Vehicle dynamics virtual sensing and advanced motion control for highly skilled autonomous vehicles

This dissertation is aimed at elucidating the path towards the development of a future generation of highly-skilled autonomous vehicles (HSAV). In brief, it is envisaged that future HSAVs will be able to exhibit advanced driving skills to maintain the vehicle within stable limits in spite of the driving conditions (limits of handling) or environmental adversities (e.g. low manoeuvrability surfaces). Current research lines on intelligent systems indicate that such advanced driving behaviour may be realised by means of expert systems capable of monitoring the current vehicle states, learning the road friction conditions, and adapting their behaviour depending on the identified situation. Such adaptation skills are often exhibited by professional motorsport drivers, who fine-tune their driving behaviour depending on the road geometry or tyre-friction characteristics. On this basis, expert systems incorporating advanced driving functions inspired by the techniques seen on highly-skilled drivers (e.g. high body slip control) are proposed to extend the operating region of autonomous vehicles and achieve high-level automation (e.g. manoeuvrability enhancement on low-adherence surfaces). Specifically, two major research topics are covered in detail in this dissertation to conceive these expert systems: vehicle dynamics virtual sensing and advanced motion control. With regards to the former, a comprehensive research is undertaken to propose virtual sensors able to estimate the vehicle planar motion states and learn the road friction characteristics from readily available measurements. In what concerns motion control, systems to mimic advanced driving skills and achieve robust path-following ability are pursued. An optimal coordinated action of different chassis subsystems (e.g. steering and individual torque control) is sought by the adoption of a centralised multi-actuated system framework. The virtual sensors developed in this work are validated experimentally with the Vehicle-Based Objective Tyre Testing (VBOTT) research testbed of JAGUAR LAND ROVER and the advanced motion control functions with the Multi-Actuated Ground Vehicle “DevBot” of ARRIVAL and ROBORACE.Diese Dissertation soll den Weg zur Entwicklung einer zukünftigen Generation hochqualifizierter autonomer Fahrzeuge (HSAV) aufzeigen. Kurz gesagt, es ist beabsichtigt, dass zukünftige HSAVs fortgeschrittene Fahrfähigkeiten aufweisen können, um das Fahrzeug trotz der Fahrbedingungen (Grenzen des Fahrverhaltens) oder Umgebungsbedingungen (z. B. Oberflächen mit geringer Manövrierfähigkeit) in stabilen Grenzen zu halten. Aktuelle Forschungslinien zu intelligenten Systemen weisen darauf hin, dass ein solches fortschrittliches Fahrverhalten mit Hilfe von Expertensystemen realisiert werden kann, die in der Lage sind, die aktuellen Fahrzeugzustände zu überwachen, die Straßenreibungsbedingungen kennenzulernen und ihr Verhalten in Abhängigkeit von der ermittelten Situation anzupassen. Solche Anpassungsfähigkeiten werden häufig von professionellen Motorsportfahrern gezeigt, die ihr Fahrverhalten in Abhängigkeit von der Straßengeometrie oder den Reifenreibungsmerkmalen abstimmen. Auf dieser Grundlage werden Expertensysteme mit fortschrittlichen Fahrfunktionen vorgeschlagen, die auf den Techniken hochqualifizierter Fahrer basieren (z. B. hohe Schlupfregelung), um den Betriebsbereich autonomer Fahrzeuge zu erweitern und eine Automatisierung auf hohem Niveau zu erreichen (z. B. Verbesserung der Manövrierfähigkeit auf niedrigem Niveau) -haftende Oberflächen). Um diese Expertensysteme zu konzipieren, werden zwei große Forschungsthemen in dieser Dissertation ausführlich behandelt: Fahrdynamik-virtuelle Wahrnehmung und fortschrittliche Bewegungssteuerung. In Bezug auf erstere wird eine umfassende Forschung durchgeführt, um virtuelle Sensoren vorzuschlagen, die in der Lage sind, die Bewegungszustände der Fahrzeugebenen abzuschätzen und die Straßenreibungseigenschaften aus leicht verfügbaren Messungen kennenzulernen. In Bezug auf die Bewegungssteuerung werden Systeme zur Nachahmung fortgeschrittener Fahrfähigkeiten und zum Erzielen einer robusten Wegfolgefähigkeit angestrebt. Eine optimale koordinierte Wirkung verschiedener Fahrgestellsubsysteme (z. B. Lenkung und individuelle Drehmomentsteuerung) wird durch die Annahme eines zentralisierten, mehrfach betätigten Systemrahmens angestrebt. Die in dieser Arbeit entwickelten virtuellen Sensoren wurden experimentell mit dem Vehicle-Based Objective Tyre Testing (VBOTT) - Prüfstand von JAGUAR LAND ROVER und den fortschrittlichen Bewegungssteuerungsfunktionen mit dem mehrfach betätigten Bodenfahrzeug ”DevBot” von ARRIVAL und ROBORACE validiert

