results suggest that decoupling the TES with the GHE could reduce energy consumption by 27%.  System-level simulations confirm that the DSHP+DPUTB system, with a customized RBC, outperforms the conventional ASHP. The proposed system can reduce the annual HVAC electricity cost by up to 50% while saving 45% on electricity consumption. In the Northern areas of the United States, the annual peak load of the HVAC system can be reduced by 60%.  However, this reduction is less in the Southern parts of the as the system's higher efficiency in winter dominates the overall decrease. The application of MPC can further reduce the cost and energy consumption of the system by 35% theoretically. However, the accuracy of model prediction affects its performance in practical applications, which can be mitigated by employing technologies such as machine learning and reinforcement learning. Further research is required to verify these technologies.</p> <p>The DSHP+DPUTB system, a type of TES-integrated GSHP, has been well-designed and demonstrated superior performance to conventional systems, with greater flexibility and thermal efficiency. As a result, this system can enable electrification in the space heating sector without requiring an escalation in the grid. Moreover, alternative controls can be utilized to exploit its decarbonization potential fully.</p>

Temperature control is a widely researched topic and a common application is in heating systems such as buildings. A temperature control method that is central in ensuring comfort and reduction of energy consumption in modern buildings and other heating systems is based on model predictive control (MPC). Traditionally, the MPC optimal control problem is to track a target, but there are other examples of optimization problems besides tracking problems and one such optimization problem is the economical optimization problem, an optimization based on economical objectives. A heating system with electrical supply may be controlled by an economic MPC such that the economical objective is to consider time-varying prices of electricity.  This thesis studies how time-varying prices of electricity can be utilized as an economical objective in an economical MPC to reduce electricity costs for a double-heater system. This is done using an available model of the double-heater system and an MPC to construct an economical MPC. The performance of the economical MPC is then investigated and compared to the existing MPC.  In the thesis it is found, through a test with six different cost profiles and a test with historical data of forecasts of electricity prices, that the economical MPC can reduce total electricity costs when compared to the existing MPC. Furthermore it is found that the performance of the economic MPC is acceptable when it is compared with and without prediction of setpoint changes, prediction of price changes and an isolating layer between the heaters. The thesis concludes that satisfactory results are attained, as the economical MPC leads to decreased total electricity costs for the double-heater system and notes that the economic MPC is versatile by accepting both user-defined and historical cost profiles.

An Over-Actuated (OA) vehicle is a system that presents more control variables than degrees of freedom. Therefore, more than one configuration of the control input can drive the system to a desired state in the state space, and this redundancy can be exploited to fulfill other tasks or solve further problems. In particular, nowadays, challenges concerning electric vehicles regarding their autonomy and solutions to reduce energy consumption are becoming more and more attractive. OA vehicles, on this problem, offer the possibility of using the redundancy to choose the control input, among possible ones, so as to minimize energy consumption. In this regard, the research objective is to investigate different techniques to control in real-time an over-actuated autonomous driving vehicle to guarantee trajectory following and stability with the aim of minimizing energy consumption. The research project focuses on a vehicle able to drive and steer the four wheels (4WD, 4WS) independently. This work extends the contribution of previous theoretical energy-based research developed and provides a control algorithm that must work in real-time on a prototype vehicle (RCV-E) developed at the Integrated Transport Research Lab (ITRL) within KTH with the over-actuation investigated. To this end, the control algorithm has to balance the complexity of a multi-input system, the optimal allocation objectives, and the agility to run in real-time on the MicroAutoBox II - dSPACE system mounted on the vehicle. The solution proposed is a two-level controller which handles separately high and low-rate dynamics with an adequate level of complexity. The upper level is responsible for trajectory following and energy minimization. The allocation problem is solved in two steps. A Linear Time-Varying Model Predictive Controller (LTV-MPC) solves the trajectory-following problem and allocates the forces at the wheels considering the wheel energy losses due to longitudinal and lateral sliding. The second step re-allocates the longitudinal forces between the front and rear axles by considering each side of the vehicle independently to minimize energy loss in the motors. The lower level is responsible for transforming the forces at the wheels into torques and steering angles; it runs at a faster rate than the upper level to