Arbetet har utförts på Saab Bofors Dynamics AB i Linköping och dess syfte är att undersöka möjligheten att applicera teorin för prediktionsreglering, Model Predictive Control (MPC), på guidance systemet i en missil av typen Medium Range Air-to-Air Missiles (MRAAM). Även implementering via Explicit MPC har undersökts. I tidigare studier har det visat sig att den moderna slutfasstyrningsalgoritmen Linear Quadratic Augmented Proportional Navigation (LQAPN), som återkopplar missilens acceleration och rotation, uppvisar en bättre prestanda än de mer klassiska styrlagarna. Det främsta intresset med denna studie är därför att undersöka hur tillvida en styrlag baserad på MPC kan mäata sig med dessa resultat. Fördelen med att använda MPC är framförallt att man kan ta hänsyn till styrsignalbegränsningar på ett direkt och intuitivt sätt. En nackdel med MPC är beräkningstiden. På senare år har dock forskning bedrivits för att ta fram en variant av MPC som beräknar styrsignalen explicit som en affin funktion av det aktuella tillståndet. Denna metod kallas Explicit MPC och har betraktats som en separat metod i detta arbete. Styrlagen baserad på MPC kallas i detta arbete för Model Predictive Control Augmented Proportional Navigation (MPCAPN) och utmärker sig framförallt i två fall. Dels då så kallade händelsestyrda simuleringar studeras, då den uppvisar ett klart bättre resultat än vad som erhålls med en styrlag baserad på Linear Quadratic Augmented Proportional Navigation (LQAPN). Även vid beräkningar av skjutzoner blir resultaten ibland bättre. Framförallt förbättras den inre skjutgränsen för flygscenariet då målet utför en så kallad ”tunnelroll”.

This work deals with the predictive control algorithms of the AC drives. The introductory section contains summary of current state of theory and further description and classification of most significant predictive algorithms. A separate chapter is dedicated to linear model predictive control (linear MPC). The main contribution of this work is the introduction of two new predictive control algorithm for PMSM motor, both of which are based on linear MPC. The first of these algorithms has been created with the aim of minimizing its computational demands, while the second algorithm introduces the ability of field weakening. Both new algorithms and linear MPC were simulated in MATLAB-Simulink.

Les convertisseurs multicellulaires DC-DC sont utilisés dans de nombreuses applications et de nombreux systèmes électriques. Ils présentent un intérêt particulier pour des applications spécifiques liées aux énergies renouvelables et aux Microgrids. Leur principal avantage provient de leur capacité intrinsèque à réduire les ondulations liées au découpage des grandeurs électriques en entrée et en sortie du système de conversion. Cette propriété intéressante au niveau système peut être étendue au fonctionnement interne du convertisseur en adjoignant à ce dernier un élément de filtrage par inductances couplées magnétiquement. Ce composant permet d’étendre les propriétés externes de réduction des ondulations au fonctionnement de chaque cellule du convertisseur. Il permet également d’augmenter la dynamique propre du système de conversion. Ces propriétés permettent de réduire significativement le niveau et le volume de filtrage en entrée et sortie du convertisseur et donc d’augmenter de manière importante sa compacité et son rendement énergétique. Cependant, l’ajout de ce dispositif magnétique induit, de par le couplage des équations du système qu’il provoque, une complexification du contrôle de la structure associée également à la nécessité d’augmenter le nombre de capteurs.Ce travail de thèse a pour objectif d’établir et d’évaluer différents modes de contrôle pour les convertisseurs multicellulaires DC-DC. Le point commun aux méthodes proposées est de permettre la gestion aussi bien des grandeurs externes au convertisseur que des grandeurs internes constituées par les courants de circulation entre cellules connectées en parallèle. Ces composantes de courant sont également nommées « courants différentiels ». Trois types de contrôle sont étudiés : Pour le premier, des correcteurs linéaires classiques sont utilisés conjointement avec des techniques de découplage des équations du système. La robustesse de ces méthodes de contrôle vis-à-vis des incertitudes sur la connaissance des paramètres du système fait l’objet d’un focus particulier dans cette partie du travail. Pour le second, une version modifiée de la technique de commande connue sous le nom Model Predictive Control est proposée. Celle-ci permet d’assurer le contrôle de la fréquence de commutation et l’entrelacement des commandes PWM des cellules. Pour le troisième mode, nous étudions une méthode basée sur le contrôle vectoriel direct des courants différentiels.Une implantation sur un système numérique équipé d’un micro-processeur et d’un FPGA est proposée et permet de valider les résultats de l’étude théorique. / The interleaved multicell DC-DC power converters are broadly used in many applications and systems especially in renewable energy systems and microgrids. They reduce the current ripple at the input and output side. Also, an implemented magnetic coupling between cells leads to reduce the current ripple in each of them and to improve the dynamical electrical behavior. These properties involve a reduction on the filtering requirements and so, allow to improve the converter compactness as well as its conversion efficiency. Nevertheless, for such power converters, the control complexity is also increased as well as the number of required sensors.The thesis aims to establish different mode of control of interleaved multicell DC-DC converters. The common point of these methods is to control the external quantities at the output of the converter but also the internal quantities, constituted by the circulating currents between parallel cells or in other words the differential currents. Three main strategies are investigated: the first one uses classical linear controllers with different decoupling technics and focuses on the robustness regarding the system parameters variations. The second one uses a Model Predictive Control technic which is designed to provide a fix switching frequency and interleaving of the cells PWM commands. The last one presents a space vector direct control of the differential currents.In a last part, these control principles are tested on a prototype and implemented on a Microcontroller and FPGA board in order to carry out an experimental verification.

