Dissertations / Theses: 'Autonomous network control'

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Dutta, Rajdeep. "Cooperative control of autonomous network topologies." Thesis, The University of Texas at San Antonio, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10151348.

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In this dissertation, we present novel solutions to cooperative control of autonomous multi-agent network topologies pertaining to the area of hostile target tracking by multiple unmanned aerial vehicles (UAVs). The present work assumes an undirected graph comprising point-mass UAVs with time-varying communication topology among agents. The level of information sharing ability among agents in a multi-agent network, i.e. the network connectivity, plays pivotal role in group dynamics. A neighborhood information based decentralized controller is proposed in order to drive UAVs into a symmetric formation of polygon shape surrounding a mobile target, simultaneously with maintaining and controlling connectivity during the formation process. Appropriate controller parameter selection schemes, both for controller weights and gains, are adapted for dynamic topologies to maintain the connectivity measure above zero at all times. A challenging task of tracking a desired connectivity profile along with the formation control, is accomplished by using time-varying controller gains throughout agents dynamics. We next present a generalized formation controller, which in fact generates a family of UAV trajectories satisfying the control criteria. The proposed decentralized controller contains additional tuning parameters as fractional powers on proportional and derivative terms, rendering flexibility in achieving the control objective. The proposed controller with proper fractional powers, results in gradual state changes in UAV dynamics by using limited control inputs. Moreover, we extend our work by addressing a ground target tracking and reacquiring problem using the visual information gathered by flying UAV. The proposed guidance law uses line-of-sight guidance to track the target pushing it towards the image center captured by UAV, and exploits UAV-target mutual information to reacquire the target in case it steers away from the field-of-view for a short time. The convergence of the closed loop systems under the proposed controllers are shown using Lyapunov theory. Simulation results validate the effectiveness and novelty of the proposed control laws.

In addition to the above, this work focuses on categorizing multi-agent topologies in concern with the network dynamics and connectivity to analyze, realize, and visualize multi-agent interactions. In order to explore various useful agents reconfiguration possibilities without compromising the network connectivity, the present work aims at determining distinct topologies with the same connectivity or isoconnected topologies. Different topologies with identical connectivity are found out with the help of analytic techniques utilizing matrix algebra and calculus of variation. Elegant strategies for preserving connectivity in a network with a single mobile agent and rest of the stationary members, are proposed in this work as well. The proposed solutions are validated with the help of sufficient examples. For visual understanding of how agents locations and topology configurations influence the network connectivity, a MATLAB based graphical user interface is designed to interact with multi-agent graphs in a user-friendly manner.

To this end, the present work succeeds to determine solutions to challenging multi-UAV cooperative control problems, such as: (1) Symmetric formation control surrounding a mobile target; (2) Maintaining, improving and controlling the network connectivity during a mission; and (3) Categorizing different multi-agent topologies to unravel useful reconfiguration options for a group. The proposed theories with appropriate analysis, and the simulation results suffice to show the contribution and novelty of this work.

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Tung, Charles P. (Charles Patrick) 1974. "A distributed processing network for autonomous micro-rover control." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/47542.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1998.
Includes bibliographical references (leaf 77).
by Charles P. Tung.
B.S.
M.Eng.

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Hemlin, Karl, and Frida Persson. "Remote Control Operation of Autonomous Cars Over Cellular Network Using PlayStation Controller." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254218.

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A big challenge regarding the development of autonomous vehicles is how to handle complex situations. If an autonomous vehicle ends up in a situation where it cannot make a decision on its own it will cause the car to stop, unable to continue driving. For these situations, human intervention is required. By making it possible to control the car remotely there is no need for an actual human in the car. Instead, a human operator can remotely control one or several cars from a distance. The purpose of this project was to identify such complex situations, evaluate remote control options and implement one of these controllers to drive the SVEA cars in the Smart Mobility Lab. After evaluation of possible remote control options, the PlayStation controller was chosen to be the simplest and most intuitive steering option. The controller was successfully implemented first in simulation and then on the SVEA cars in the Smart Mobility Lab. A test track was designed to measure the performance of the implemented controller and to be able to measure user-friendliness through a survey. It was concluded that a majority of the participants would not feel comfortable steering a real car using the PlayStation controller. However, a more extensive evaluation would be required to draw any major conclusions.

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Garratt, Matthew Adam, and m. garratt@adfa edu au. "Biologically Inspired Vision and Control for an Autonomous Flying Vehicle." The Australian National University. Research School of Biological Sciences, 2008. http://thesis.anu.edu.au./public/adt-ANU20090116.154822.

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This thesis makes a number of new contributions to control and sensing for unmanned vehicles. I begin by developing a non-linear simulation of a small unmanned helicopter and then proceed to develop new algorithms for control and sensing using the simulation. The work is field-tested in successful flight trials of biologically inspired vision and neural network control for an unstable rotorcraft. The techniques are more robust and more easily implemented on a small flying vehicle than previously attempted methods.¶ Experiments from biology suggest that the sensing of image motion or optic flow in insects provides a means of determining the range to obstacles and terrain. This biologically inspired approach is applied to control of height in a helicopter, leading to the Worlds first optic flow based terrain following controller for an unmanned helicopter in forward flight. Another novel optic flow based controller is developed for the control of velocity in hover. Using the measurements of height from other sensors, optic flow is used to provide a measure of the helicopters lateral and longitudinal velocities relative to the ground plane. Feedback of these velocity measurements enables automated hover with a drift of only a few cm per second, which is sufficient to allow a helicopter to land autonomously in gusty conditions with no absolute measurement of position.¶ New techniques for sensor fusion using Extended Kalman Filtering are developed to estimate attitude and velocity from noisy inertial sensors and optic flow measurements. However, such control and sensor fusion techniques can be computationally intensive, rendering them difficult or impossible to implement on a small unmanned vehicle due to limitations on computing resources. Since neural networks can perform these functions with minimal computing hardware, a new technique of control using neural networks is presented. First a hybrid plant model consisting of exactly known dynamics is combined with a black-box representation of the unknown dynamics. Simulated trajectories are then calculated for the plant using an optimal controller. Finally, a neural network is trained to mimic the optimal controller. Flight test results of control of the heave dynamics of a helicopter confirm the neural network controllers ability to operate in high disturbance conditions and suggest that the neural network outperforms a PD controller. Sensor fusion and control of the lateral and longitudinal dynamics of the helicopter are also shown to be easily achieved using computationally modest neural networks.

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Dalamagkidis, Konstantinos. "Autonomous vertical autorotation for unmanned helicopters." [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0003147.

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Youmans, Elisabeth A. "Neural network control of space vehicle orbit transfer, intercept, and rendezvous maneuvers." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-06062008-162101/.

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Darr, Matthew John. "DEVELOPMENT AND EVALUATION OF A CONTROLLER AREA NETWORK BASED AUTONOMOUS VEHICLE." UKnowledge, 2004. http://uknowledge.uky.edu/gradschool_theses/192.

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Through the work of researchers and the development of commercially availableproducts, automated guidance has become a viable option for agricultural producers.Some of the limitations of commercially available technologies are that they onlyautomate one function of the agricultural vehicle and that the systems are proprietary toa single machine model.The objective of this project was to evaluate a controller area network (CAN bus)as the basis of an automated guidance system. The prototype system utilized severalmicrocontroller-driven nodes to act as control points along a system wide CAN bus.Messages were transferred to the steering, transmission, and hitch control nodes from atask computer. The task computer utilized global positioning system data to determinethe appropriate control commands.Infield testing demonstrated that each of the control nodes could be controlledsimultaneously over the CAN bus. Results showed that the task computer adequatelyapplied a feedback control model to the system and achieved guidance accuracy levelswell within the range sought. Testing also demonstrated the system's ability tocomplete normal field operations such as headland turning and implement control.