account for the high-frequency dynamics of the wheels. Last, the overall control strategy is tested in simulation concerning the trajectory-following and energy minimization performance. The real-time performance are assessed on MircoAutoBox II, the control interface used on the RCV-E. / Ett fordon med olika grad av över-aktuering är ett system som har fler kontrollvariabler än frihetsgrader. Därför kan mer än en konfiguration av styrinmatningen driva systemet till ett önskat tillstånd i tillståndsrummet, och denna redundans kan utnyttjas för att utföra andra uppgifter eller lösa andra problem. I synnerhet blir det i dag allt mer attraktivt med utmaningar som rör elfordon när det gäller deras självklörande drift och lösningar för att minska energiförbrukningen. Överaktuerat fordon ger möjlighet att använda redundansen för att välja en av de möjliga styrinmatningarna för att minimera energiförbrukningen. Forskningsmålet är att undersöka olika tekniker för att i realtid styra ett självkörande fordon som är överaktuerat för att garantera banföljning och stabilitet i syfte att minimera energiförbrukningen. Forskningsprojektet är inriktat på ett fordon som kan köra och styra de fyra hjulen (4WD, 4WS) självständigt. Detta arbete utökar bidraget från den tidigare teoretisk energi-baserade forskning som utvecklats genom att tillhandahålla en regleralgoritm som måste fungera i realtid på ett prototypfordon (RCV-E) som utvecklats vid ITRL inom KTH med den undersökta överaktueringen. I detta syfte måste regleralgoritmen balansera komplexiteten hos ett system med flera ingångar, målen för optimal tilldelning och smidigheten samt att fungera i realtid på MicroAutoBox II - dSPACE-systemet som är monterat på fordonet. Den föreslagna lösningen är en tvåstegsstyrning som hanterar dynamiken med hög och låg hastighet separat med en lämplig komplexitetsnivå. Den övre nivån ansvarar för banföljning och energiminimering. Tilldelningsproblemet löses i två steg. En LTV-MPC löser banföljningsproblemet och fördelar krafterna på hjulen med hänsyn till energiförlusterna på hjulen på grund av longitudinell och lateral glidning. I det andra steget omfördelas de längsgående krafterna mellan fram- och bakaxlarna genom att varje fordonssida beaktas oberoende av varandra för att minimera energiförlusterna i motorerna. Den lägre nivån ansvarar för att omvandla krafterna vid hjulen till vridmoment och styrvinklar; den körs i snabbare takt än den övre nivån för att ta hänsyn till hjulens högfrekventa dynamik. Slutligen testas den övergripande reglerstrategin i simulering med avseende på banföljning och energiminimering, och därefter på MircoAutoBox II monterad på RCV-E för att bedöma realtidsprestanda. / Un veicolo sovra-attuato è un sistema che presenta più variabili di controllo che gradi di libertà. Pertanto, più di una configurazione dell’ingresso di controllo può portare il sistema a uno stato desiderato nello spazio degli stati e questa ridondanza può essere sfruttata per svolgere altri compiti o risolvere ulteriori problemi. In particolare, al giorno d’oggi le sfide relative ai veicoli elettrici per quanto riguarda la loro autonomia e le soluzioni per ridurre il consumo energetico stanno diventando sempre più interessanti. I veicoli sovra-attuati, riguardo a questo problema, offrono la possibilità di utilizzare la ridondanza per scegliere l’ingresso di controllo, tra quelli possibili, che minimizza i consumi energetici. A questo proposito, l’obiettivo della ricerca è studiare diverse tecniche per controllare, in tempo reale, un veicolo a guida autonoma sovra-attuato per garantire l’inseguimento della traiettoria e la stabilità con l’obiettivo di minimizzare il consumo energetico. Questo studio si concentra su un veicolo in grado di guidare e sterzare le quattro ruote (4WD, 4WS) in modo indipendente, ed estende il contributo delle precedenti ricerche teoriche fornendo un algoritmo di controllo che deve funzionare in tempo reale su un prototipo di veicolo (RCV-E) sviluppato presso l’ITRL all’interno del KTH, che presenta la sovra-attuazione studiata. A tal fine, l’algoritmo di controllo deve bilanciare la complessità di un sistema a più ingressi, gli obiettivi di allocazione dell’azione di controllo ottimale e l’agilità di funzionamento in tempo reale sul sistema MicroAutoBox II - dSPACE montato sul veicolo. La soluzione proposta è un controllore a due livelli che gestisce separatamente le dinamiche ad alta e bassa frequenza. Il livello superiore è responsabile dell’inseguimento della traiettoria e della minimizzazione dell’energia. Il problema di allocazione viene risolto in due fasi. Un LTV-MPC risolve il problema dell’inseguimento della traiettoria e assegna le forze alle ruote tenendo conto delle perdite di energia agli pneumatici dovute al loro scorrimento longitudinale e laterale. Il secondo passo rialloca le forze longitudinali tra l’asse anteriore e quello posteriore considerando ciascun lato del veicolo in modo indipendente per minimizzare le perdite di energia nei motori. Il livello inferiore è responsabile della trasformazione delle forze alle ruote in coppia e angolo di sterzo; funziona a una più alta frequenza rispetto al livello superiore per tenere conto delle dinamiche veloci delle ruote. Infine, la strategia di controllo viene testata in simulazione per quanto riguarda le prestazioni di inseguimento della traiettoria e di minimizzazione dell’energia, e successivamente su MircoAutoBox II montato sull’RCV-E per valutare le prestazioni in tempo reale.