Recent decades have witnessed the phenomenal success of model predictive control (MPC) in a wide spectrum of domains, such as process industries, intelligent transportation, automotive applications, power systems, cyber security, and robotics. For constrained dynamic systems subject to uncertainties, robust MPC is attractive due to its capability of effectively dealing with various types of uncertainties while ensuring optimal performance concerning prescribed performance indices. But most robust MPC schemes require prior knowledge on the uncertainty, which may not be satisfied in practical applications. Therefore, it is desired to design robust MPC algorithms that proactively update the uncertainty description based on the history of inputs and measurements, motivating the development of adaptive MPC. This dissertation investigates four problems in robust and adaptive MPC from theoretical and application points of view. New algorithms are developed to address these issues efficiently with theoretical guarantees of closed-loop performance. Chapter 1 provides an overview of robust MPC, adaptive MPC, and self-triggered MPC, where the recent advances in these fields are reviewed. Chapter 2 presents notations and preliminary results that are used in this dissertation. Chapter 3 investigates adaptive MPC for a class of constrained linear systems with unknown model parameters. Based on the recursive least-squares (RLS) technique, we design an online set-membership system identification scheme to estimate unknown parameters. Then a novel integration of the proposed estimator and homothetic tube MPC is developed to improve closed-loop performance and reduce conservatism. In Chapter 4, a self-triggered adaptive MPC method is proposed for constrained discrete-time nonlinear systems subject to parametric uncertainties and additive disturbances. Based on the zonotope-based reachable set computation, a set-membership parameter estimator is developed to refine a set-valued description of the time-varying parametric uncertainty under the self-triggered scheduling. We leverage this estimation scheme to design a novel self-triggered adaptive MPC approach for uncertain nonlinear systems. The resultant adaptive MPC method can reduce the average sampling frequency further while preserving comparable closed-loop performance compared with the periodic adaptive MPC method. Chapter 5 proposes a robust nonlinear MPC scheme for the visual servoing of quadrotors subject to external disturbances. By using the virtual camera approach, an image-based visual servoing (IBVS) system model is established with decoupled image kinematics and quadrotor dynamics. A robust MPC scheme is developed to maintain the visual target stay within the field of view of the camera, where the tightened state constraints are constructed based on the Lipschitz condition to tackle external disturbances. In Chapter 6, an adaptive MPC scheme is proposed for the trajectory tracking of perturbed autonomous ground vehicles (AGVs) subject to input constraints. We develop an RLS-based set-membership based parameter to improve the prediction accuracy. In the proposed adaptive MPC scheme, a robustness constraint is designed to handle parametric and additive uncertainties. The proposed constraint has the offline computed shape and online updated shrinkage rate, leading to further reduced conservatism and slightly increased computational complexity compared with the robust MPC methods. Chapter 7 shows some conclusion remarks and future research directions. / Graduate

This thesis work presents a model predictive control (MPC) based trajectory planner forhigh speed lane change and low speed parking scenarios of autonomous four wheel steering(4WS) vehicle. A four wheel steering vehicle has better low speed maneuverabilityand high speed stability compared with normal front wheel steering(FWS) vehicles. TheMPC optimal trajectory planner is formulated in a curvilinear coordinate frame (Frenetframe) minimizing the lateral deviation, heading error and velocity error in a kinematicdouble track model of a four wheel steering vehicle. Using the proposed trajectory planner,simulations show that a four wheel steering vehicle is able to track different type ofpath with lower lateral deviations, less heading error and shorter longitudinal distance. / I detta avhandlingsarbete presenteras en modellbaserad prediktiv kontroll (MPC) -baseradbanplaneringsplan f¨or h¨oghastighetsbanan och l°aghastighetsparametrar f¨or autonomtfyrhjulsdrift (4WS). Ett fyrhjulsdrivna fordon har b¨attre man¨ovrerbarhet med l°ag hastighetoch h¨oghastighetsstabilitet j¨amf¨ort med vanliga fr¨amre hjulstyrningar (FWS). MPC-optimalbanplanerare ¨ar formulerad i en kr¨okt koordinatram (Frenet-ram) som minimerar sidof¨orl¨angningen,kursfel och hastighetsfel i en kinematisk dubbelsp°armodell av ett fyrhjulsstyrda fordon.Med hj¨alp av den f¨oreslagna banaplaneraren visar simuleringar att ett fyrhjulsstyrfordonkan sp°ara olika typer av banor med l¨agre sidof¨orl¨angningar, mindre kursfel ochkortare l¨angsg°aende avst°and.