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Puttige, Vishwas Ramadas Engineering &amp Information Technology Australian Defence Force Academy UNSW. "Neural network based adaptive control for autonomous flight of fixed wing unmanned aerial vehicles." Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/43736.

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This thesis presents the development of small, inexpensive unmanned aerial vehicles (UAVs) to achieve autonomous fight. Fixed wing hobby model planes are modified and instrumented to form experimental platforms. Different sensors employed to collect the flight data are discussed along with their calibrations. The time constant and delay for the servo-actuators for the platform are estimated. Two different data collection and processing units based on micro-controller and PC104 architectures are developed and discussed. These units are also used to program the identification and control algorithms. Flight control of fixed wing UAVs is a challenging task due to the coupled, time-varying, nonlinear dynamic behaviour. One of the possible alternatives for the flight control system is to use the intelligent adaptive control techniques that provide online learning capability to cope with varying dynamics and disturbances. Neural network based indirect adaptive control strategy is applied for the current work. The two main components of the adaptive control technique are the identification block and the control block. Identification provides a mathematical model for the controller to adapt to varying dynamics. Neural network based identification provides a black-box identification technique wherein a suitable network provides prediction capability based upon the past inputs and outputs. Auto-regressive neural networks are employed for this to ensure good retention capabilities for the model that uses the past outputs and inputs along with the present inputs. Online and offline identification of UAV platforms are discussed based upon the flight data. Suitable modifications to the Levenberg-Marquardt training algorithm for online training are proposed. The effect of varying the different network parameters on the performance of the network are numerically tested out. A new performance index is proposed that is shown to improve the accuracy of prediction and also reduces the training time for these networks. The identification algorithms are validated both numerically and flight tested. A hardware-in-loop simulation system has been developed to test the identification and control algorithms before flight testing to identify the problems in real time implementation on the UAVs. This is developed to keep the validation process simple and a graphical user interface is provided to visualise the UAV flight during simulations. A dual neural network controller is proposed as the adaptive controller based upon the identification models. This has two neural networks collated together. One of the neural networks is trained online to adapt to changes in the dynamics. Two feedback loops are provided as part of the overall structure that is seen to improve the accuracy. Proofs for stability analysis in the form of convergence of the identifier and controller networks based on Lyapunov's technique are presented. In this analysis suitable bounds on the rate of learning for the networks are imposed. Numerical results are presented to validate the adaptive controller for single-input single-output as well as multi-input multi-output subsystems of the UAV. Real time validation results and various flight test results confirm the feasibility of the proposed adaptive technique as a reliable tool to achieve autonomous flight. The comparison of the proposed technique with a baseline gain scheduled controller both in numerical simulations as well as test flights bring out the salient adaptive feature of the proposed technique to the time-varying, nonlinear dynamics of the UAV platforms under different flying conditions.

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Livianu, Mathew Joseph. "Human-in-the-loop neural network control of a planetary rover on harsh terrain." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26576.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Dr. Ayanna Howard; Committee Member: Dr. Patricio Vela; Committee Member: Dr. Yoria Wardi. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Kam, Khim Yee. "High bandwidth communications links between heterogeneous autonomous vehicles using sensor network modeling and extremum control approaches." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://edocs.nps.edu/npspubs/scholarly/theses/2008/Dec/08Dec%5FKam.pdf.

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Thesis (M.S. in Engineering Science (Mechanical Engineering))--Naval Postgraduate School, December 2008.
Thesis Advisor(s): Kaminer, Isaac I. ; Lee, Deok Jin. "December 2008." Description based on title screen as viewed on January 29, 2009. Includes bibliographical references (p. 57-58). Also available in print.

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de, Freitas Edison Pignaton, Tales Heimfarth, Armando Morado Ferreira, Flávio Rech Wagner, Carlos Eduardo Pereira, and Tony Larsson. "An agent framework to support sensor networks’ setup and adaptation." Högskolan i Halmstad, Inbyggda system (CERES), 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14691.

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Sensor networks are being used in several emerging applications not even imagined some years ago due to advances in sensing, computing, and communication techniques. However, these advances also pose various challenges that must be faced. One important challenge is related to the autonomous capability needed to setup and adapt the networks, which decentralizes the control of the network, saving communication and energy resources. Middleware technology helps in addressing this kind of problem, but there is still a need for additional solutions, particularly considering dynamic changes in users' requirements and operation conditions. This paper presents an agent-based framework acting as an integral part of a middleware to support autonomous setup and adaptation of sensor networks. It adds interoperability among heterogeneous nodes in the network, by means of autonomous behavior and reasoning. These features also address the needs for system setup and adaptations in the network, reducing the communication overhead and decentralizing the decision making mechanism. Additionally, preliminary results are also presented.

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Cheng, Yongqiang. "Wireless mosaic eyes based robot path planning and control : autonomous robot navigation using environment intelligence with distributed vision sensors." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4421.

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As an attempt to steer away from developing an autonomous robot with complex centralised intelligence, this thesis proposes an intelligent environment infrastructure where intelligences are distributed in the environment through collaborative vision sensors mounted in a physical architecture, forming a wireless sensor network, to enable the navigation of unintelligent robots within that physical architecture. The aim is to avoid the bottleneck of centralised robot intelligence that hinders the application and exploitation of autonomous robot. A bio-mimetic snake algorithm is proposed to coordinate the distributed vision sensors for the generation of a collision free Reference-snake (R-snake) path during the path planning process. By following the R-snake path, a novel Accompanied snake (A-snake) method that complies with the robot's nonholonomic constraints for trajectory generation and motion control is introduced to generate real time robot motion commands to navigate the robot from its current position to the target position. A rolling window optimisation mechanism subject to control input saturation constraints is carried out for time-optimal control along the A-snake. A comprehensive simulation software and a practical distributed intelligent environment with vision sensors mounted on a building ceiling are developed. All the algorithms proposed in this thesis are first verified by the simulation and then implemented in the practical intelligent environment. A model car with less on-board intelligence is successfully controlled by the distributed vision sensors and demonstrated superior mobility.

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Krishnakumar, Sita Srinivasaraghavan. "Intelligent actor mobility in wireless sensor and actor networks." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24735.

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Thesis (Ph.D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008.
Committee Chair: Abler, Randal T.; Committee Member: Copeland, John A.; Committee Member: Haas, Kevin; Committee Member: Moore II, Elliot; Committee Member: Owen III, Henry L.

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Iyengar, Navneet. "Providing QoS in Autonomous and Neighbor-aware multi-hop Wireless Body Area Networks." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439306913.

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Rojas, Castro Dalia Marcela. "The RHIZOME architecture : a hybrid neurobehavioral control architecture for autonomous vision-based indoor robot navigation." Thesis, La Rochelle, 2017. http://www.theses.fr/2017LAROS001/document.