<p dir="ltr">The growing movement toward sustainable use of ocean resources is driven by the pressing need to alleviate environmental and human stressors on the planet and its oceans. From monitoring the food web to supporting sustainable fisheries and observing environmental shifts to protect against the effects of climate change, ocean observations significantly impact the Blue Economy. Acknowledging the critical role of Autonomous Underwater Vehicles (AUVs) in achieving persistent ocean exploration, this research addresses challenges focusing on the limited energy and storage capacity of AUVs, introducing a comprehensive underwater docking solution with a specific emphasis on enhancing the terminal homing phase through innovative vision algorithms leveraging neural networks.</p><p dir="ltr">The primary goal of this work is to establish a docking procedure that is failure-tolerant, scalable, and systematically validated across diverse environmental conditions. To fulfill this objective, a robust dock detection mechanism has been developed that ensures the resilience of the docking procedure through \comment{an} improved detection in different challenging environmental conditions. Additionally, the study addresses the prevalent issue of data sparsity in the marine domain by artificially generating data using CycleGAN and Artistic Style Transfer. These approaches effectively provide sufficient data for the docking detection algorithm, improving the localization of the docking station.</p><p dir="ltr">Furthermore, this work introduces methods to compress the learned docking detection model without compromising performance, enhancing the efficiency of the overall system. Alongside these advancements, a station-keeping algorithm is presented, enabling the mobile docking station to maintain position and heading while awaiting the arrival of the AUV. To leverage the sensors onboard and to take advantage of the computational resources to their fullest extent, this research has demonstrated the feasibility of simultaneously learning docking detection and marine wildlife classification through multi-task and transfer learning. This multifaceted approach not only tackles the limitations of AUVs' energy and storage capacity but also contributes to the robustness, scalability, and systematic validation of underwater docking procedures, aligning with the broader goals of sustainable ocean exploration and the blue economy.</p>

This master thesis project is focused on the development of an LQR controller and its comparison with other controllers (PID and MPC), in order to successfully control an Autonomous Underwater Vehicle manipulation system. The modelling of the manipulator was performed first in Matlab and later on in Simulink-Simscape. Once the manipulator was integrated with the AUV model, the LQR controller was also developed initially in Matlab and then in Simulink. The controller was then extracted from Simulink as a C-code and verified in Stonefish. After confirming that the LQR code was working in Stonefish, its results from Simulink were compared with PID and MPC results for two different trajectories. The data for comparison and statistical analysis were divided into the two trajectory scenarios (horizontal and vertical) since the weight matrices of both controllers were different. Looking at the system’s overall behavior the Model Predictive Control (MPC) and LQR had similar results, regarding the rise time, overshoot, steady-state error and robustness to disturbances. An anticipated fact for the MPC was that it takes the longest run time for both scenarios. Lastly, as expected the PID had the worst response of all three controllers, in both scenarios. Implementing a PID on a nonlinear system, produced many oscillations without being able to stabilize at the reference value, thus giving a large steady-state error. In addition, it could not counteract the noise disturbances in the signal. / Detta examensarbete är inriktat på utvecklingen av en LQR-styrenhet och dess jämförelse med andra kontroller (PID och MPC), för att framgångsrikt styra ett autonomt undervattensfordon-manipulationssystem. Modelleringen av manipulatorn utfördes först i Matlab och senare i Simulink-Simscape. När manipulatorn väl hade integrerats med AUV modellen, utvecklades LQR styrenheten också inledningsvis i Matlab och sedan i Simulink. Kontrollenheten extraherades sedan från Simulink som en C-kod och verifierades i Stonefish. Efter att ha bekräftat att LQR koden fungerade i Stonefish, jämfördes resultaten från Simulink med PID och MPC resultat för två olika banor. Data för jämförelse och statistisk analys delades in i de två bana-scenarierna (horisontella och vertikala), eftersom viktmatriserna för båda kontrollerna var olika. När man tittar på systemets övergripande beteende hade Model Predictive Controller (MPC) och LQR liknande resultat när det gäller stigningstid, överskott, steady-state fel och robusthet mot störningar. Ett förväntat faktum för MPC var att det tar den längsta körtiden för båda scenarierna. Slutligen, som väntat, hade PID det sämsta svaret av alla tre kontrollerna, i båda scenarierna. Implementering av ett PID på ett olinjärt system gav många oscillationer utan att kunna stabilisera sig vid referensvärdet, vilket gav ett stort steady-state fel. Dessutom kunde den inte motverka bullerstörningarna i signalen.