Le travail de thèse décrit dans ce manuscrit concerne les Systèmes Avancés d’Aide à la Conduite (ADAS) qui sont devenus de nos jours un axe de recherche stratégique chez de nombreux constructeurs automobiles. Ce type de systèmes peuvent être considérés comme la première génération de dispositifs de conduite assistée ou semi-autonome et qui ouvrira la voie aux véhicules pleinement autonomes. La première partie de ce manuscrit concerne l’analyse et la commande pour les applications de contrôle de la dynamique latérale du véhicule – autoguidage par suivi de cible et aide au maintien au centre de la voie (LCA). Dans ce cadre, la sécurité joue un rôle clé, mettant en lumière la mise en oeuvre différentes techniques de commande contrainte pour des modèles linéaires à paramètres variants (LPV). La commande prédictive (MPC) et la commande par interpolation (IBC) ont été sélectionnés dans ce travail. De plus, la conception d’un système de commande robuste qui assure un comportement correct malgré la variation des paramètres du système ou la présence d’incertitudes est une caractéristique critique. Les outils de la théorie de l’invariance positive robuste (RPI) sont pris en considération pour la conception de stratégies de commande robustes LPV par rapport aux larges variations de la vitesse véhicule et aux changements de courbure de la route. Le second axe de cette thèse est la planification optimale de trajectoire pour les manouvres de dépassement et de changement de voie sur autoroute, avec réduction des risques de collision. Pour atteindre cet objectif, la description exhaustive des scénarios possible est présentée, permettant de formuler un problème d’optimisation qui maximise le confort du conducteur et assure la satisfaction des contraintes du système. / The thesis work contained in this manuscript is dedicated to the Advanced Driving Assistance Systems, which has become nowadays a strategic research line in many car companies. This kind of systems can be seen as a first generation of assisted or semi-autonomous driving, that will set the way to fully automated vehicles. The first part focuses on the analysis and control of lateral dynamics control applications - Autosteer by target tracking and the Lane Centering Assistance System (LCA). In this framework, safety plays a key role, bringing into focus the application of different constrained control techniques for linear parametervarying (LPV) models. Model Predictive Control (MPC) and Interpolation Based Control (IBC) have been the selected ones in the present work. In addition, it is a critical feature to design robust control systems that ensure a correct behavior under system's variation of parameters or in the presence of uncertainty. Robust Positive Invariance (RPI) theory tools are considered to design robust LPV control strategies with respect to large vehicle speed variations and curvature of the road changes. The second axis of this thesis is the optimization-based trajectory planning for overtaking and lane change in highways with anti-collision enhancements. To achieve this goal, an exhaustive description of the possible scenarios that may arise is presented, allowing to formulate an optimization problem which maximizes passenger comfort and ensures system constraints' satisfaction.

In model predictive control an optimization problem is solved in every time step, which in real-time applications has to be solved within a limited time frame. When applied on embedded hardware in fast changing systems it is important to use efficient solvers and crucial to guarantee that the optimization problem can be solved within the time frame. In this thesis a path following controller which follows a motion plan given by a motion planner is implemented to steer a truck and trailer system. To solve the optimization problems which in this thesis are quadratic programs the three different solvers DAQP, qpOASES and OSQP are employed. The computational time of the active-set solvers DAQP, qpOASES and the operator splitting solver OSQP are compared, where the controller using DAQP was found the fastest and therefore most suited to use in this application of real-time model predictive control.  A certification framework for the active-set method is used to give complexity guarantees on the controller using DAQP. The exact worst-case number of iterations when the truck and trailer system is following a straight path is presented. Furthermore, initial experiments show that given enough computational time/power the exact iteration complexity can be determined for every possible quadratic program that can appear in the controller.

This compilation thesis presents an overarching framework on the utilization of nonlinear model predictive control(NMPC) for various applications in the context of Unmanned Aerial Vehicle (UAV) path planning and collision avoidance. Fast and novel optimization algorithms allow for NMPC formulations with high runtime requirement, as those posed by controlling UAVs, to also have sufficiently large prediction horizons as to in an efficient manner integrate collision avoidance in the form of set-exclusion constraints that constrain the available position-space of the robot. This allows for an elegant merging of set-point reference tracking with the collision avoidance problem, all integrated in the control layer of the UAV. The works included in this thesis presents the UAV modeling, cost functions, constraint definitions, as well as the utilized optimization framework. Additional contributions include the use case on multi-agent systems, how to classify and predict trajectories of moving (dynamic) obstacles, as well as obstacle prioritization when an aerial agent is in the precense of more obstacles, or other aerial agents, than can reasonably be defined in the NMPC formulation. For the cases of dynamic obstacles and for multi-agent distributed collision avoidance this thesis offers extensive experimental validation of the overall NMPC framework. These works push the limits of the State-of-the-Art regarding real-time real-life implementations of NMPC-based collision avoidance. The works also include a novel RRT-based exploration framework that combines path planning with exploration behavior. Here, a multi-path RRT * planner plans paths to multiple pseudo-random goals based on a sensor model and evaluates them based on the potential information gain, distance travelled, and the optimimal actuation along the paths.The actuation is solved for as as the solutions to a NMPC problem, implying that the nonlinear actuator-based and dynamically constrained UAV model is considered as part of the combined exploration plus path planning problem. To the authors best knowledge, this is the first time the optimal actuation has been considered in such a planning problem. For all of these applications, the utilized optimization framework is the Optimization Engine: a code-generation framework that generates a custom Rust-based solver from a specified model, cost function, and constraints. The Optimization Engine solves general nonlinear and nonconvex optimization problems, and in this thesis we offer extensive experimental validation of the utilized Proximal-Averaged Newton-type method for Optimal Control (PANOC) algorithm as well as both the integrated Penalty Method and Augmented Lagrangian Method for handling the nonlinear nonconvex constraints that result from collision avoidance problems.

