Yes / This paper proposes a novel trajectory tracking control approach for nonholonomic wheeled mobile robots. In this approach, the integration of feed-forward and feedback controls is presented to design the kinematic controller of wheeled mobile robots, where the control law is constructed on the basis of Lyapunov stability theory, for generating the precisely desired velocity as the input of the dynamic model of wheeled mobile robots; a proportional-integral-derivative based membrane controller is introduced to design the dynamic controller of wheeled mobile robots to make the actual velocity follow the desired velocity command. The proposed approach is defined by using an enzymatic numerical membrane system to integrate two proportional-integral-derivative controllers, where neural networks and experts’ knowledge are applied to tune parameters. Extensive experiments conducted on the simulated wheeled mobile robots show the effectiveness of this approach. / The work of XW and GZ is supported by the National Natural Science Foundation of China (61170016, 61373047). The work of MG, FI and RL was supported by a grant of the Romanian National Authority for Scientific Research, CNCS-UEFISCDI (project number: PN-II-ID-PCE-2011-3-0688).

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Vehicle recognition and trajectory tracking plays an integral role in many aspects of Intelligent Transportation Systems (ITS) applications; from behavioral modeling and car-following analyses to congestion prevention, crash prediction, dynamic signal timing, and active traffic management. This dissertation aims to improve the tasks of multi-object detection and tracking (MOT) as it pertains to urban traffic by utilizing the domain knowledge of traffic flow then utilize this improvement for applications in real-time traffic performance assessment, safety evaluation, and driver behavior modeling. First, the author proposes an ad-hoc framework for real-time turn count and trajectory reconstruction for vehicles passing through urban intersections. This framework introduces the concept of virtual traffic lanes representing the eight standard National Electrical Manufacturers Association (NEMA) movements within an intersection as spatio-temporal clusters utilized for movement classification and vehicle re-identification. The proposed framework runs as an additional layer to any multi-object tracker with minimal additional computation. The results obtained for a case study and on the AI City benchmark dataset indicate the high ability of the proposed framework in obtaining reliable turn count, speed estimates, and efficiently resolving the vehicle identity switches which occur within the intersection due to detection errors and occlusion. The author then proposes the utilization of the high accuracy and granularity trajectories obtained from video inference to develop a real-time safety-based driver behavior model, which managed to effectively capture the observed driving behavior in the site of study. Finally, the developed model was implemented as an external driver model in VISSIM and managed to reproduce the observed behavior and safety conflicts in simulation, providing an effective decision-support tool to identify appropriate safety interventions that would mitigate those conflicts. The work presented in this dissertation provides an efficient end-to-end framework and blueprint for trajectory extraction from road-side traffic video data, driver behavior modeling, and their applications for real-time traffic performance and safety assessment, as well as improved modeling of safety interventions via microscopic simulation. / Doctor of Philosophy / Traffic crashes are one of the leading causes of death in the world, averaging over 3,000 deaths per day according to the World Health Organization. In the United States alone, there are around 40,000 traffic fatalities annually. Approximately, 21.5% of all traffic fatalities occur due to intersection-related crashes. Intelligent Transportation Systems (ITS) is a field of traffic engineering that aims to transform traffic systems to make safer, more coordinated, and 'smarter' use of transport networks. Vehicle recognition and trajectory tracking, the process of identifying a specific vehicle's movement through time and space, plays an integral role in many aspects of ITS applications; from understanding how people drive and modeling that behavior, to congestion prevention, on-board crash avoidance systems, adaptive signal timing, and active traffic management. This dissertation aims to bridge the gaps in the application of ITS, computer vision, and traffic flow theory and create tools that will aid in evaluating and proactively addressing traffic safety concerns at urban intersections. The author presents an efficient, real-time framework for extracting reliable vehicle trajectories from roadside cameras, then proposes a safety-based driving behavior model that succeeds in capturing the observed driving behavior. This work is concluded by implementing this model in simulation software to replicate the existing safety concerns for an area of study, allowing practitioners to accurately model the existing safety conflicts and evaluate the different operation and safety interventions that would best mitigate them to proactively prevent crashes.

