Orientador: Furio Damiani / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-21T16:34:47Z (GMT). No. of bitstreams: 1 Dezani_Henrique_D.pdf: 4299449 bytes, checksum: 739b593ddd96b9124b1882d5510a0ce0 (MD5) Previous issue date: 2012 / Resumo: O controle do tráfego urbano constitui um importante tópico de pesquisa nos dias atuais. De fato, a cada dia aumenta a quantidade de veículos trafegando nas vias urbanas, as quais nem sempre são modificadas para a melhoria do tráfego. A existência de transportes públicos também não resolve o problema, visto que estes disputam o espaço nas vias com os demais veículos. Na literatura, foram encontrados diversos trabalhos que tentam resolver este problema reajustando o tempo semafórico das interseções ou, de maneira isolada, definindo rotas para um veículo. Neste projeto é apresentada uma nova maneira de abordar o problema, por meio do desenvolvimento de um sistema que otimiza o fluxo do tráfego urbano, em tempo real, ao explorar as possíveis rotas para cada veículo trafegando em uma determinada área urbana, utilizando análises em rede de Petri, e, então, a partir de um Algoritmo Genético, definir as rotas que diminuam o tempo e/ou tamanho das filas formadas nestas vias. Ainda neste trabalho, diversos testes são efetuados sobre o sistema desenvolvido, comprovando sua eficiência em relação às técnicas existentes / Abstract: Urban traffic control is an important research topic in the present days. In fact, everyday, the amount of vehicles traveling on urban roads increases. Moreover, such roads are not always modified for traffic improvement. Also, the availability of public transportation does not solve the problem, since they compete for space on the roads with other vehicles. Several papers have been found in the literature that attempt to solve this problem by either readjusting the traffic light time in intersections or isolately defining routes for a vehicle . This thesis presents a new way to approach the problem through the development of a system that optimizes the urban traffic stream, in real time, exploring possible routes for each vehicle traveling on a given urban area using Petri net analysis, and then, with a Genetic Algorithm, defines routes which reduce the time and/or size of the queues formed in such lanes. Also in this work, several tests are performed on the developed system, proving its efficiency with respect to existing techniques / Doutorado / Eletrônica, Microeletrônica e Optoeletrônica / Doutor em Engenharia Elétrica

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A single-layer neural network-based voltage compensation technique which generates minimum-distortion sinusoidal output voltages from a three-phase PWM inverter used for uninterruptible power supplies (UPS) is described. The proposed compensation technique is implemented in a microprocessor-based controller constructed in the stationary d-q frame where the controller sampling rate is twice the inverter switching frequency. The structure of a feed-forward artificial neural network connects network inputs and outputs through multiple linear or nonlinear neuron models, and processes these input/output data associations in a parallel distributed manner. Network inputs in the form of UPS load voltage commands and load current feedback are propagated forward in the network each controller sampling period generating the inverter output voltage commands, the network outputs, which are converted to three phase inverter switching Signals using the space vector PWM waveform generation process. / Ph. D.

