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Passenger safety and comfort are important aspects in the process of vehicle development. The world is heading towards developing the safest possible vehicle on the road. Using vehicle motion control functions is one of the ways to enhance vehicle stability. These motion control functions need to be developed in an energy optimised way. By complementing some of the development process with virtual models, both the development time and cost could be minimised. Hence, a sustainable way of control function development could be achieved. In order to verify, validate and calibrate vehicle motion control functions, an accurate model of the virtual vehicle is required. Hence, a research question on how good the virtual model needs to be for the purpose has been addressed. This report suggests a framework in order to determine the capabilities of a virtual vehicle.In this report, a comparison study has been carried out by exciting the real car and virtual model of a Volvo XC90 with a focus of covering the six degrees of freedom (Yaw, pitch, roll, longitudinal, lateral and vertical). A semi automated framework that possesses the capability of automating the testing in a virtual platform has been established. From the test results, the virtual vehicle capabilities were determined. Further, in the second part of the report, an example use case has been considered by taking two calibration sets of Electronic stability control (ESC) system in order to verify the previously established framework.The analysis includes various levels of plant and controller complexity such as Model-in-loop, Software-in-loop and Hardware-in-loop and on two different road surfaces, low friction and high friction. From the observations, the virtual models considered correlates well for the purpose of verification and validation. However, for the purpose of calibration, the models need to be fine-tuned in the virtual platform. Furthermore, the correlation on low friction road surface could be improved by simulating the tests using an advanced tyre model. Overall, this study helps in choosing the correct complexity of various subsystems in a vehicle for the purpose of verification, validation and calibration of vehicle motion control functions. / Passagerarsäkerhet och komfort är viktiga aspekter i utvecklingen av ett fordon. Världen är på väg mot att utveckla säkraste möjliga fordon på vägen. Användning av fordonetse rörelsekontrollfunktioner är ett av sätten att förbättra fordonets stabilitet. Dessa rörelsekontrollfunktioner måste utvecklas på ett energioptimerat sätt. Genom att komplettera en del av utvecklingsprocessen med virtuella modeller kan både utvecklingstid och kostnad minimeras. Därför kan ett hållbart sätt att utveckla funktionerna för kontrollfunktioner uppnås. För att verifiera, validera och kalibrera fordonets rörelsekontrollfunktioner krävs en detaljerad modell av ett virtuellt fordon. Därför har en forskningsfråga om hur bra den virtuella modellen måste vara för ändamålet behandlats. Denna rapport föreslår ett ramverk för att bestämma funktionerna hos virtuella fordon.I denna rapport har en jämförelsestudie genomförts genom att excitera den verkliga bilen och den virtuella modellen av en Volvo XC90 med fokus på att täcka de sex frihetsgraderna (gir, nick, roll, längs, lateral, vertikal). Ett semi-automatiserat ramverk som har förmågan att automatisera testningen i en virtuell plattform har skapats. Från testresultaten bestämdes de virtuella fordonsfunktionerna. Vidare har i den andra delen av rapporten ett exempel på användningsfall beaktats genom att man tar två kalibreringsuppsättningar av ESC-system (Electronic Stability Control) för att verifiera det tidigare etablerade ramverket.Analysen innefattar olika nivåer av modell- och styrenhetskomplexitet såsom Model-in-loop, Software-in-loop och Hardware-in-loop och på två olika vägytor, låg friktion och hög friktion. Enligt observationerna är de virtuella modellerna väl korrelerade för verifiering och validering. För kalibreringen måste dock modellerna finjusteras på den virtuella plattformen. Dessutom kunde korrelationen på lågfriktionsvägytan förbättras genom att simulera testerna med hjälp av en avancerad däckmodell. Sammantaget hjälper den här studien att välja rätt komplexitet hos olika delsystem i ett fordon för verifiering, validering och kalibrering av fordonets rörelsekontrollfunktioner.