The navigation and control of an autonomous vehicle is a highly complex task. Making a vehicle intelligent and able to operate "unmanned" requires extensive theoretical as well as practical knowledge. An autonomous vehicle must be able to make decisions and respond to situations completely on its own. Navigation and control serves as the major limitation of the overall performance, accuracy and robustness of an autonomous vehicle. This thesis will address this problem and propose a unique navigation and control scheme for an autonomous lawn mower (ALM). Navigation is a key aspect when designing an autonomous vehicle. An autonomous vehicle must be able to sense its location, navigate its way toward its destination, and avoid obstacles it encounters. Since this thesis attempts to automate the lawn mowing process, it will present a navigational algorithm that covers a bounded region in a systematic way, while avoiding obstacles. This algorithm has many applications including search and rescue, floor cleaning, and lawn mowing. Furthermore, the robustness and utility of this algorithm is demonstrated in a 3D simulation. This thesis will specifically study the dynamics of a two-wheeled differential drive vehicle. Using this dynamic model, various control techniques can then be applied to control the movement of the vehicle. This thesis will consider both open loop and closed loop control schemes. Optimal control, path following, and trajectory tracking are all considered, simulated, and evaluated as practical solutions for control of an ALM. To design and build an autonomous vehicle requires the integration of many sensors, actuators, and controllers. Software serves as the glue to fuse all these devices together. This thesis will suggest various sensors and actuators that could be used to physically implement an ALM. This thesis will also describe the operation of each sensor and actuator, present the software used to control the system, and discuss physical limitations and constraints that might be encountered while building an ALM. / Master of Science

Thesis (PhD)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: A strategy for the design of an effective, practically feasible, robust, computationally efficient autopilot for three dimensional manoeuvre flight control of Unmanned Aerial Vehicles is presented. The core feature of the strategy is the design of attitude independent inner loop acceleration controllers. With these controllers implemented, the aircraft is reduced to a point mass with a steerable acceleration vector when viewed from an outer loop guidance perspective. Trajectory generation is also simplified with reference trajectories only required to be kinematically feasible. Robustness is achieved through uncertainty encapsulation and disturbance rejection at an acceleration level. The detailed design and associated analysis of the inner loop acceleration controllers is carried out for the case where the airflow incidence angles are small. For this case it is shown that under mild practically feasible conditions the inner loop dynamics decouple and become linear, thereby allowing the derivation of closed form pole placement solutions. Dimensional and normalised non-dimensional time variants of the inner loop controllers are designed and their respective advantages highlighted. Pole placement constraints that arise due to the typically weak non-minimum phase nature of aircraft dynamics are developed. A generic, aircraft independent guidance control algorithm, well suited for use with the inner loop acceleration controllers, is also presented. The guidance algorithm regulates the aircraft about a kinematically feasible reference trajectory. A number of fundamental basis trajectories are presented which are easily linkable to form complex three dimensional manoeuvres. Results from simulations with a number of different aircraft and reference trajectories illustrate the versatility and functionality of the autopilot. Key words: Aircraft control, Autonomous vehicles, UAV flight control, Acceleration control, Aircraft guidance, Trajectory tracking, Manoeuvre flight control. / AFRIKAANSE OPSOMMING: ’n Strategie vir die ontwerp van ’n effektiewe, prakties haalbaar, robuuste, rekenkundig effektiewe outoloods vir drie dimensionele maneuver vlugbeheer van onbemande vliegtuie word voorgestel. Die kerneienskap van die strategie is die ontwerp van oriëntasie-onafhanklike binnelus-versnellingbeheerders. Hierdie beheerders stel die navigasie buitelus in staat om die voertuig as ’n puntmassa met ’n stuurbare versnellingsvektor te beskou. Trajekgenerasie is ook vereenvoudig deurdat verwysingstrajekte slegs kinematies haalbaar hoef te wees. Robuustheid word verkry deur onsekerhede en versteuringsverwerping op ’n versnellingsvlak te hanteer. Die gedetaileerde ontwerp en saamhangende analise van die binnelus versnellingsbeheerders word uitgevoer vir die geval waar die invalshoeke klein is. Dit word aangetoon dat, onder praktiese omstandighede, die binnelus dinamika ontkoppel kan word en lineêr word, wat die afleiding van geslotevorm poolplasingoplossings toelaat. Dimensionele en genormaliseerde, nie-dimensionele tydvariante van die binnelusbeheerders word ontwerp en hul onderskeidelike voordele word uitgewys. Poolplasing beperkings, wat ontstaan as gevolg van die tipiese geringe nie-minimum fasegedrag van voertuigdinamika, word ontwikkel. ’n Gepaste generiese, voertuig onafhanklike navigasiebeheer algoritme vir gebruik saam met die binnelus-versnellingsbeheerders word voorgestel. Die voertuig word om ’n kinematies haalbare verwysingstrajek deur hierdie navigasie algoritme gereguleer. ’n Aantal fundamentele trajekte word voorgestel wat maklik gekombineer kan word om komplekse drie dimensionele maneuvers te vorm. Die veelsydigheid en funksionaliteit van die outoloods word deur simulasieresultate met ’n verskeidenheid voertuie en verwysingstrajekte gedemonstreer.