Analysis and Control of High-Speed Wheeled Vehicles

In this work we reproduce driving techniques to mimic expert race drivers and obtain the open-loop control signals that may be used by auto-pilot agents driving autonomous ground wheeled vehicles. Race drivers operate their vehicles at the limits of the acceleration envelope. An accurate characterization of the acceleration capacity of the vehicle is required. Understanding and reproduction of such complex maneuvers also require a physics-based mathematical description of the vehicle dynamics. While most of the modeling issues of ground-vehicles/automobiles are already well established in the literature, lack of understanding of the physics associated with friction generation results in ad-hoc approaches to tire friction modeling. In this work we revisit this aspect of the overall vehicle modeling and develop a tire friction model that provides physical interpretation of the tire forces. The new model is free of those singularities at low vehicle speed and wheel angular rate that are inherent in the widely used empirical static models. In addition, the dynamic nature of the tire model proposed herein allows the study of dynamic effects such as transients and hysteresis. The trajectory-planning problem for an autonomous ground wheeled vehicle is formulated in an optimal control framework aiming to minimize the time of travel and maximize the use of the available acceleration capacity. The first approach to solve the optimal control problem is using numerical techniques. Numerical optimization allows incorporation of a vehicle model of high fidelity and generates realistic solutions. Such an optimization scheme provides an ideal platform to study the limit operation of the vehicle, which would not be possible via straightforward simulation. In this work we emphasize the importance of online applicability of the proposed methodologies. This underlines the need for optimal solutions that require little computational cost and are able to incorporate real, unpredictable environments. A semi-analytic methodology is developed to generate the optimal velocity profile for minimum time travel along a prescribed path. The semi-analytic nature ensures minimal computational cost while a receding horizon implementation allows application of the methodology in uncertain environments. Extensions to increase fidelity of the vehicle model are finally provided.Ph.D.Committee Chair: Dr. Panagiotis Tsiotras; Committee Member: Dr. Amy Pritchett; Committee Member: Dr. David Taylor; Committee Member: Dr. Olivier Bauchau; Committee Member: Dr. Wassim Hadda

Measurement and analysis of rally car dynamics at high attitude angles

This research aims to investigate the nature of high β-angle cornering as seen in rallying and in particular the World Rally Championship. This is achieved through a combination of sensor development, on-car measurement and vehicle dynamic simulation. Through the development of novel β-angle measurement technology it has become possible to measure and study vehicle attitude dynamics on loose gravel surfaces. Using this sensor, an understanding of how a rally driver uses the dynamics of the vehicle and surface to maximise performance has been obtained. By combining the new data stream with accepted vehicle dynamic theory, the tyres have been considered and general trends in gravel tyre performance unveiled. Through feedback, these trends have been implemented as a means of tuning a dynamic model to improve realism and permit an analysis of cornering trends in rally cars. Active control systems have been considered that could implement more sophisticated algorithms based on this understanding and potentially use the new sensor information as an input signal. A case study which explores such a possibility is included

Mechatronic guidance of rail vehicles through switches and crossings to enable vehicle-based switching

The research presented in this thesis demonstrates the theory that a mechatronic rail vehicle could be used on conventional switches and crossings (S\&Cs) to reduce wear. Railway track switches withstand high vertical and lateral forces leading to wear and damage. This necessitates a disproportionately high level of maintenance of over 10 \% of total maintenance costs, despite accounting for less than 0.1 \% of the network length. Mechatronically-guided rail vehicles are of paramount importance in addressing the increasing interest in reducing wheel-rail wear across the network and improving guidance and steering. Conventional passively-guided rail vehicles are limited by the mechanical constraints of the suspension elements. Currently, a typical rail vehicle suspension needs to be sufficiently stiff to stabilize the wheelsets while being complaint enough to negotiate curved track profiles. The suspension is therefore a compromise for the contradictory requirements of curving and stability. In mechatronic vehicles, actuators are used with the conventional suspension components to provide pseudo stiffness or damping forces needed to optimise a vehicle for a wide variety of scenarios, which can be positive or negative. This means that the vehicle is not reliant on a sub-optimal combination of passive components. Previous research in the area of mechatronic rail vehicles has shown the performance improvement in different straight or curved track profiles compared to a conventional vehicle. In this thesis, three vehicle configurations discussed previously in the literature, are evaluated on several different track profiles. These are the secondary yaw control (SYC), actuated solid-axle wheelset (ASW) and driven independently-rotating wheelsets (DIRW) steering mechanisms. The vehicle models are implemented in a multi-body simulation software Simpack to obtain high fidelity simulations that are comparable to a real rail vehicle. The DIRW vehicle showed the best performance in terms of reduced wear and minimal flange contact and was therefore chosen for studying its performance on a conventional S\&C. The DIRW vehicle was simulated on a C switch which is the most common on the UK mainline and on a high speed H switch. The results show that the DIRW vehicle gives a significant reduction in wear and reduces flange contact on the through and diverging routes of both S\&Cs. This proves the theory that active vehicles could be used to reduce impact forces at conventional S\&Cs. This could be an intermediate step towards a longer term vision of having a track switch without any moving parts where the switching is vehicle-based instead of track-based. Ultimately, if active elements on the vehicle could fully control the route while the track switch was completely passive i.e. had no moving parts, the reliability of the railways as a transport system would increase significantly. The technology could be combined with electronically-coupled vehicles which could form longer trains on busier routes and decouple to serve intermediary routes