Vehicular Ad hoc NETworks (VANETs) are a subclass of Mobile Ad hoc NETworks and represent a relatively new and very active field of research. VANETs will enable in the near future applications that will dramatically improve roadway safety and traffic efficiency. There is a need to increase traffic efficiency as the gap between the traveled and the physical lane miles keeps increasing. The Dynamic Traffic Assignment problem tries to dynamically distribute vehicles efficiently on the road network and in accordance with their origins and destinations. We present a novel dynamic decentralized and infrastructure-less algorithm to alleviate traffic congestions on road networks and to fill the void left by current algorithms which are either static, centralized, or require infrastructure. The algorithm follows an online approach that seeks stochastic user equilibrium and assigns traffic as it evolves in real time, without prior knowledge of the traffic demand or the schedule of the cars that will enter the road network in the future. The Reverse Online Algorithm for the Dynamic Traffic Assignment inspired by Ant Colony Optimization for VANETs follows a metaheuristic approach that uses reports from other vehicles to update the vehicle’s perceived view of the road network and change route if necessary. To alleviate the broadcast storm spontaneous clusters are created around traffic incidents and a threshold system based on the level of congestion is used to limit the number of incidents to be reported. Simulation results for the algorithm show a great improvement on travel time over routing based on shortest distance. As the VANET transceivers have a limited range, that would limit messages to reach at most 1,000 meters, we present a modified version of this algorithm that uses a rebroadcasting scheme. This rebroadcasting scheme has been successfully tested on roadways with segments of up to 4,000 meters. This is accomplished for the case of traffic flowing in a single direction on the roads. It is anticipated that future simulations will show further improvement when traffic in the other direction is introduced and vehicles travelling in that direction are allowed to use a store carry and forward mechanism. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Vehicular Ad Hoc Network (VANET) supports wireless communication among vehicles using vehicle-to-vehicle (V2V) communication and between vehicles and infrastructure using vehicle-to-infrastructure (V2I) communication. This communication can be utilized to allow the distribution of safety and non-safety messages in the network. VANET supports a wide range of applications which rely on the messages exchanged within the network. Such applications will enhance the drivers' consciousness and improve their driving experience. However, the efficiency of these applications depends on the availability of vehicles real-time location information. A number of methods have been proposed to fulfill this requirement. However, designing a V2V-based localization method is challenged by the high mobility and dynamic topology of VANET and the interference noise due to objects and buildings. Currently, vehicle localization is based on GPS technology, which is not always reliable. Therefore, utilizing V2V communication in VANET can enhance the GPS positioning. With V2V-based localization, vehicles can determine their locations by exchanging mobility data among neighboring vehicles. In this research work, we address the above challenges and design a realistic V2V-based localization method that extends the centroid localization (CL) by assigning a weight value to each neighboring vehicle. This weight value is obtained using a weighting function that utilizes the following factors: 1) link quality distance between the neighboring vehicles 2) heading information and 3) map information. We also use fuzzy logic to model neighboring vehicles' weight values. Due to the sensitivity and importance of the exchanged information, it is very critical to ensure its integrity and reliability. Therefore, in this work, we present the design and the integration of a mobility data verification component into the proposed localization method, so that only verified data from trusted neighboring vehicles are considered. We also use subjective logic to design a trust management system to evaluate the trustworthiness of neighboring vehicles based on the formulated subjective opinions. Extensive experimental work is conducted using simulation programs to evaluate the performance of the proposed methods. The results show improvement on the location accuracy for varying vehicle densities and transmission ranges as well as in the presence of malicious/untrusted neighboring vehicles. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

A methodology to estimate the state of a moving marine vehicle, defined by its position, velocity and heading, from an unmanned surface vehicle (USV), also in motion, using a stereo vision-based system, is presented in this work, in support of following a target vehicle using an USV. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

This Open Platform for Limit Protection guides the open design of maneuver limit protection systems in general, and manned, rotorcraft, aerospace applications in particular. The platform uses three stages of limit protection modules: limit cue creation, limit cue arbitration, and control system interface. A common set of limit cue modules provides commands that can include constraints, alerts, transfer functions, and friction. An arbitration module selects the best limit protection cues and distributes them to the most appropriate control path interface. This platform adopts a holistic approach to limit protection whereby it considers all potential interface points, including the pilots visual, aural, and tactile displays; and automatic command restraint shaping for autonomous limit protection. For each functional module, this thesis guides the control system designer through the design choices and information interfaces among the modules. Limit cue module design choices include type of prediction, prediction mechanism, method of critical control calculation, and type of limit cue. Special consideration is given to the nature of the limit, particularly the level of knowledge about it, and the ramifications for limit protection design, especially with respect to intelligent control methods such as fuzzy inference systems and neural networks. The Open Platform for Limit Protection reduces the effort required for initial limit protection design by defining a practical structure that still allows considerable design freedom. The platform reduces lifecycle effort through its open engineering systems approach of decoupled, modular design and standardized information interfaces. Using the Open Platform for Limit Protection, a carefree maneuver system is designed that addresses: main rotor blade stall as a steady-state limit; hub moment as a transient structural limit; and pilot induced oscillation as a controllability limit. The limit cue modules in this system make use of static neural networks, adaptive neural networks, and fuzzy inference systems to predict these limits. Visual (heads up display) and tactile (force-feedback) limit cues are employed. The carefree maneuver system is demonstrated in manned simulation using a General Helicopter (GENHEL) math model of the UH-60 Black Hawk, a projected, 53 degree field of view for the pilot, and a two-axis, active sidestick for cyclic control.