Today, the control of heat pumps aims to first and foremost maintain a comfortable indoor temperature. This is primarily done by deciding input power based on outside temperature. The cost of electricity, which can be rather volatile, is not taken into account. Electricity price can be provided on an hourly rate, and since a house can store thermal energy for a duration of time, it is possible to move electricity consumption to hours when electricity is cheap. In this thesis, the strategy used in the developed controller is Model Predictive Control (MPC). It is a suitable strategy because of the ability to incorporate an objective function that can be designed to take the trade-off between indoor temperature and electricity cost into account. The MPC prediction horizon is dynamic as the horizon of known electricity spot prices varies between 12 and 36 hours throughout the day. We model a residential house heated with a ground source heat pump for use in a case analysis. Sampled weather and spot price data for three different weeks are used in computer simulations. The developed MPC controller is compared with a classic \textit{heat curve} controller, as well as with variations of the MPC controller to estimate the effects of prediction and model errors.  The MPC controller is found to be able to reduce the electricity cost and/or provide better comfort and the prioritization of these factors can be changed depending on user preferences. When shifting energy consumption in time it is necessary to store thermal energy somewhere. If the house itself is used for this purpose, variations in indoor temperature must be accepted. Further, accurate modeling of the Coefficient of Performance (COP) is essential for ground source heat pumps. The COP varies significantly depending on operating conditions and the MPC controller must therefore have a correct perception of the COP. Publicly available weather forecasts are of sufficient quality to be usable for future prediction of outside temperature. For future studies, it would be advantageous if better models can be developed for prediction of global radiation. Including radiation in the MPC controller model would enable better comfort with very similar operating costs compared to when the MPC controller does not take radiation into account.

Truck platooning is a technology where trucks drive in a formation with each other with a small distance in between trucks in order to save fuel and reduce emissions. In this project,a distributed method for solving the optimal time problem for every truck in a hub-based transport system will be developed.Each truck will have its own utility function to optimize and is able to adjust its schedule independently. To create and test the method, a simulation of hundreds of trucks in a network of routes was created using the Python language. The results produced by running the simulation were positive and realistic. / Konvojkörning med lastbilar är en teknologi där lastbilar kör i en formation med varandra med små avstånd mellan lastbil för att spara på bränsle och minska utsläppen. I det här projektet kommer en distribuerande metod för att lösa det optimala tidsschemat för varje lastbil i ett navbaserat tranportsystem att utvecklas. Varje lastbil kommer att ha sin egen vinstfunktion att optimera och kommer självständigt att kunna ändra sitt reseschema. För att skapa och testa metoden kördes en simulation som skrevs i Python, och som behandlade hundratals lastbilar i ett nätverk av vägar. Resultaten som simulationen cerade var positiva och realistiska. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

In this dissertation, we focus on the strategy that places and stabilizes the path of an aerial drogue, which is towed by a mothership aircraft using a long flexible cable, onto a horizontally flat orbit by maneuvering the mothership in the presence of wind. To achieve this goal, several studies for towed cable systems are conducted, which include the dynamic modeling for the cable, trajectory generation strategies for the mothership, trajectory-tracking control law design, and simulation and flight test implementations. First, a discretized approximation method based on finite element and lumped mass is employed to establish the mathematical model for the towed cable system in the simulation. Two approaches, Gauss's Principle and Newton's second law, are utilized to derive the equations of motion for inelastic and elastic cables, respectively. The preliminary studies for several key parameters of the system are conducted to learn their sensitivities to the system motion in the steady state. Flight test results are used to validate the mathematical model as well as to determine an appropriate number of cable links. Furthermore, differential flatness and model predictive control based methods are used to produce a mothership trajectory that leads the drogue onto a desired orbit. Different desired drogue orbits are utilized to generate required mothership trajectories in different wind conditions. The trajectory generation for a transitional flight in which the system flies from a straight and level flight into a circular orbit is also presented. The numerical results are presented to illustrate the required mothership orbits and its maneuverability in different wind conditions. A waypoint following based strategy for mothership to track its desired trajectory in flight test is developed. The flight test results are also presented to illustrate the effectiveness of the trajectory generation methods. In addition, a nonlinear time-varying feedback control law is developed to regulate the mothership to follow the desired trajectory in the presence of wind. Cable tensions and wind disturbance are taken into account in the design model and Lyapunov based backstepping technique is employed to develop the controller. The mothership tracking error is proved to be capable of exponentially converging to an ultimate bound, which is a function of the upper limit of the unknown component of the wind. The simulation results are presented to validate the controller. Finally, a trajectory-tracking strategy for unmanned aerial vehicles is developed where the autopilot is involved in the feedback controller design. The trajectory-tracking controller is derived based on a generalized design model using Lyapunov based backstepping. The augmentations of the design model and trajectory-tracking controller are conducted to involve the autopilot in the closed-loop system. Lyapunov stability theory is used to guarantee the augmented controller is capable of driving the vehicle to exponentially converge to and follow the desired trajectory with the other states remaining bounded. Numerical and Software-In-the-Loop simulation results are presented to validate the augmented controller. This method presents a framework of implementing the developed trajectory-tracking controllers for unmanned aerial vehicles without any modification to the autopilot.