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Les travaux décrits dans cette thèse apportent une contribution au problème de la navigation autonome de robots mobiles dans un contexte de vision indoor. Il s’agit de chercher à concilier les avantages des différents paradigmes d’architecture de contrôle et des stratégies de navigation. Ainsi, nous proposons l’architecture RHIZOME (Robotic Hybrid Indoor-Zone Operational ModulE) : une architecture unique de contrôle robotique mettant en synergie ces différentes approches en s’appuyant sur un système neuronal. Les interactions du robot avec son environnement ainsi que les multiples connexions neuronales permettent à l’ensemble du système de s’adapter aux conditions de navigation. L’architecture RHIZOME proposée combine les avantages des approches comportementales (e.g. rapidité de réaction face à des problèmes imprévus dans un contexte d’environnement dynamique), et ceux des approches délibératives qui tirent profit d’une connaissance a priori de l’environnement. Cependant, cette connaissance est uniquement exploitée pour corroborer les informations perçues visuellement avec celles embarquées. Elle est représentée par une séquence de symboles artificiels de navigation guidant le robot vers sa destination finale. Cette séquence est présentée au robot soit sous la forme d’une liste de paramètres, soit sous la forme d’un plan. Dans ce dernier cas, le robot doit extraire lui-même la séquence de symboles à suivre grâce à une chaine de traitements d’images. Ainsi, afin de prendre la bonne décision lors de sa navigation, le robot traite l’ensemble de l’information perçue, la compare en temps réel avec l’information a priori apportée ou extraite, et réagit en conséquence. Lorsque certains symboles de navigation ne sont plus présents dans l’environnement de navigation, l’architecture RHIZOME construit de nouveaux lieux de référence à partir des panoramas extraits de ces lieux. Ainsi, le robot, lors de phases exploratoires, peut s’appuyer sur ces nouvelles informations pour atteindre sa destination finale, et surmonter des situations imprévues. Nous avons mis en place notre architecture sur le robot humanoïde NAO. Les résultats expérimentaux obtenus lors d’une navigation indoor, dans des scenarios à la fois déterministes et stochastiques, montrent la faisabilité et la robustesse de cette approche unifiée
The work described in this dissertation is a contribution to the problem of autonomous indoor vision-based mobile robot navigation, which is still a vast ongoing research topic. It addresses it by trying to conciliate all differences found among the state-of-the-art control architecture paradigms and navigation strategies. Hence, the author proposes the RHIZOME architecture (Robotic Hybrid Indoor-Zone Operational ModulE) : a unique robotic control architecture capable of creating a synergy of different approaches by merging them into a neural system. The interactions of the robot with its environment and the multiple neural connections allow the whole system to adapt to navigation conditions. The RHIZOME architecture preserves all the advantages of behavior-based architectures such as rapid responses to unforeseen problems in dynamic environments while combining it with the a priori knowledge of the world used indeliberative architectures. However, this knowledge is used to only corroborate the dynamic visual perception information and embedded knowledge, instead of directly controlling the actions of the robot as most hybrid architectures do. The information is represented by a sequence of artificial navigation signs leading to the final destination that are expected to be found in the navigation path. Such sequence is provided to the robot either by means of a program command or by enabling it to extract itself the sequence from a floor plan. This latter implies the execution of a floor plan analysis process. Consequently, in order to take the right decision during navigation, the robot processes both set of information, compares them in real time and reacts accordingly. When navigation signs are not present in the navigation environment as expected, the RHIZOME architecture builds new reference places from landmark constellations, which are extracted from these places and learns them. Thus, during navigation, the robot can use this new information to achieve its final destination by overcoming unforeseen situations.The overall architecture has been implemented on the NAO humanoid robot. Real-time experimental results during indoor navigation under both, deterministic and stochastic scenarios show the feasibility and robustness of the proposed unified approach

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Silva, Joelson Coelho da. "Uma proposta de controle neural adaptativo para a navegação de veículos autônomos." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1999. http://hdl.handle.net/10183/18631.

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Os equipamentos robóticos foram inicialmente criados para atuarem em ambientes industriais fechados. Com o passar do tempo, melhorias foram conquistadas. Atualmente, não se limitam mais à realização de tarefas simples e repetitivas em locais especialmente preparados. Novos equipamentos, capazes de atuarem em ambientes abertos e de realizarem as mais diversas atividades, estão sendo desenvolvidos. Para tanto, é necessário que seus sistemas de controle realizem uma efetiva interação com o mundo onde estão inseridos. Fazem-se necessários, portanto, novos sistemas controladores com capacidade de uma contínua adaptação ao ambiente dinâmico onde operam. As redes neurais artificiais, devido a sua capacidade de tratamento de problemas não lineares – matematicamente difíceis de serem resolvidos, estão sendo empregadas no controle destes processos. O gerenciamento da trajetória de um veículo móvel em ambientes abertos ou fechados é um procedimento altamente não-linear, logo, a aplicação das redes neurais artificiais é bastante promissora. Apesar de sua grande versatilidade, as redes neurais artificiais têm sido utilizadas apenas como sistemas de mapeamento. A grande maioria delas necessita de uma fase de treinamento para que possam armazenar a diversidade de estados possíveis do sistema. Quando atuam, elas simplesmente mapeiam os seus valores de entrada (estado atual) nas soluções previamente armazenadas. Contudo, esta não é a melhor abordagem para os sistemas abertos, ou seja, para os processos cujas situações e possibilidades não podem ser totalmente enumeradas e que podem ser mutáveis no decorrer do tempo. Este trabalho apresenta uma metodologia de controle neural adaptativo para guiar um veículo móvel até o seu destino em ambientes contendo obstáculos fixos ou móveis. Diferentemente das abordagens tradicionais, não existe a necessidade de um treinamento prévio da rede. A rede neural artificial escolhida promove uma contínua adaptação do sistema enquanto atua. Neste processo, são utilizados sensores que fornecem subsídios para que a rede possa gerar, adaptativamente, soluções parciais que façam com que o veículo autônomo se aproxime cada vez mais do seu objetivo, até, finalmente, atingi-lo.
The robotic equipments were created initially to actuate in closed industrial environments. Improvements have been acquieved in this area. Nowadays, they are no longer limited to perform simple and repetitive tasks in controlled places. New equipments, capable of acting in open environments and doing the most several activities, are being developed. For so much, it is necessary that its control systems accomplish an effective interaction with the world where they are inserted. Therefore, new systems controllers with capacity of a continuous adaptation to the dynamic environments are essential. Artificial neural networks, due to their capacity of dealing wit non-linear problems – mathematically difficult to be solved – are being used to control these kind of processes. Guide a mobile vehicle through an open or controlled environments is a highly non-linear procedure; therefore, the use of an artificial neural nets is quite promising. In spite of its great versatility, they have just been used as mapping systems. Most of them need a training phase so that they can store the diversity of system’s possible states. When they actuate, they simply map their input values (current state) to the solutions previously stored. However, this is not the best approach for open systems, i.e. systems whose situations and possibilities cannot be totally enumerated and that can change in time. This work presents an adaptive neural control methodology to guide a mobile vehicle to its target in environments with fixed or mobile obstacles. Differently from the traditional approaches, the need of a previous training phase of the neural network doesn't exist. The chosen model of artificial neural net promotes a continuous adaptation of the system while it actuates. Sensors are used to provide informations to the net. This way it generates partial solutions that makes the autonomous vehicle gets closer of its goal, until, finally, reach it.

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Svensson, August. "Range-based Wireless Sensor Network Localization for Planetary Rovers." Thesis, Luleå tekniska universitet, Rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83213.