Haptic systems incorporate many different components, ranging from virtual simulations, physical robotic interfaces (super joysticks), robotic slaves, signal communication, and digital control; two-port networks offer compact and modular organization of such haptic components. By establishing specific stability properties of the individual component networks, their control parameters can be tuned independently of external components or interfacing environment. This allows the development of independent haptic two-port networks for interfacing with a class of haptic components. Furthermore, by using the two-port network with virtual coupling paradigm to analyze linear haptic systems, the complete duality between an admittance controlled device with velocity (position) feedback and virtual coupling can be compared to an impedance controlled device with force feedback and virtual coupling. This research first provides background on linear haptic two-port networks and use of Llewelyn's Stability Criterion to prove their stability when interfaced with passive environments, with specific comments regarding application of these linear techniques to nonlinear systems. Furthermore, man-machine interaction dynamics are addressed, with specific attention given to the human is a passive element assumption and how to include estimated human impedance / admittance dynamic limits into the two--port design. Two--port numerical tuning algorithms and analysis techniques are presented and lay the groundwork for testing of said haptic networks on HuRBiRT (Human Robotic Bilateral Research Tool), a large scale nonlinear hybrid active / passive haptic display. First, two-port networks are numerically tuned using a linearized dynamic model of HuRBiRT. Resulting admittance and impedance limits of the respective networks are compared to add insight on the advantages / disadvantages of the two different implementations of haptic causality for the same device, with specific consideration given to the advantage of adding force feedback to the impedance network, selection of virtual coupling form, effects of varying system parameters (such as physical or EMF damping, filters, etc.), and effects of adding human dynamic limits into the network formulation. Impedance and admittance two-port network implementations are experimentally validated on HuRBiRT, adding further practical insight into network formulation. Resulting experimental networks are directly compared to those numerically formulated through use of HuRBiRT's linearized dynamic models.

Wireless sensor and actor networks are used in situations where interaction is required between a network and the environment in which the network is deployed. This research studies the functioning of a single mobile actor deployed in a sparsely connected network. When deployed in a sparsely connected network, an actor has to do more than acting. It has to perform the additional duties of an event collector - collecting events from the naturally occurring clusters - so that it can fulfill its primary obligation as an actor. The path taken by a mobile actor node is generated by a mobility model. The existing random mobility models are non-intelligent mobility models. While they may bring about a chance meeting between an actor and an event, there is no guarantee that these meetings will actually happen. This motivates the development of intelligent mobility models for the actor node, which will generate paths that are reflective of the network in which the actor is deployed. In this thesis, intelligent mobility models for the actor node were developed using the inherent clustering information of a sparsely connected network. These models were applied to an actor node in networks of varying sparseness and the following conclusions were reached: (i) Existing random mobility models are unsuitable for an actor in a sparsely connected network. (ii) High probability of events can be sensed when a sparsely connected network is used. (iii) 100% event detection by the actor node is possible at higher speeds. (iv) When the single actor functioned both as an event collector and as an actor, the number of events acted upon by the actor was very close to the number of events acted upon by an actor in a fully connected network. (v) The Correlation Theory developed in this research suggests using a combination of the intelligent mobility models to obtain the best performance results under all circumstances. (vi) Early detection of events can be supported where it is required. All of the above conclusions justify the deployment of a single actor and a sparsely connected network, either individually or as a combination.

We present automatic tools for configuring and deploying multi-robot networks of decentralized, mobile robots. These methods are tailored to the decentralized nature of the multi-robot network and the limited information available to each robot. We present methods for determining if user-defined network tasks are feasible or infeasible for the network, considering the limited range of its sensors. To this end, we define rigid and persistent feasibility and present necessary and sufficient conditions (along with corresponding algorithms) for determining the feasibility of arbitrary, user-defined deployments. Control laws for moving multi-robot networks in acyclic, persistent formations are defined. We also present novel Embedded Graph Grammar Systems (EGGs) for coordinating and deploying the network. These methods exploit graph representations of the network, as well as graph-based rules that dictate how robots coordinate their control. Automatic systems are defined that allow the robots to assemble arbitrary, user-defined formations without any reliance on localization. Further, this system is augmented to deploy these formations at the user-defined, global location in the environment, despite limited localization of the network. The culmination of this research is an intuitive software program with a Graphical User Interface (GUI) and a satellite image map which allows users to enter the desired locations of sensors. The automatic tools presented here automatically configure an actual multi-robot network to deploy and execute user-defined network tasks.