Ce travail est développé dans le contexte des projets SESAR et Next-Gen, où de nouvelles applications de la gestion du trafic aérien (ATM) comme le concept de gestion d'opérations en 4D, se sont focalisées sur les opérations basées sur la trajectoire (TBO - Trajectory Based Operations). Ces opérations sont en relation avec l'extension de la flexibilité de la séparation entre avions, et par conséquence, avec l'augmentation de la capacité du trafic aérien. En sachant qu'une évolution des routes fixes et autorisations émises par le contrôle du trafic aérien (ATC - Air Traffic Control) vers des trajectoires flexibles est imminente, en s'appuyant en même temps aux niveaux les plus élevés de l'automatique embarquée, ce travail de recherche s'intéresse aux sujets qui aideront à la transition des systèmes actuels vers les systèmes compatibles avec les nouveaux besoins des TBO. Les principaux axes de recherche de ce manuscrit s'articulent en trois points: La génération de trajectoires en 4D, le guidage en 4D, et l'estimation de la masse d'un avion pour l'optimisation des trajectoires. Concernant la génération des trajectoires, le besoin des utilisateurs d'espaces aériens de planifier leurs routes préférées à partir d'un point d'entrée dans l'espace aérien sans être limités par les configurations existantes est considéré. Une solution particulière pour la génération de trajectoires lisses en 4D à partir de points de contrôle prédéfinis est alors explorée. La méthode proposée s'appuie sur les courbes de Bézier, et elle permet de contrôler la distance euclidienne entre le point de contrôle donné et la trajectoire proposée. Ceci est fait en modifiant la trajectoire de telle façon qu'elle reste à l'intérieur des limites des facteurs de charge, en considérant un compromis entre la courbure de la trajectoire et la vitesse voulue de l'avion, ce qui représente une étape importante dans le chemin vers les TBO. Le guidage précis en 4D améliorera la sûreté en diminuant l'occurrence de quasi-collisions aériennes pour des trajectoires en 4D planifiées en avance. En conséquence, deux autopilotes et deux méthodes de guidage sont développées avec l'objectif de réduire la charge de travail des contrôleurs du trafic aérien associée à un vol. Les techniques de backstepping et feedback linearization sont utilisées pour le pilotage, alors que l'inversion non linéaire directe et indirecte sont adoptées pour le guidage. De plus, l'impact de la connaissance inexacte de la masse de l'avion dans le suivi de trajectoires, ses conséquences dans l'optimisation, la consommation de carburant, et la performance de l'avion, a conduit à l'implémentation d'une estimation embarquée de la masse de l'avion. L'approche créée est basée sur les moindres carrées, en fournissant des estimations de la masse initiale et la masse courante, toutes les deux avec une précision suffisante pour atteindre les objectifs liées aux TBO. Les méthodes proposées dans cette thèse sont examinées en utilisant un modèle à six degrés de liberté, dont les paramètres approchent un appareil du type B737-200 ou A320-200. La simulation est basée sur une modélisation complète et non linéaire de la dynamique des avions de transport incluant des perturbations liées au vent. Des réseaux de neurones sont utilisés pour obtenir les différents coefficients aérodynamiques correspondant aux forces et moments de l'avion. / This work is developed with the perspective of SESAR and Next-Gen projects, where new applications of Air Traffic Management (ATM) such as the Full 4D Management concept, are centered on Trajectory-Based Operations (TBO), deeply related with the extension of the flexibility in separation between aircraft, and hence, with the augmentation of air traffic capacity. Therefore, since a shift from fixed routes and Air Traffic Control (ATC) clearances to flexible trajectories is imminent, while relying on higher levels of onboard automation, the thesis hinges around topics that should enable or ease the transition from current systems to systems compliant with the new expectancies of Trajectory-Based Operations. The main axes of the manuscript can be summarized in three topics: 4D trajectory generation, 4D guidance, and mass estimation for trajectory optimization. Regarding the trajectory generation, the need of airspace users to plan their preferred route from an entry to an exit point of the airspace without being constrained by the existent configurations is considered. Thus, a particular solution for 4D smooth path generation from preexisting control points is explored. The method is based on Bezier curves, and is able to control the Euclidian distance between the given control points and the proposed trajectory. This is done by reshaping the path to remain within load factor limits, taking into account a tradeoff between path curvature and aircraft intended speed, representing a milestone in the road towards Trajectory-Based Operations. It is considered that accurate 4D guidance will improve safety by decreasing the occurrence of near mid-air collisions for planned conflict free 4D trajectories. In consequence, two autopilots and two guidance approaches are developed with the objective of diminishing the workload for air traffic controllers associated to a single flight. The backstepping and feedback linearization techniques are used for attitude control, while direct and indirect nonlinear inversion are adopted for guidance. Furthermore, the impact of inaccurate mass knowledge in trajectory guidance, with consequences in optimization, fuel consumption, and aircraft performance, has led to the implementation of an on-board aircraft mass estimation. The created approach is based on least squares, providing an initial mass estimation, and online computations of the current mass, both with enough accuracy to meet the objectives related to TBO. The methods proposed in this thesis are tested in a six degrees of freedom Matlab model with its parameters chosen similar to an aircraft type B737-200 or A320-200. The simulation is based on a full nonlinear modelling of transport aircraft dynamics under wind disturbances. Trained neural networks are used to obtain the aerodynamic coefficients corresponding the aircraft forces and moments.