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Obstacles faced in planetary surface exploration require innovation in many areas, primarily that of robotics. To be able to study interesting areas that are by current means hard to reach, such as steep slopes, ravines, caves andlava tubes, the surface vehicles of today need to be modified or augmented. Oneaugmentation with such a goal is PHALANX (Projectile Hordes for AdvancedLong-term and Networked eXploration), a prototype system being developed atthe NASA Ames Research Center. PHALANX uses remote deployment of expendablesensor nodes from a lander or rover vehicle. This enables in-situ measurementsin hard-to-reach areas with reduced risk to the rover. The deployed sensornodes are equipped with capabilities to transmit data wirelessly back to therover and to form a network with the rover and other nodes. Knowledge of the location of deployed sensor nodes and the momentary locationof the rover is greatly desired. PHALANX can be of aid in this aspect as well.With the addition of inter-node and rover-to-node range measurements, arange-based network SLAM (Simultaneous Localization and Mapping) system can beimplemented for the rover to use while it is driving within the network. Theresulting SLAM system in PHALANX shares characteristics with others in the SLAM literature, but with some additions that make it unique. One crucial additionis that the rover itself deploys the nodes. Another is the ability for therover to more accurately localize deployed nodes by external sensing, such asby utilizing the rover cameras. In this thesis, the SLAM of PHALANX is studied by means of computer simulation.The simulation software is created using real mission values and valuesresulting from testing of the PHALANX prototype hardware. An overview of issuesthat a SLAM solution has to face as present in the literature is given in thecontext of the PHALANX SLAM system, such as poor connectivity, and highlycollinear placements of nodes. The system performance and sensitivities arethen investigated for the described issues, using predicted typical PHALANXapplication scenarios. The results are presented as errors in estimated positions of the sensor nodesand in the estimated position of the rover. I find that there are relativesensitivities to the investigated parameters, but that in general SLAM inPHALANX is fairly insensitive. This gives mission planners and operatorsgreater flexibility to prioritize other aspects important to the mission athand. The simulation software developed in this thesis work also has thepotential to be expanded on as a tool for mission planners to prepare forspecific mission scenarios using PHALANX.

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Dvořáček, Jiří. "Autonomní a dispečerské řízení distribuovaných zdrojů v distribuční síti VN." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442469.

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Theses focuses on the evaluation of the possible means of active and reactive power control of generating units connected to medium voltage. First part summarizes analysis of Czech Republic medium voltage distribution grid. It summarizes means of autonomous and dispatcher control of generating units with respect to European Comission directive RfG, standard ČSN EN 50549-2 and national implementation PPDS. Following parts provide description of generating unit and distribution grid models used for simulation in PSCAD. Last part focuses on evaluation of results obtained via simulating steady-state scenarios as well as continuous simulation.

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Hallqvist, Erik, and Sebastian Håkansson. "Networked control of autonomous ground vehicles." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199347.

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Ehrlin, E., and M. Törnqvist. "Networked control of autonomous ground vehicles." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199313.

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Athari, Kayvan. "Networked control of autonomous ground vehicles." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199389.

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Bakutis, Vladas, and Qiao Jin. "Networked Control of Autonomous Ground Vehicles." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200572.

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Unnikrishnan, Suraj. "Adaptive Envelope Protection Methods for Aircraft." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11478.

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Carefree handling refers to the ability of a pilot to operate an aircraft without the need to continuously monitor aircraft operating limits. At the heart of all carefree handling or maneuvering systems, also referred to as envelope protection systems, are algorithms and methods for predicting future limit violations. Recently, envelope protection methods that have gained more acceptance, translate limit proximity information to its equivalent in the control channel. Envelope protection algorithms either use very small prediction horizon or are static methods with no capability to adapt to changes in system configurations. Adaptive approaches maximizing prediction horizon such as dynamic trim, are only applicable to steady-state-response critical limit parameters. In this thesis, a new adaptive envelope protection method is developed that is applicable to steady-state and transient response critical limit parameters. The approach is based upon devising the most aggressive optimal control profile to the limit boundary and using it to compute control limits. Pilot-in-the-loop evaluations of the proposed approach are conducted at the Georgia Tech Carefree Maneuver lab for transient longitudinal hub moment limit protection. Carefree maneuvering is the dual of carefree handling in the realm of autonomous Uninhabited Aerial Vehicles (UAVs). Designing a flight control system to fully and effectively utilize the operational flight envelope is very difficult. With the increasing role and demands for extreme maneuverability there is a need for developing envelope protection methods for autonomous UAVs. In this thesis, a full-authority automatic envelope protection method is proposed for limit protection in UAVs. The approach uses adaptive estimate of limit parameter dynamics and finite-time horizon predictions to detect impending limit boundary violations. Limit violations are prevented by treating the limit boundary as an obstacle and by correcting nominal control/command inputs to track a limit parameter safe-response profile near the limit boundary. The method is evaluated using software-in-the-loop and flight evaluations on the Georgia Tech unmanned rotorcraft platform- GTMax. The thesis also develops and evaluates an extension for calculating control margins based on restricting limit parameter response aggressiveness near the limit boundary.

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Rush, Jonathan Reginald. "Evolving cellular neural networks for autonomous robot control." Thesis, University of Salford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308293.

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Orihuela, Swartling Johanna, and Magnus Pontusson. "Cooperative networked control of autonomous ground vehicles." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199264.

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Lycke, Jens, and Fredrik Svensson. "Cooperative networked control of autonomous ground vehicles." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199265.

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Liu, Bing. "Contrôle et optimisation des systèmes de transport intelligents dans le voisinage des intersections." Thesis, Ecole centrale de Lille, 2016. http://www.theses.fr/2016ECLI0008/document.

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Cette thèse est consacrée à étudier les applications potentielles de véhicules autonomes et communications V2X pour construire les systèmes de transport intelligents. Premièrement, le comportement de caravane dans un environnement de véhicule connecté est étudié. Un algorithme de commande de caravane est conçu pour obtenir l'espacement sécuritaire ainsi que la conformité de la vitesse et de l'accélération. Deuxièmement, à plus grande échelle, les caravanes autour d'une intersection sont considérées. Le débit pendant une période de signal de trafic peut être amélioré en tirant profit de la capacité redondante de la route. Dans diverses contraintes, les véhicules peuvent choisir d'accélérer et rejoindre la caravane précédente ou à décélérer de déroger à l'actuel. Troisièmement, une intersection sans signalisation en VANET est considérée. Dans des conditions de faible trafic, les véhicules peuvent réguler leur vitesse avant d'arriver à l'intersection en fonction du temps d'occupation de la zone de conflit (TOZC) stocké au niveau du gestionnaire, afin qu'ils puissent traverser l'intersection sans collision ni arrêt. Le délai peut être réduit en conséquence. Enfin, un algorithme de gestion d'intersection autonome universelle, qui peut fonctionner même avec le trafic lourd, est développé. Le véhicule cherche à sécuriser les fenêtres entrant dans le TOZC. Ensuite, sur la base des fenêtres trouvées et le mouvement du véhicule qui précède, les trajectoires des véhicules peuvent être planifiées en utilisant une méthode de programmation dynamique segmentée. Tous les algorithmes conçus sont testés et vérifiés avec succès par des simulations dans scénarios différents
This thesis is devoted to study the potential applications of autonomous vehicles and V2X communications to construct the intelligent transportation systems. Firstly, the behavior of platoon in connected vehicle environment is studied. A platoon control algorithm is designed to obtain safe spacing as well as accordance of velocity and acceleration for vehicles in the same lane. Secondly, in larger scale, the platoons around an intersection are considered. The throughput in a traffic signal period can be improved by taking advantage of the redundant road capacity. Within diverse constraints, vehicles can choose to accelerate to join in the preceding platoon or to decelerate to depart from the current one. Thirdly, an unsignalized intersection in VANET is considered. In light traffic conditions, vehicles can regulate their velocities before arriving at the intersection according to the conflict zone occupancy time (CZOT) stored at the manager, so that they could get through the intersection without collision or stop. The delay can be reduced accordingly. Finally, an universal autonomous intersection management algorithm, which can work even with heavy traffic, is developed. The vehicle searches for safe entering windows in the CZOT. Then based on the found windows and the motion of preceding vehicle, the trajectories of vehicles can be planned using a segmented dynamic programming method. All the designed algorithms are successfully tested and verified by simulations in various scenarios

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Smart, Royce Raymond, and roycesmart@hotmail com. "Evolutionary Control of Autonomous Underwater Vehicles." RMIT University. Aerospace, Mechanical and Manufacturing Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090331.143104.