The transport industry is experiencing a disruption as fully autonomous vehicles are introduced in traffic. The intelligent, driverless vehicles will reduce cost, liberate human effort and increase safety. Today, the hardware technology seems to have reached the required processing power, but the decision-making algorithm still has a long way to go until they’re proven to be road-safe. Among these is the problem of lateral path tracking control. This thesis will consider the lateral control problem with the goal to send the right signal to the steering actuators so that the vehicle follows a predetermined trajectory. The vehicle in question is a triaxial, rigid, electric truck with active steering on both front and rearmost wheels. With servo latency and large inertial parameters in mind, a highly accurate model of the lateral and yaw behavior must be identified in order to predict the vehicle dynamics for a given steering input. Then, the properties of an optimal lateral controller are iteratively improved until a sufficiently low tracking error is obtained. Lastly, the controller is tuned to guarantee robustness for a range of uncertain vehicle parameters. The derived triaxial model with servo actuation is proven to be better at predicting the vehicle dynamics compared to other models common in literature with only one active steering input. When constructing a lateral controller, the importance was shown of considering 1) state feedback control of the lateral error, 2) feedforward control operating on future road curvature, 3) integrating control which combats biases and model errors, 4) using a tailored triaxial model and 5) minimizing the control signal change. Lastly, the derived controller was shown to have a decent stability margin with respect to estimated uncertainties. / Transportbranschen är i ett skifte då helt autonoma fordon införs i trafiken. De intelligenta, förarlösa fordonen minskar kostnader, ökar säkerheten och låter oss människor syssla med annat. Idag verkar det som att hårdvarutekniken har den processorkraft som behövs men de beslutsfattande algoritmerna har fortfarande en lång väg att gå tills de har visat sig vara helt vägsäkra. Bland dessa är problemet med lateral styrningskontroll som kommer ses över i denna avhandling. Fordonet i fråga är en rigid lastbil med tre hjulaxlar och aktiv styrning på både de främre och bakersta hjulen. Med tanke på servofördröjningar och de stora tröghetsparametrarna måste en noggrann modell av dynamiken identifieras för att förutspå responsen för en viss styrvinkel. Därefter utvecklas en optimal lågnivåregulator iterativt tills ett tillräckligt lågt spårningsfel erhållits. Slutligen ställs regulatorn in för att garantera robusthet för ett set av osäkra fordonsparametrar Den härledda triaxialmodellen med servostyrning var bevisbart bättre på att förutspå fordonsdynamiken jämfört med andra modeller som återkommer frekvent i litteraturen. Vid regulatorkonstruktionen påvisades vikten av att överväga 1) återkoppling av laterala felet, 2) förhandsgranskning som tittar på den kommande vägkrökningen, 3) integrering av styrfelet som åtgärdar modellfel, 4) en skräddarsydd fordonsmodell med tre axlar och 5) minimering av ändringen utav kontrollsignalen. Slutligen visades den härledda regulatorn ha en skaplig stabilitetsmarginal gentemot uppskattade osäkerheter av parametrar.

The goal of this thesis is to develop nontraditional strategies to provide motion control for different engineering applications. We focus our attention on three topics: 1) roll reduction of ships in a seaway; 2) response reduction of buildings under seismic excitations; 3) new training strategies and neural-network configurations. The first topic of this research is based on a multidisciplinary simulation, which includes ship-motion simulation by means of a numerical model called LAMP, the modeling of fins and computation of the hydrodynamic forces produced by them, and a neural-network/fuzzy-logic controller. LAMP is based on a source-panel method to model the flowfield around the ship, whereas the fins are modeled by a general unsteady vortex-lattice method. The ship is considered to be a rigid body and the complete equations of motion are integrated numerically in the time domain. The motion of the ship and the complete flowfield are calculated simultaneously and interactively. The neural-network/fuzzy-logic controller can be progressively trained. The second topic is the development of a neural-network-based approach for the control of seismic structural response. To this end, a two-dimensional linear model and a hysteretic model of a multistory building are used. To control the response of the structure a tuned mass damper is located on the roof of the building. Such devices provide a good passive reduction. Once the mass damper is properly tuned, active control is added to improve the already efficient passive controller. This is achieved by means of a neural network. As part of the last topic, two new flexible and expeditious training strategies are developed to train the neural-network and fuzzy-logic controllers for both naval and civil engineering applications. The first strategy is based on a load-matching procedure, which seeks to adjust the controller in order to counteract the loads (forces and moments) which generate the motion that is to be reduced. A second training strategy provides training by means of an adaptive gradient search. This technique provides a wide flexibility in defining the parameters to be optimized. Also a novel neural-network approach called modal neural network is designed as a suitable controller for multiple-input multiple output control systems (MIMO). / Ph. D.