Tratamos o problema do planejamento periódico de trajetórias: fixados uma trajetória periódica de um sistema afim sem arrasto em um grupo de Lie compacto e conexo G e uma condição inicial em G, encontrar outra trajetória do mesmo sistema satisfazendo a condição inicial dada e que rastreie assintoticamente a trajetória periódica dada. Resolvemos esse problema localmente (para condições iniciais em uma vizinhança de um ponto da trajetória periódica) quando G é semi-simples e o sistema afim é Lie-determinado (i.e. controlável), e apenas para um classe de trajetórias periódicas (as quais denominamos \"regulares\"). Apresentamos por fim um conjunto de condições suficientes para a existência de tais trajetórias. / We treat the periodic motion planning problem: given a periodic trajectory of a control-affine driftless system in a compact and connected Lie group G and an initial condition in G, find another trajectory of the same system satisfying the initial condition given and that asymptotically tracks the periodic trajectory. We solve this problem locally (for initial conditions in a neighborhood of some point of the periodic trajectory) when G is semisimple and the system is Lie-determined (i.e. controllable), and only for a class of periodic trajectories (which we call \"regular\"). Finally we present a set of sufficient conditions to ensure the existence of such trajectories.

Tratamos o problema do planejamento periódico de trajetórias: fixados uma trajetória periódica de um sistema afim sem arrasto em um grupo de Lie compacto e conexo G e uma condição inicial em G, encontrar outra trajetória do mesmo sistema satisfazendo a condição inicial dada e que rastreie assintoticamente a trajetória periódica dada. Resolvemos esse problema localmente (para condições iniciais em uma vizinhança de um ponto da trajetória periódica) quando G é semi-simples e o sistema afim é Lie-determinado (i.e. controlável), e apenas para um classe de trajetórias periódicas (as quais denominamos \"regulares\"). Apresentamos por fim um conjunto de condições suficientes para a existência de tais trajetórias. / We treat the periodic motion planning problem: given a periodic trajectory of a control-affine driftless system in a compact and connected Lie group G and an initial condition in G, find another trajectory of the same system satisfying the initial condition given and that asymptotically tracks the periodic trajectory. We solve this problem locally (for initial conditions in a neighborhood of some point of the periodic trajectory) when G is semisimple and the system is Lie-determined (i.e. controllable), and only for a class of periodic trajectories (which we call \"regular\"). Finally we present a set of sufficient conditions to ensure the existence of such trajectories.

This thesis is concerned with model predictive control (MPC) within the field of autonomous driving. MPC requires a model of the system to be controlled. Since a vehicle is expected to handle a wide range of driving conditions, it is crucial that the model of the vehicle dynamics is able to account for this. Differences in road grip caused by snowy, icy or muddy roads change the driving dynamics and relying on a single model, based on ideal conditions, could possibly lead to dangerous behaviour. This work investigates the use of Gaussian processes for learning a model that can account for varying road friction coefficients. This model is incorporated as an extension to a nominal vehicle model. A double lane change scenario is considered and the aim is to learn a GP model of the disturbance based on previous driving experiences with a road friction coefficient of 0.4 and 0.6 performed with a regular MPC controller. The data is then used to train a GP model. The GPMPC controller is then compared with the regular MPC controller in the case of trajectory tracking. The results show that the obtained GP models in most cases correctly predict the model error in one prediction step. For multi-step predictions, the results vary more with some cases showing an improved prediction with a GP model compared to the nominal model. In all cases, the GPMPC controller gives a better trajectory tracking than the MPC controller while using less control input.