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The goal of Evolutionary Robotics (ER) is the development of automatic processes for the synthesis of robot control systems using evolutionary computation. The idea that it may be possible to synthesise robotic control systems using an automatic design process is appealing. However, ER is considerably more challenging and less automatic than its advocates would suggest. ER applies methods from the field of neuroevolution to evolve robot control systems. Neuroevolution is a machine learning algorithm that applies evolutionary computation to the design of Artificial Neural Networks (ANN). The aim of this thesis is to assay the practical characteristics of neuroevolution by performing bulk experiments on a set of Reinforcement Learning (RL) problems. This thesis was conducted with the view of applying neuroevolution to the design of neurocontrollers for small low-cost Autonomous Underwater Vehicles (AUV). A general approach to neuroevolution for RL problems is presented. The is selected to evolve ANN connection weights on the basis that it has shown competitive performance on continuous optimisation problems, is self-adaptive and can exploit dependencies between connection weights. Practical implementation issues are identified and discussed. A series of experiments are conducted on RL problems. These problems are representative of problems from the AUV domain, but manageable in terms of problem complexity and computational resources required. Results from these experiments are analysed to draw out practical characteristics of neuroevolution. Bulk experiments are conducted using the inverted pendulum problem. This popular control benchmark is inherently unstable, underactuated and non-linear: characteristics common to underwater vehicles. Two practical characteristics of neuroevolution are demonstrated: the importance of using randomly generated evaluation sets and the effect of evaluation noise on search performance. As part of these experiments, deficiencies in the benchmark are identified and modifications suggested. The problem of an underwater vehicle travelling to a goal in an obstacle free environment is studied. The vehicle is modelled as a Dubins car, which is a simplified model of the high-level kinematics of a torpedo class underwater vehicle. Two practical characteristics of neuroevolution are demonstrated: the importance of domain knowledge when formulating ANN inputs and how the fitness function defines the set of evolvable control policies. Paths generated by the evolved neurocontrollers are compared with known optimal solutions. A framework is presented to guide the practical application of neuroevolution to RL problems that covers a range of issues identified during the experiments conducted in this thesis. An assessment of neuroevolution concludes that it is far from automatic yet still has potential as a technique for solving reinforcement problems, although further research is required to better understand the process of evolutionary learning. The major contribution made by this thesis is a rigorous empirical study of the practical characteristics of neuroevolution as applied to RL problems. A critical, yet constructive, viewpoint is taken of neuroevolution. This viewpoint differs from much of the reseach undertaken in this field, which is often unjustifiably optimistic and tends to gloss over difficult practical issues.

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Amir, Mohammad. "Semantically-enriched and semi-Autonomous collaboration framework for the Web of Things. Design, implementation and evaluation of a multi-party collaboration framework with semantic annotation and representation of sensors in the Web of Things and a case study on disaster management." Thesis, University of Bradford, 2015. http://hdl.handle.net/10454/14363.

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This thesis proposes a collaboration framework for the Web of Things based on the concepts of Service-oriented Architecture and integrated with semantic web technologies to offer new possibilities in terms of efficient asset management during operations requiring multi-actor collaboration. The motivation for the project comes from the rise in disasters where effective cross-organisation collaboration can increase the efficiency of critical information dissemination. Organisational boundaries of participants as well as their IT capability and trust issues hinders the deployment of a multi-party collaboration framework, thereby preventing timely dissemination of critical data. In order to tackle some of these issues, this thesis proposes a new collaboration framework consisting of a resource-based data model, resource-oriented access control mechanism and semantic technologies utilising the Semantic Sensor Network Ontology that can be used simultaneously by multiple actors without impacting each other’s networks and thus increase the efficiency of disaster management and relief operations. The generic design of the framework enables future extensions, thus enabling its exploitation across many application domains. The performance of the framework is evaluated in two areas: the capability of the access control mechanism to scale with increasing number of devices, and the capability of the semantic annotation process to increase in efficiency as more information is provided. The results demonstrate that the proposed framework is fit for purpose.

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Alliche, Abderrahmane Redha. "Contrôle du réseau cloud basé intelligence artificielle." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4022.

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L'explosion du nombre d'utilisateurs d'Internet et du volume de trafic constitue un défi majeur pour la gestion efficace des réseaux de diffusion de contenu (CDN). Bien que ces réseaux aient amélioré leur temps de réponse en exploitant la mise en cache dans des serveurs cloud proches des utilisateurs, les services non mis en cache continuent de poser des problèmes de gestion de trafic. Pour répondre à cette problématique, les réseaux overlay cloud ont émergé, mais ils introduisent des complexités telles que les violations d'inégalités triangulaires (TIV). Dans ce contexte, l'application du paradigme des réseaux à définition logicielle (SDN) combinée aux techniques d'apprentissage par renforcement profond (DRL) offre une opportunité prometteuse pour s'adapter en temps réel aux fluctuations de l'environnement. Face à l'augmentation constante du nombre de serveurs edge, les solutions distribuées de DRL, notamment les modèles d'apprentissage par renforcement profond multi-agent (MA-DRL), deviennent cruciales. Cependant, ces modèles rencontrent des défis non résolus tels que l'absence de simulateurs réseau réalistes, le surcoût de communication entre agents et la convergence et stabilité.Cette thèse se concentre donc sur l'exploration des méthodes MA-DRL pour le routage de paquets dans les réseaux overlay cloud. Elle propose des solutions pour relever ces défis, notamment le développement de simulateurs de réseau réalistes, l'étude du surcoût de communication et la conception d'une solution MA-DRL adaptée aux réseaux overlay cloud. L'accent est mis sur le compromis entre la performance et la quantité d'information partagée entre les agents, ainsi que sur la convergence et la stabilité durant l'entraînement
The exponential growth of Internet traffic in recent decades has prompted the emergence of Content Delivery Networks (CDNs) as a solution for managing high traffic volumes through data caching in cloud servers located near end-users. However, challenges persist, particularly for non-cacheable services, necessitating the use of cloud overlay networks. Due to a lack of knowledge about the underlay network, cloud overlay networks introduce complexities such as Triangle inequality violations (TIV) and dynamic traffic routing challenges.Leveraging the Software Defined Networks (SDN) paradigm, Deep Reinforcement Learning (DRL) techniques offer the possibility to exploit collected data to better adapt to network changes. Furthermore, the increase of cloud edge servers presents scalability challenges, motivating the exploration of Multi-Agent DRL (MA-DRL) solutions. Despite its suitability for the distributed packet routing problem in cloud overlay networks, MA-DRL faces non-addressed challenges such as the need for realistic network simulators, handling communication overhead, and addressing the multi-objective nature of the routing problem.This Ph.D. thesis delves into the realm of distributed Multi-Agent Deep Reinforcement Learning (MA-DRL) methods, specifically targeting the Distributed Packet Routing problem in cloud overlay networks. Throughout the thesis, we address these challenges by developing realistic network simulators, studying communication overhead in the non-overlay general setting, and proposing a distributed MA-DRL framework tailored to cloud overlay networks, focusing on communication overhead, convergence, and model stability

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Janet, Jason Andre. "Pattern analysis, tracking and control for autonomous vehicles using neural networks." Raleigh, NC : North Carolina State University, 1998. http://www.lib.ncsu.edu/etd/public/etd-45309999852811/etd.pdf.