In this work, an improved Learning Model Predictive Control (LMPC)architecture for autonomous racing is presented. The controller is referencefree and is able to improve lap time by learning from history data of previouslaps. A terminal cost and a sampled safe set are learned from history data toguarantee recursive feasibility and non-decreasing performance at each lap.Improvements have been proposed to implement LMPC on autonomousracing in a more efficient and reliable way. Improvements have been doneon three aspects. Firstly, system identification has been improved to be runin a more efficient way by collecting feature data in subspace, so that thesize of feature data set is reduced and time needed to run sorting algorithmcan be reduced. Secondly, different strategies have been proposed toimprove model accuracy, such as least mean square with/without lifting andGaussian process regression. Thirdly, for reducing algorithm complexity,methods combining different model construction strategies were proposed.Also, running controller in a multi-rate way has also been proposed toreduced algorithm complexity when increment of controller frequency isnecessary. Besides, the performance of different system identificationstrategies have been compared, which include strategy from newton’s law,strategy from classical system identification and strategy from machinelearning. Factors that can possibly influence converged result of LMPCwere also investigated, such as prediction horizon, controller frequency.Experiment results on a 1:10 scaled RC car illustrates the effectiveness ofproposed improvements and the difference of different system identificationstrategies. / I detta arbete, presenteras en förbättrad inlärning baserad modell prediktivkontroll (LMPC) för autonom racing, styralgoritm är referens fritt och har visatsig att kunna förbättra varvtid genom att lära sig ifrån historiska data från tidigarevarv. En terminal kostnad och en samplad säker mängd är lärde ifrån historiskdata för att garantera rekursiv genomförbarhet och icke-avtagande prestanda vidvarje varv.förbättringar har presenterats för implementering av LMPC på autonom racingpå ett mer effektivt och pålitligt sätt. Förbättringar har gjorts på tre aspekter.Först, för system identifiering, föreslår vi att samlar feature data i delrummet,så att storlek på samlade datamängd reduceras och tiden som krävs för attköra sorteringsalgoritm minskas. För det andra, föreslår vi olika strategierför förbättrade modellnoggrannheten, såsom LMS med/utan lyft och Gaussianprocess regression. För det tredje, För att reducerar komplexitet för algoritm,metoder som kombinerar olika modellbygg strategier föreslogs. Att körastyrenhet på ett multi-rate sätt har också föreslagits till för att reduceraalgoritmkomplexitet då inkrementet av styrfrekvensen är nödvändigt.Prestanda av olika systemidentifiering har jämförts, bland annat, Newtonslag, klassisk systemidentifierings metoder och strategier från maskininlärning.Faktorer som eventuellt kan påverka konvergens av LMPC resultat har ocksåundersökts. Såsom, prediktions horisont, styrfrekvensen.Experimentresultat på en 1:10 skalad RC-bilen visar effektiviteten hos föreslagnaförbättringarna och skillnaderna i olika systemidentifierings strategier.

Yes / The need for the development of clean but still profitable processes and the study of low environmental impact and economically convenient management policies for them are two challenges for the years to come. This paper tries to give a first answer to the second of these needs, limited to the area of discontinuous productions. It deals with the development of a robust methodology for the profitable and clean management of (fed-)batch units under uncertainty, which can be referred to as a robust sustainability-oriented model-based optimization & control strategy. This procedure is specifically designed to ensure elevated process performances along with low-cost utilities usage reduction in real-time, simultaneously allowing for the effect of any external perturbation. In this way, conventional offline methods for process sustainable optimization can be easily overcome since the most suitable management policy, aimed at process sustainability, can be dynamically determined and applied in any operating condition. This leads to a significant step forward with respect to the nowadays options in terms of sustainable process management, that drives towards a cleaner and more energy-efficient future. The proposed theoretical framework is validated and tested on a case study based on the well-known fed-batch version of the Williams-Otto process to demonstrate its tangible benefits. The results achieved in this case study are promising and show that the framework is very effective in case of typical process operation while it is partially effective in case of unusual/unlikely critical process disturbances. Future works will go towards the removal of this weakness and further improvement in the algorithm robustness.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24040号 / 情博第796号 / 新制||情||135(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 加納 学, 教授 大塚 敏之, 教授 下平 英寿 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DGAM

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