Tien ja renkaan välisen kitkapotentiaalin arviointi inertia-anturin mittausten perusteella alhaisen kitkan olosuhteissa

Electronic driver aids have become commonplace in passenger cars in the last two decades. These systems improve safety by attempting to prevent the vehicle from exceeding the limits of its handling and becoming unstable. Those limits are largely defined by the tire-road friction potential. Consequently, the friction potential is one of the variables used in the control logics of these systems. Thus, by estimating the potential, the effectiveness of electronic driver aids can be significantly improved. The purpose of this thesis is to develop and test the accuracy of a novel friction estimation algorithm that uses the accelerations and yaw, pitch, and roll rates of the vehicle measured with an inertial sensor as its basis. The algorithm was designed to account for the effects of inclination and banking, as they influence the acceleration capabilities of the vehicle and the acceleration measurements. Three different versions of the algorithm were created so that the effects of compensating for inclination and bank angle could be assessed. Additionally, the algorithm was designed in such a way that it should be able to estimate the friction potential accurately in start maneuvers where the steering angle is high. The single-track model was incorporated into the algorithm for this purpose. The algorithm must also detect when the vehicle is on the limits of its handling, as it is only then that the measured friction coefficient is equal to the friction potential. The algorithm accomplishes this by monitoring the states of the driver aids. The algorithm was tested with simulations and experimental tests. The research vehicle was modelled in simulation software, including the most significant electronic driver aids. A variety of acceleration, braking, and cornering maneuvers were performed in order to test the capabilities of the algorithm on roads with varying inclinations and bank angles. The tests focused on low-friction conditions, as friction estimation is at its most beneficial in such circumstances. The results show that this novel algorithm is capable of estimating the friction potential accurately in most acceleration, braking, and cornering situations on inclined, banked, and level roads. However, the results also indicate that accounting for the inclination and the bank angle makes little difference in the friction estimation. The algorithm calculates the tire-road forces largely based on the longitudinal and lateral acceleration measurements of the inertial sensor, which contain a component of gravitational acceleration if the road is not level. Thus, the effects of inclination and bank angle get mostly compensated even in the versions that were not specifically designed to account for them. The results also show that the friction potential estimation produced by the single-track model in high steering angle start maneuvers contains significant error due to the two front tires producing forces in different directions in such situations.Elektronisista ajoavuista on tullut yleisiä henkilöautoissa viimeisten kahden vuosikymmenen aikana. Nämä järjestelmät parantavat turvallisuutta yrittämällä estää autoa ylittämästä suorituskykyrajojaan, jolloin auto muuttuu epästabiiliksi. Kyseiset rajat perustuvat laajalti renkaan ja tien väliseen kitkapotentiaaliin. Kitkapotentiaali on siksi yksi niistä muuttujista, joita nämä järjestelmät käyttävät ohjauslogiikoissaan. Täten ajoapujen toimintaa voidaan tehostaa merkittävästi estimoimalla kitkapotentiaalia. Tämän opinnäytetyön tarkoituksena on luoda uudenlainen kitkaestimointialgoritmi, jonka toiminta perustuu inertia-anturilla mitattaviin auton kiihtyvyyksiin ja kallistumis-, nyökkimis- ja pystykiertymänopeuksiin, ja tutkia sen tarkkuutta. Algoritmi suunniteltiin huomioimaan tien nousu- ja sivuttaiskulmien vaikutus, sillä ne vaikuttavat auton kiihtyvyysrajoihin ja mitattuihin kiihtyvyyslukemiin. Algoritmista luotiin kolme eri versiota, jotta tien kulmien kompensoinnin vaikutusta voitaisiin arvioida. Lisäksi algoritmi suunniteltiin siten, että sen pitäisi kyetä arvioimaan kitkapotentiaalia tarkasti myös sellaisissa liikkeellelähtötilanteissa, joissa ohjauskulma on suuri. Kaksipyörämalli sisällytettiin algoritmiin tätä tarkoitusta varten. Algoritmin on myös kyettävä havaitsemaan, milloin auto on lähellä suorituskykyrajojaan, koska arvioitu kitkakerroin on lähellä kitkapotentiaalia vain silloin. Algoritmi toteuttaa tämän tarkkailemalla ajoapujen tiloja. Algoritmia testattiin simulaatioiden ja koeautolla tehtävien testien avulla. Koeauto ja sen merkittävimmät ajoavut mallinnettiin simulaatio-ohjelmistossa. Monenlaisia kiihdytys-, jarrutus- ja kaarreajoliikkeitä suoritettiin algoritmin kykyjen tutkimiseksi erilaisia kallistuksia sisältävillä teillä. Testit keskittyivät alhaisen kitkan olosuhteisiin, sillä kitkaestimoinnista on eniten hyötyä juuri sellaisissa oloissa. Tulokset näyttävät, että luotu algoritmi kykenee arvioimaan kitkapotentiaalia tarkasti useimmissa kiihdytys-, jarrutus- ja kaarreajotilanteissa mäkisillä, kallistetuilla ja tasaisilla teillä. Tulokset kuitenkin myös osoittavat, että nousu- ja sivuttaiskulman huomiointi algoritmissa tuottaa vain pienen eron kitkaestimoinnissa. Algoritmi laskee rengasvoimat perustuen enimmäkseen inertia-anturin pitkittäis- ja sivuttaiskiihtyvyysmittauksiin, jotka sisältävät putoamiskiihtyvyyskomponentin, mikäli tie ei ole tasainen. Täten nousu- ja sivuttaiskulmien vaikutus kompensoituu enimmäkseen pois niissäkin algoritmiversioissa, joita ei erityisesti suunniteltu huomioimaan kyseisiä kulmia. Tulokset näyttävät myös, että kaksipyörämallin tuottama kitkapotentiaaliarvio suuren ohjauskulman liikkeellelähtötilanteissa sisältää merkittävästi virhettä johtuen siitä, että etupyörät tuottavat tällöin voimaa eri suuntiin