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Janet, Jason Andre. "Pattern Analysis, Tracking and Control for Autonomous Mobile Robots Using Neural Networks." NCSU, 1998. http://www.lib.ncsu.edu/theses/available/etd-19981003-104929.

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Autonomous vehicles require that all on-board processes be efficient in time, complexity and data storage. Infact, an ideal system employs multi-funcitonal models where ever possible. The research documented hereproposes that the Region-Feature Neural Network (RFNN) and the Hyper-Ellipsoid Clustering (HEC)Kohonen neural network (or HECNN) are viable pattern analysis and control engines that contribute to thesolution of a variety of problems. The theoretical development of the RFNN and HECNN, along with severalproof-of-concept applications are presented in detail. The RFNN is a feed-forward, back-propagation modelthat is more general than standard textbook models because it also accomodates receptive fields and weightsharing. The RFNN uses a modified version of adaptive learning rates, called "shocking" to reduce training timeand maintain stability. Small-scale benchmark problems like the XOR and XOP problems are used todemonstrate the utility of the "shocking" model. Due to its modularity, the RFNN allows the user to constructflexible, multi-layered, feed-forward architectures as well as add to and prune from an architecture even aftertraining has begun. The RFNN also permits the user to include previously learned features, called "analogies" tofurther expedite the training process on new problems or whenever new classes are added. The HECNN is aself-organizing neural network that incorporates hyperellipsoid clustering by use of the Mahalanobis distance tolearn elongated shapes and obtain a stochastic measurement of data-node association. The number of nodes canalso be regulated in a self-organizing manner by measuring how well each node models the statistical propertiesof its associated data. This measurement, called "compactness", determines where and whether to add neuralunits or prune them completely. We make several enhancements to the Kolmogorov-Smirnov compactness testto control the triggering of mitosis and/or pruning. Because fewer nodes are needed for an HECNN than for aKohonen that uses only Euclidean distance, the data size is smaller for the HEC Kohonen, even forhigh-dimensional problems. The large-scale pattern analysis problems presented here for the RFNN includesonar pattern recognition and outdoor landmark recognition. For the HECNN, we focus on sonar patternrecognition and (topographical) map building. Both the RFNN and the HECNN can be generalized to solve orcontribute to the solution of other pattern recognition problems. Both are also multifunctional in that theyaccommodate compact geometric motion planning (MP), self-referencing (SR) and tracking algorithms.Additionally, we propose the "traversability vector" (t-vector) as an efficient bridge between the HECNN andboth motion planning and self-referencing for mobile robots. As with the RFNN and HECNN, the t-vector is amodular and multi-functional tool that minimizes the computation requirements and data size as it detects pathobstructions, Euclidean optimal via points, and geometric beacons, as well as identify which geometric featuresare visible to sensors in environments that can be static or dynamic. Tracking is made possible with Julier andUhlmann's unscented filter. The unscented filter particularly compliments the HECNN in that it performslow-level (non-linear) tracking more efficiently and more accurately than its predecessor, the extended Kalmanfilter (EKF). By estimating and propagating error covariances through system transformations, the unscentedfilter eliminates the need to derive Jacobian matrices. The inclusion of stochastic information inherent to the HECmap rendered the JUKF an excellent tool for our HEC-based map building, global self-localization, motionplanning and low-level tracking.

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Glöde, Isabella. "Autonomous control of a mobile robot with incremental deep learning neural networks." Master's thesis, Pontificia Universidad Católica del Perú, 2021. http://hdl.handle.net/20.500.12404/18676.

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Over the last few years autonomous driving had an increasingly strong impact on the automotive industry. This created an increased need for artificial intelligence algo- rithms which allow for computers to make human-like decisions. However, a compro- mise between the computational power drawn by these algorithms and their subsequent performance must be found to fulfil production requirements. In this thesis incremental deep learning strategies are used for the control of a mobile robot such as a four wheel steering vehicle. This strategy is similar to the human approach of learning. In many small steps the vehicle learns to achieve a specific goal. The usage of incremental training leads to growing knowledge-base within the system. It also provides the opportunity to use older training achievements to improve the system, when more training data is available. To demonstrate the capabilities of such an algorithm, two different models have been formulated. First, a more simple model with counter wheel steering, and second, a more complex, nonlinear model with independent steering. These two models are trained incrementally to follow different types of trajectories. Therefore an algorithm was established to generate useful initial points. The incremental steps allow the robot to be positioned further and further away from the desired trajectory in the environ- ment. Afterwards, the effects of different trajectory types on model behaviour are investigated by over one thousand simulation runs. To do this, path planning for straight lines and circles are introduced. This work demonstrates that even simulations with simple network structures can have high performance.

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Richard, Mark G. "Cooperative control of distributed autonomous systems with applications to wireless sensor networks." Thesis, Monterey, Calif. : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Jun/09Jun%5FRichard.pdf.

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Thesis (M.S. in Mechanical Engineering)--Naval Postgraduate School, June 2009.
Thesis Advisor(s): Lee, Deok Jin ; Kaminer, Issac I. "June 2009." Description based on title screen as viewed on 13 July 2009. Author(s) subject terms: Unmanned Aerial Vehicle, UAV, extremum seeking, simulink, high bandwidth communication links, SNR Model, coordinated control, cooperative control, decentralized control, wireless sensor network. Includes bibliographical references (p. 51). Also available in print.

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Водоп'янов, Сергій В’ячеславович. "Методи побудови автономних комп’ютерних сегментів аеровузлової мережі." Thesis, Національний авіаційний університет, 2018. http://er.nau.edu.ua/handle/NAU/37402.