Recent advancements in time-optimal trajectory planning, control, and state estimation for autonomous vehicles have paved the way for the emerging field of autonomous racing. In the last 5-10 years, this form of racing has become a popular and challenging testbed for autonomous driving algorithms, aiming to enhance the safety and performance of future intelligent vehicles. In autonomous racing, the main goal is to develop real-time algorithms capable of autonomously maneuvering a vehicle around a racetrack, even in the presence of moving opponents. However, as a vehicle approaches its handling limits, several challenges arise for online trajectory planning and control. The vehicle dynamics become nonlinear and hard to capture with low-complexity models, while fast re-planning and good generalization capabilities are crucial to execute optimal maneuvers in unforeseen scenarios. These challenges leave several open research questions, three of which will be addressed in this thesis. The first explores developing accurate yet computationally efficient vehicle models for online time-optimal trajectory planning. The second focuses on enhancing learning-based methods for trajectory planning, control, and state estimation, overcoming issues like poor generalization and the need for large amounts of training data. The third investigates the optimality of online-executed trajectories with simplified vehicle models, compared to offline solutions of minimum-lap-time optimal control problems using high-fidelity vehicle models. This thesis consists of four parts, each of which addresses one or more of the aforementioned research questions, in the fields of time-optimal vehicle trajectory planning, control and state estimation. The first part of the thesis presents a novel artificial race driver (ARD), which autonomously learns to drive a vehicle around an obstacle-free circuit, performing online time-optimal vehicle trajectory planning and control. The following research questions are addressed in this part: How optimal is the trajectory executed online by an artificial agent that drives a high-fidelity vehicle model, in comparison with a minimum-lap-time optimal control problem (MLT-OCP), based on the same vehicle model and solved offline? Can the artificial agent generalize to circuits and conditions not seen during training? ARD employs an original neural network with a physics-driven internal structure (PhS-NN) for steering control, and a novel kineto-dynamical vehicle model for time-optimal trajectory planning. A new learning scheme enables ARD to progressively learn the nonlinear dynamics of an unknown vehicle. When tested on a high-fidelity model of a high-performance car, ARD achieves very similar results as an MLT-OCP, based on the same vehicle model and solved offline. When tested on a 1:8 vehicle prototype, ARD achieves similar lap times as an offline optimization problem. Thanks to its physics-driven architecture, ARD generalizes well to unseen circuits and scenarios, and is robust to unmodeled changes in the vehicle’s mass. The second part of the thesis deals with online time-optimal trajectory planning for dynamic obstacle avoidance. The research questions addressed in this part are: Can time-optimal trajectory planning for dynamic obstacle avoidance be performed online and with low computational times? How optimal is the resulting trajectory? Can the planner generalize to unseen circuits and scenarios? At each planning step, the proposed approach builds a tree of time-optimal motion primitives, by performing a sampling-based exploration in a local mesh of waypoints. The novel planner is validated in challenging scenarios with multiple dynamic opponents, and is shown to be computationally efficient, to return near-time-optimal trajectories, and to generalize well to new circuits and scenarios. The third part of the thesis shows an application of time-optimal trajectory planning with optimal control and PhS-NNs in the context of autonomous parking. The research questions addressed in this part are: Can an autonomous parking framework perform fast online trajectory planning and tracking in real-life parking scenarios, such as parallel, reverse and angle parking spots, and unstructured environments? Can the framework generalize to unknown variations in the vehicle’s parameters and road adherence, and operate with measurement noise? The autonomous parking framework employs a novel penalty function for collision avoidance with optimal control, a new warm-start strategy and an original PhS-NN for steering control. The framework executes complex maneuvers in a wide range of parking scenarios, and is validated with a high-fidelity vehicle model. The framework is shown to be robust to variations in the vehicle’s mass and road adherence, and to operate with realistic measurement noise. The fourth and last part of the thesis develops novel kinematics-structured neural networks (KS-NNs) to estimate the vehicle’s lateral velocity, which is a key quantity for time-optimal trajectory planning and control. The KS-NNs are a special type of PhS-NNs: their internal structure is designed to incorporate the kinematic principles, which enhances the generalization capabilities and physical explainability. The research questions addressed in this part are: Can a neural network-based lateral velocity estimator generalize well when tested on a vehicle not used for training? Can the network’s parameters be physically explainable? The approach is validated using an open dataset with two race cars. In comparison with traditional and neural network estimators of the literature, the KS-NNs improve noise rejection, exhibit better generalization capacity, are more sample-efficient, and their structure is physically explainable.

