Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-04-18T15:37:21Z No. of bitstreams: 1 rodolfolacerdavalle.pdf: 15632691 bytes, checksum: 70a89dc2e4e38eb468aa2013be66ab1f (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-04-19T11:31:54Z (GMT) No. of bitstreams: 1 rodolfolacerdavalle.pdf: 15632691 bytes, checksum: 70a89dc2e4e38eb468aa2013be66ab1f (MD5) / Made available in DSpace on 2017-04-19T11:31:54Z (GMT). No. of bitstreams: 1 rodolfolacerdavalle.pdf: 15632691 bytes, checksum: 70a89dc2e4e38eb468aa2013be66ab1f (MD5) Previous issue date: 2017-02-02 / Esta tese apresenta as etapas de projeto e de implementação de um controlador digital preditivo para regular as correntes de um motor CC de ímãs permanentes sem escovas (do inglês, Permanent Magnets Brushless DC motor) (BLDC) de baixa indutância, trifásico a três fios. Estes tipos de motores são usualmente acionados por conversores eletrônicos de potência com múltiplos estágios, comutados em altas frequências, ou usam filtros adicionais para limitar as ondulações de corrente. As formas de onda da força contra eletromotriz induzida e das correntes retangulares dificulta o projeto e sintonia de controladores lineares. A complexidade desta tarefa aumenta quando se considera a ampla faixa de velocidade de operação do motor. Controladores digitais preditivos são facilmente implementados em processadores digitais de sinais, tendo sido usados com sucesso para regular correntes de diferentes tipos de conversores eletrônicos de potência. Três estratégias de comutação (bipolar, unipolar síncrona e unipolar) são usadas para obter três controladores PWM preditivos para regular as correntes retangulares do motor BLDC, sem a necessidade de filtros ou conversores adicionais. O fato das correntes fluírem por apenas duas das fases do motor em cada intervalo de π/3 rad permite operar o inversor fonte de tensão trifásico (Voltage Source Inverter) (VSI) como um conversor CC-CC em ponte completa. Esta característica simplifica o algoritmo de controle sendo necessário apenas um controlador para as correntes trifásicas. Parcelas para compensar o erro em regime permanente devido ao tempo morto, queda de tensão nos interruptores e atrasos de amostragem e de disparo dos interruptores são incorporadas à lei de controle modificada. Resultados experimentais e de simulação para um motor BLDC trifásico de 5 kW/48 V são apresentados para demonstrar a viabilidade da proposta. O algoritmo de controle do sistema de acionamento do motor BLDC foi implementado usando o controlador digital de sinais TMS320F28335. / This thesis presents the design and implementation steps of a digital predictive controller to regulate a low–inductance, three–phase, three–wire permanent magnet brushless DC (BLDC) motor currents. These types of motors are usually driven by multi-stage converters, switched at high frequencies, or use additional inductances to limit the current ripple. The motor’s trapezoidal back electromotive force and rectangular currents waveforms make the design and the tuning process of linear controllers difficult. This task complexity increases when a wide speed range is considered. Digital predictive controllers are easily implemented using digital signal processors (DSP), as successfully used for different types of power electronic converters currents regulation. Three switching strategies (bipolar, synchronous unipolar e unipolar) are used to obtain three PWM predictive controllers to regulate the rectangular currents of a brushless DC motor, without the need for any additional filters or converter. Due the fact that the currents flow only between two phases of the motor at every π/3 rad period makes possible to operate the three-phase voltage source inverter as a full-bridge DC–DC converter. This feature simplify the control algorithm which requires only one controller to synthesize the three-phase currents. A methodology to eliminate the steady-state error due to blanking time, switches voltage drop, sampling delay and pulse driver delay were incorporated in the modified predictive control law. Experimental and simulation results using a 5 kW/48 V three–phase BLDC motor are presented to demonstrate the feasibility of this proposal. The control algorithm was implemented in a digital signal controller TMS320F28335.

Experience Mapping based Prediction Control (EMPC) is a new type of controller presented in literature, which is based on the concept of Human Motor Control (HMC). During the developmental phase, called the initial learning phase, the controller records the experience in a knowledge base, through online interactions with the system to be controlled. This knowledge base created using the experience maps is termed as Experience Mapped Knowledge Base (EMK). The controller envisages the development of EMK only through interaction with the system, without the need for knowledge of the detailed plant model. The EMPC controls the system through prediction of actions based on the mapped experiences of EMK. Depending on the nature of control required for the system chosen, various strategies can be used to achieve control using the EMK. The above controller has previously been utilized for motion control applications. In the present work an effort has been made to study the suitability of the EMPC for the voltage regulation of switching converters. The plant chosen for the control study is a discontinuous conduction mode (DCM) buck converter. The parameter to be monitored for the purpose of control is the load voltage. The control input from the EMPC to the converter is a duty ratio value based pulse-width modulated (PWM) signal. Two strategies of control have been proposed: steady state control and transient control. Steady state control action maintains the steady state output voltage at the required value for a given load. The transient control action is used to improve the transient performance of the system. Iterative predictive action and iterative transient actions are used to facilitate convergence of the output voltage to within the required range in presence of non-linearities and uncertainties in the system. Impulse action is introduced to further improve the transient performance of the system. The EMPC is compared a proportional-integral (PI) controller for the given DCM buck system.

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>