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ЗМІСТ ПЕРЕЛІК УМОВНИХ ПОЗНАЧЕНЬ ВСТУП РОЗДІЛ 1 АНАЛІТИЧНИЙ ОГЛЯД І ПОСТАНОВКА ЗАДАЧІ ДОСЛІДЖЕННЯ 1.1 Терміни та визначення 1.2 Перспективи вдосконалення національної системи організації повітряного руху 1.3 Сучасний стан та перспективи розвитку авіаційних управляючих та інформаційно-обчислювальних систем 1.4 Стан проблеми побудови автономних мережних сегментів для перспективних інформаційно-обчислювальних систем авіації Висновки до розділу 1 РОЗДІЛ 2 МОДЕЛЬ ІЄРАРХІЧНОЇ БАГАТОРІВНЕВОЇ СТРУКТУРИ АЕРОВУЗЛОВОЇ МЕРЕЖІ 2.1 Принципи побудови та функціонування сучасної інформаційно-обчислювальної мережі аеровузла 2.2 Ключові показники управління якістю сервісу в інформаційно-обчислювальній мережі аеровузла 2.3 Кореляційно-регресійна модель системи ключових показників ефективності ієрархічної мережі аеровузла Висновки до розділу 2 РОЗДІЛ 3 МЕТОДИ ОЦІНЮВАННЯ ТА ЗАБЕЗПЕЧЕННЯ ЕФЕКТИВНОСТІ АВТОНОМНИХ СЕГМЕНТІВ АЕРОВУЗЛОВОЇ МЕРЕЖІ 3.1 Специфіка авіаційних бортових комп'ютерних мереж 3.2 Технологія взаємодії мобільних мережних вузлів 3.3 Розробка моделі та методу забезпечення якості сервісу в мережі з мобільними вузлами 3.4 Моделі й метод оцінювання характеристик та управління автономними сегментами інформаційно-керуючої системи крупного аеровузла Висновки до розділу 3 РОЗДІЛ 4 РЕКОМЕНДАЦІЇ З ВИБОРУ СТРУКТУРИ ТА ПАРАМЕТРІВ АЕРОВУЗЛОВОЇ ІНФОРМАЦІЙНО-КОМУНІКАЦІЙНОЇ СИСТЕМИ 4.1 Загальні вимоги до аеровузлової мережі 4.2 Аналіз методів обміну різнорідним та різнопріоритетним мережним трафіком 4.3 Розробка методу діагностування автономних сегментів аеровузлової мережі 4.4 Вибір та обґрунтування варіантів побудови аеровузлової мережі Висновки до розділу 4 ВИСНОВКИ СПИСОК ВИКОРИСТАНИХ ДЖЕРЕЛ ДОДАТОК А .АКТИ ПРО ВПРОВАДЖЕННЯ РЕЗУЛЬТАТІВ ДОСЛІДЖЕНЬ ДОДАТОК Б. СПИСОК ПУБЛІКАЦІЙ ЗДОБУВАЧА ЗА ТЕМОЮ ДИСЕРТАЦІЇ ТА ВІДОМОСТІ ПРО АПРОБАЦІЮ РЕЗУЛЬТАТІВ ДИСЕРТАЦІЇ
У дисертаційній роботі розроблено методи побудови автономних сегментів інформаційно-обчислювальної системи реального часу для роботи в умовах критичного застосування, значних коливань навантаження та виникнення екстремальних ситуацій в аеровузлових мережах складеного типу з високим степенем гетерогенності. Розроблено математичну модель бортової локальної мережі з мобільними вузлами та спорадичною зміною структури, коли одні вузли виходять з зони дії мережі, нові вузли з’являються. Виведено розрахункові формули для середнього часу очікування заявок у пам'яті, середньої довжини черги тощо для змішаного – еластичного та нееластичного трафіку. Розраховані можливі розміри черг у буферній пам'яті мережних вузлів за наявністю заявок (сигнальних пакетів) з необмеженим часом очікування та заявок з обмеженим часом очікування у чергах. За результатами розрахунків можна обирати максимально можливий коефіцієнт використання мережі, при якому зростання черги у буферній пам'яті є припустимим. Запропоновані структури сегментів аеровузлової мережі і локальних обчислювальних мереж для систем критичного застосування, які можуть служити в основі побудови інформаційно-обчислювальної підсистеми АС ОрПР, тобто розгалуженої аеровузлової мережі з автономними супутниковими та авіаційними бортовими мережними сегментами.

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Beckman, Erik, and Linus Harenius. "Monitored Neural Networks for Autonomous Articulated Machines." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48708.

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Being able to safely control autonomous heavy machinery is of uttermost importance for the conversion of traditional machines to autonomous machines. With the continuous growth of autonomous vehicles around the globe, an increasing effort has been put into certifying autonomous vehicles in terms of reliability and safety. In this thesis, we will investigate the problem with a deviation from the planned path for an autonomous hauler from Volvo Construction Equipment. The autonomous hauler has an error within the kinematic model, the feed-forward curvature-steering controller, due to a slip-effect that comes with the third wheel-axle. The deviation can especially be seen in sharp curves, where the deviation needs to be decreased in order to make the autonomous hauler more dependable and achieve an increased accuracy when following any given path. The aim of the thesis is to develop a fully functional Artificial Neural Network that has a new steering angle as output. The hypothesis for this thesis is to use an ANN to mimic the steering of a human driver, since a real driver compensates for the slipping behavior; both because the operator knows where on the road the machine is and also in the way that a human thinks many steps ahead whilst driving. This proposed ANN will have a monitor function which ensures that the steering angle command operates within its boundaries. Hence this thesis implies that it is indeed possible to ensure that the ANN performs reliably with the help of a monitor function in a simulated environment and can thus be used in dependable systems.

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Ahmad, Syed Amaar. "Autonomous Link-Adaptive Schemes for Heterogeneous Networks with Congestion Feedback." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/46725.

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LTE heterogeneous wireless networks promise significant increase in data rates and improved coverage through (i) the deployment of relays and cell densification, (ii) carrier aggregation to enhance bandwidth usage and (iii) by enabling nodes to have dual connectivity. These emerging cellular networks are complex and large systems which are difficult to optimize with centralized control and where mobiles need to balance spectral efficiency, power consumption and fairness constraints. In this dissertation we focus on how decentralized and autonomous mobiles in multihop cellular systems can optimize their own local objectives by taking into account end-to-end or network-wide conditions. We propose several link-adaptive schemes where nodes can adjust their transmit power, aggregate carriers and select points of access to the network (relays and/or macrocell base stations) autonomously, based on both local and global conditions. Under our approach, this is achieved by disseminating the dynamic congestion level in the backhaul links of the points of access. As nodes adapt locally, the congestion levels in the backhaul links can change, which can in turn induce them to also change their adaptation objectives. We show that under our schemes, even with this dynamic congestion feedback, nodes can distributedly converge to a stable selection of transmit power levels and points of access. We also analytically derive the transmit power levels at the equilibrium points for certain cases. Moreover, through numerical results we show that the corresponding system throughput is significantly higher than when nodes adapt greedily following traditional link layer optimization objectives. Given the growing data rate demand, increasing system complexity and the difficulty of implementing centralized cross-layer optimization frameworks, our work simplifies resource allocation in heterogeneous cellular systems. Our work can be extended to any multihop wireless system where the backhaul link capacity is limited and feedback on the dynamic congestion levels at the access points is available.
Ph. D.

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38

Grebner, Anna-Maria Stephanie. "Autonomous obstacle avoidance and positioning control of mobile robots using fuzzy neural networks." Master's thesis, Pontificia Universidad Católica del Perú, 2018. http://tesis.pucp.edu.pe/repositorio/handle/123456789/12893.

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Navigation and obstacle avoidance are important tasks in the research field of au- tonomous mobile robots. The challenge tackled in this work is the navigation of a 4- wheeled car-type robot to a desired parking position while avoiding obstacles on the way. The taken approach to solve this problem is based on neural fuzzy techniques. Earlier works resulted in a controller to navigate the robot in a clear environment. It is extended by considering additional parameters in the training process. The learning method used in this training is dynamic backpropagation. For the obstacle avoidance problem an additional neuro-fuzzy controller is set up and trained. It influences the results from the navigation controller to avoid collisions with objects blocking the path. The controller is trained with dynamic backpropagation and a reinforcement learning algorithm called deep deterministic policy gradient.
Tesis

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39

Pérez, Guirao María Dolores. "Cross layer, cognitive, cooperative pulse rate control for autonomous, low power, IR-UWB networks." Aachen Shaker, 2008. http://d-nb.info/993570593/04.