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

Model based detection and reconstruction of road traffic accidents

This thesis describes the detection and reconstruction of traffic accidents with event data recorders. The underlying idea is to describe the vehicle motion and dynamics up to the stability limit by means of linear and non-linear vehicle models. These models are used to categorize the driving behavior and to freeze the recorded data in a memory if an accident occurs. Based on these data, among others the vehicle trajectory is reconstructed with fuzzy data fusion. The side slip angle which is a crucial quantity describing the vehicle stability is estimated with non-linear state observers and Kalman-Filters. The methodologies presented may lead from accident reconstruction considered here to accident avoidance

Compendium in Vehicle Motion Engineering

This compendium is written for the course “MMF062 Vehicle Motion Engineering” at Chalmers University of Technology. The compendium covers more than included in that course; both in terms of subsystem designs and in terms of some teasers for more advanced studies of vehicle dynamics. Therefore, it is also useful for the more advanced course “TME102 Vehicle Modelling and Control”.The overall objective of the compendium is to educate vehicle dynamists, i.e., engineers that understand and can contribute to development of good motion and energy functionality of vehicles. The compendium focuses on road vehicles, primarily passenger cars and commercial vehicles. Smaller road vehicles, such as bicycles and single-person cars, are only very briefly addressed. It should be mentioned that there exist a lot of ground-vehicle types not covered at all, such as: off-road/construction vehicles, tracked vehicles, horse wagons, hovercrafts, or railway vehicles.Functions are needed for requirement setting, design and verification. The overall order within the compendium is that models/methods/tools needed to understand each function are placed before the functions. Chapters 3-5 describes (complete vehicle) “functions”, organised after vehicle motion directions:\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 Chapter 3:\ua0Longitudinal\ua0dynamics\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 Chapter 4:\ua0Lateral\ua0dynamics\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 Chapter 5:\ua0Vertical\ua0dynamicsChapter 1 introduces automotive industry and the overall way of working there and defines required pre-knowledge from “product-generic” engineering, e.g. modelling of dynamic systems.Chapter 2 also describes the subsystems relevant for vehicle dynamics:• Wheels and Tyre\ua0• Suspension\ua0• Propulsion\ua0• Braking System\ua0• Steering System\ua0• Environment Sensing Syste