In der vorliegenden Arbeit werden rekurrente neuronale Netze im Hinblick auf ihre Eignung zur Bewegungssteuerung autonomer Roboter untersucht. Nacheinander werden Oszillatoren für Vierbeiner, homöostatische Ringmodule für segmentierte Roboter und monostabile Neuromodule für Roboter mit vielen Freiheitsgraden und komplexen Bewegungsabläufen besprochen. Neben dem mathematisch-theoretischen Hintergrund der Neuromodule steht in gleichberechtigter Weise deren praktische Implementierung auf realen Robotersystemen. Hierzu wird die funktionale Einbettung ins Gesamtsystem ebenso betrachtet, wie die konkreten Aspekte der zugrundeliegenden Hardware: Rechengenauigkeit, zeitliche Auflösung, Einfluss verwendeter Materialien und dergleichen mehr. Interessante elektronische Schaltungsprinzipien werden detailliert besprochen. Insgesamt enthält die vorliegende Arbeit alle notwendigen theoretischen und praktischen Informationen, um individuelle Robotersysteme mit einer angemessenen Bewegungssteuerung zu versehen. Ein weiteres Anliegen der Arbeit ist es, aus der Richtung der klassischen Ingenieurswissenschaften kommend, einen neuen Zugang zur Theorie rekurrenter neuronaler Netze zu schaffen. Gezielte Vergleiche der Neuromodule mit analogen elektronischen Schaltungen, physikalischen Modellen und Algorithmen aus der digitalen Signalverarbeitung können das Verständnis von Neurodynamiken erleichtern. / How recurrent neural networks can help to make autonomous robots move, will be investigated within this thesis. First, oscillators which are able to control four-legged robots will be dealt with, then homeostatic ring modules which control segmented robots, and finally monostable neural modules, which are able to drive complex motion sequences on robots with many degrees of freedom will be focused upon. The mathematical theory of neural modules will be addressed as well as their practical implementation on real robot platforms. This includes their embedding into a major framework and concrete aspects, like computational accuracy, timing and dependance on materials. Details on electronics will be given, so that individual robot systems can be built and equipped with an appropriate motion controller. It is another concern of this thesis, to shed a new light on the theory of recurrent neural networks, from the perspective of classical engineering science. Selective comparisons to analog electronic schematics, physical models, and digital signal processing algorithms can ease the understanding of neural dynamics.

Das Softwarewerkzeug FUP Blockbibliothek wurde für Entwurf, Simulation, Echtzeitkodegenerierung und Dokumentation von ereignisgesteuerten Systemen, speziell in Maschinenbau, Mechatronik und Elektrotechnik entwickelt. Es stellt eine Erweiterung zu MATLAB /Simulink dar und bietet eine umfangreiche Entwurfsunterstützung einschließlich Werkzeugen zur Erkennung von Eingabe- und Strukturfehlern. Die graphische Darstellung (Blockikonen) lehnt sich an die VDI / VDE - Richtlinie 3684 "Beschreibung ereignisgesteuerter Bewegungsabläufe mit Funktionsplänen" an.

Cílem této práce je vývoj dvoukanálového kontroléru krokových motorů. V rámci práce je popsán jak vývoj elektroniky, tak vývoj příslušného software. Elektronika kontrolŕu je založena na mikrokontroléru STM32F405 a driverů krokových motorů vyráběných firmou Trinamic. Pro komunikaci s nadřazenými systémy je implementován protokol CANOpen a sběrnice I\textsuperscript{2}C a USB. Elektronika byla navržena v software KiCAD and využívá čtyřvrstvého plošného spoje a moderních výrobních technologií. Co se týká software, byl vyvinut jak firmware pro mikrokontrolér, tak software pro ovládání kontroléru. Obě části software využívají programovacího jazyka Rust, který se zaměřuje na bezpečnost práce s pamětí, rychlost a zero-cost abstrakce. Sekundárním cílem této práce je ukázat, jak lze tento programovací jazyk s výhodou použít pro programování nízkoúrovňového embedded software. Firmware kontroléru implementuje nezávislé řízení pohybu obou os kontroléru a to jak v rychlostním, tak v pozičním režimu a zároveň implementuje bezpečnostní funkce pro případy selhání komunikace. Výsledný kontrolér by měl být použit v rámcí výzkumné skupiny Robotiky a Umělé Inteligence a studenty na Ústavu Automatizace FEKT VUT.

The master thesis treats the design of control system of special purpose machine tool. The first part analyzes and compares two possible variants of control system which use either a CNC control system Sinumerik or motion control system Simotion. Only the variant with the Simotion control system is subsequently examined in detail: its hardware configuration, the selection of components and partly also the software solution. The thesis further deals with the safety of the realized control system and of the whole machine. There are described general safety requirements of machinery based on harmonized European standards and their specific application in the design of safety functions and the safety circuits of solved machine tool.

Das Softwarewerkzeug FUP Blockbibliothek wurde für Entwurf, Simulation, Echtzeitkodegenerierung und Dokumentation von ereignisgesteuerten Systemen, speziell in Maschinenbau, Mechatronik und Elektrotechnik entwickelt. Es stellt eine Erweiterung zu MATLAB /Simulink dar und bietet eine umfangreiche Entwurfsunterstützung einschließlich Werkzeugen zur Erkennung von Eingabe- und Strukturfehlern. Die graphische Darstellung (Blockikonen) lehnt sich an die VDI / VDE - Richtlinie 3684 "Beschreibung ereignisgesteuerter Bewegungsabläufe mit Funktionsplänen" an.:Einführung S. 3 Wesentliche Eigenschaften S. 4 Anwendungshinweise S. 6 Blockbibliothek S. 11 Beispiele S. 12 Blockbeschreibungen S. 33