This thesis work is based on a dynamic programming solution of a fuel optimizing cruise controller that was developed at Scania CV AB last year. Known data of the road ahead, mainly the slope, is used to continuously calculate the optimal torque and gear choices of a given moving vehicle for a certain horizon. The optimization calculations are based on fuel consumption and the vehicle's arrival time to the final destination. This report has been focused on achieving better "driveability" of the cruise controller while still maintaining the good fuel saving qualities that is already there. Simulation is used to evaluate the cruise controller on roads where the wanted data is known. The result is smaller speed variations on at road segments, which will improve a driver's impression of the cruise controller. The great fuel benefits of using roll-techniques in hilly areas is maintained from the previous implementation. The key to the optimal balance between these two behaviors is found using a method that limits the torque usage of the truck to a certain speed interval and then finds exception areas where the torque usage should be unlimited. / Detta examensarbete är baserad på en dynamisk programmeringslösning av en bränsleoptimerande farthållare som utvecklades på Scania CV AB förra året. Känd data om den framförvarande vägen, så som lutningen, används för att beräkna optimalt drivmoment och växelval för ett givet fordon för en viss horizont. Optimeringsberäkningarna baseras på bränsleförbrukning och fordonets ankomsttid till målet. Denna rapport focuserar på att uppnå bättre "körbarhet" för farthållaren och samtidigt behålla de goda bränslebesparande egenskaper som farthållaren redan har. Simulering nyttjas för att analysera farthållaren på vägar där önskad data är känd. Resultatet är mindre hastighetsvariationer på platta vägar, vilket bör förbättra förarens uppfattning av farthållaren. De stora fördelar som kommer med användning av rull-tekniker på kuperade vägsträckor bevaras från den tidigare implementeringen. Nyckeln till optimal balans mellan dessa två körbeteenden är en metod som går ut på att begränsa fordonets momentanvändning till ett visst hastighetsinterval och sedan hitta undantagsområden där momentanvändning borde vara obegränsad.

This master thesis studies the implementation of a Robust MPC controllerin marine vessels on different tasks. A tube based MPC is designed based onsystem linearization around the target point guaranteeing local input to statestability of the respective linearized version of the original nonlinear system.The method is then applied to three different tasks: Dynamic positioningon which recursive feasibility of the nominal MPC is also guaranteed, Speed-Heading control and trajectory tracking with the Line of sight algorithm.Numerical simulation is then provided to show technique’s effectiveness. / Detta examensarbete studerar design och implementering av en robustmodellprediktiv regulator (MPC) för marina fartyg. En tub-baserad MPCär designad baserad på linjärisering av systemdynamiken runt en målpunkt,vilket garanterar local insignal-till-tillstånds stabilitet av det linjäriserade systemet.Metoden är sedan applicerad på tre olika uppgifter: dynamisk positionering,för vilken vi även kan garantera rekursiv lösbarhet för den nominellaregulatorn; riktningsstyrning; och banfötljning med en siktlinje-algoritm. Numeriskasimuleringsstudier bekräftar metodens effektivitet.

The objective of this thesis is to understand the interactions between the computational mechanisms, described by algorithms and software, and the physical world, described by differential equations, in the context of networked systems. Such systems can be denoted as cyber-physical nodes connected over a network. In this work, the power grid is used as a guiding example and a rich source of problems which can be generalized to networked cyber-physical systems. We address specific problems that arise in cyber-physical networks due to the presence of a computational network and a physical network as well as provide directions for future research.

The area of autonomous vehicles is an interesting research topic, which is popular in both research and industry worldwide. Linköping university is no exception and some of their research is based on using Model Predictive Control (MPC) for autonomous vehicles. They are using MPC to plan a path and control the autonomous vehicles. Additionally, they are using different methods (for example deep learning or likelihood) to calculate collision probabilities for the obstacles. These are very complex algorithms, and it is not always easy to see how they work. Therefore, it is interesting to study if a visualization tool, where the algorithms are presented in a three-dimensional way, can be useful in understanding them, and if it can be useful in the development of the algorithms.  This project has consisted of implementing such a visualization tool, and evaluating it. This has been done by implementing a visualization using a 3D library, and then evaluating it both analytically and empirically. The evaluation showed positive results, where the proposed tool is shown to be helpful when developing algorithms for autonomous vehicles, but also showing that some aspects of the algorithm still would need more research on how they could be implemented. This concerns the neural networks, which was shown to be difficult to visualize, especially given the available data. It was found that more information about the internal variables in the network would be needed to make a better visualization of them.

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