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40

Buttar, Sarpreet Singh. "Applying Artificial Neural Networks to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87117.

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Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation space is small. The size of the adaptation space is often in hundreds or thousands, which makes formal analysis approaches inefficient in large-scale self-adaptive systems. In this thesis, we tackle this problem by presenting an online learning approach that enables formal analysis approaches to analyze large adaptation spaces efficiently. The approach integrates with the standard feedback loop and reduces the adaptation space to a subset of adaptation options that are relevant to the current runtime uncertainties. The subset is then analyzed by the formal analysis approaches, which allows them to complete the analysis faster and efficiently within the available time. We evaluate our approach on two different instances of an Internet of Things application. The evaluation shows that our approach dramatically reduces the adaptation space and analysis time without compromising the adaptation goals.

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Watson, Simon Andrew. "Mobile platforms for underwater sensor networks." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/mobile-platforms-for-underwater-sensor-networks(00f93130-f9d6-4479-80ab-58a0c60327c0).html.

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The production of clean water, the generation of nuclear power and the development of chemicals, petro-chemicals and pharmaceuticals all rely on liquid-based processes. They are fundamental to modern society, however the real-time monitoring of such processes is an inherently difficult challenge which has not yet been satisfactorily solved.Current methods of monitoring include on- and off-line spot checks and industrial process tomography. Neither of these methods provides the spatial or temporal resolution required to properly characterise the processes. This research project proposes a new monitoring method for processes which can tolerate foreign objects; a mobile underwater sensor network (MUSN).An MUSN has the potential to increase both the spatial and temporal resolution of measurements and could be used in real-time. The network would be formed by a number of mobile sensor platforms, in the form of micro-autonomous underwater vehicles (uAUVs) which would communicate using acoustics. The demonstrator for the technology is for use in the monitoring of nuclear storage ponds.Current AUV technology is not suitable for use in enclosed environments such as storage ponds due to the size and maneuverability. This thesis presents the research conducted in the development of a new vehicle uAUV. The work presented covers the mechatronic aspects of the vehicle; the design of the hull, propulsion systems, corresponding control circuitry and basic motion control systems. One of the main factors influencing the design of the vehicle has been cost. If a large number of vehicles are used to form a network, the cost of an individual uAUV should be kept as low as possible. This has raised a number of technical challenges as low-cost components are often of low-tolerance. Imbalanced time-varying thrust, low manufacturing tolerances and noisy indirect sensor measurements for the control systems have all been overcome in the design of the vehicle. The outcome of the research is a fully functional prototype uAUV. The vehicle is spherical in shape with a diameter of approximately 15cm, with six thruster units mounted around the equator (increasing the horizontal clearance to 20cm) to provide thrust in four degrees of freedom (surge, sway, heave and yaw). The vehicle has a sensor suite which includes a pressure sensor, digital compass and a gyroscope which provide inputs to the motion control systems. The controllers have been developed and implemented on the vehicle's custom built embedded system. Experiments have been conducted showing that the uAUV is able to move in 3D with closed-loop control in heave and yaw. Motion in surge and sway is open-loop, via a dead-reckoning system.

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Rypkema, Nicholas Rahardiyan. "Distributed autonomy and formation control of a drifting swarm of autonomous underwater vehicles." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101474.

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Thesis: S.M., Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 163-168).
Recent advances in autonomous underwater vehicle (AUV) technology have led to their wide- spread acceptance and adoption for use in scientific, commercial, and defence applications in the underwater domain. At the same time, research progress in swarm robotics has seen swarm intelligence algorithms in use with greater eect on real-world robots in the field. A group of AUVs utilizing swarm intelligence concepts has the potential to address issues more effectively than a single AUV, and such a group can potentially open up new areas of application. Examples include the monitoring and tracking of highly dynamic oceanographic phenomena such as phytoplankton blooms and the use of an AUV swarm as a virtual acoustic receiver for sea-bottom seismic surveying or the monitoring of naturally occurring acoustic radiation from cracking ice. However, the limitations of the undersea environment places unique constraints on the use of existing swarm robotics approaches with AUVs. In particular, algorithms must be distributed and robust in the face of localization error and degraded communications. This work presents an investigation into one particular swarm strategy for a group of AUVs, termed formation control, with consideration to the constraints of the underwater domain. Four formation control algorithms, each developed and tested within the MOOS-IvP framework, are presented. In addition, a 'formation quality' metric is introduced. This metric is used in conjunction with a measure of formation energy expenditure to compare the efficacy of each behaviour during construction of a desired formation, and formation maintenance while it drifts in ocean currents. This metric is also used to compare robustness of each algorithm in the presence of vehicle failure and changing communication rate.
by Nicholas Rahardiyan Rypkema.
S.M.

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Sattigeri, Ramachandra Jayant. "Adaptive Estimation and Control with Application to Vision-based Autonomous Formation Flight." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16272.

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The role of vision as an additional sensing mechanism has received a lot of attention in recent years in the context of autonomous flight applications. Modern Unmanned Aerial Vehicles (UAVs) are equipped with vision sensors because of their light-weight, low-cost characteristics and also their ability to provide a rich variety of information of the environment in which the UAVs are navigating in. The problem of vision based autonomous flight is very difficult and challenging since it requires bringing together concepts from image processing and computer vision, target tracking and state estimation, and flight guidance and control. This thesis focuses on the adaptive state estimation, guidance and control problems involved in vision-based formation flight. Specifically, the thesis presents a composite adaptation approach to the partial state estimation of a class of nonlinear systems with unmodeled dynamics. In this approach, a linear time-varying Kalman filter is the nominal state estimator which is augmented by the output of an adaptive neural network (NN) that is trained with two error signals. The benefit of the proposed approach is in its faster and more accurate adaptation to the modeling errors over a conventional approach. The thesis also presents two approaches to the design of adaptive guidance and control (G&C) laws for line-of-sight formation flight. In the first approach, the guidance and autopilot systems are designed separately and then combined together by assuming time-scale separation. The second approach is based on integrating the guidance and autopilot design process. The developed G&C laws using both approaches are adaptive to unmodeled leader aircraft acceleration and to own aircraft aerodynamic uncertainties. The thesis also presents theoretical justification based on Lyapunov-like stability analysis for integrating the adaptive state estimation and adaptive G&C designs. All the developed designs are validated in nonlinear, 6DOF fixed-wing aircraft simulations. Finally, the thesis presents a decentralized coordination strategy for vision-based multiple-aircraft formation control. In this approach, each aircraft in formation regulates range from up to two nearest neighboring aircraft while simultaneously tracking nominal desired trajectories common to all aircraft and avoiding static obstacles.

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Young, Forrest C. "Phoenix autonomous underwater vehicle (AUV) : networked control of multiple analog and digital devices using LonTalk /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA342308.

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Thesis (M.S. in Electrical Engineering)) Naval Postgraduate School, December 1997.
"December 1997." Thesis advisor(s): Xiaoping Yun, Don Brutzman. DTIC Descriptors: Underwater Vehicles, Autonomous Navigation, Digital Communications, Signal Processing, Robotics, Real Time, Robots, Computer Architecture, Theses, Analog to Digital Converters, Digital To Analog Converters. Author(s) subject terms: Autonomous Underwater Vehicle, AUV, Networked Control, Lon Works Technology, LonTalk, LonBuilder. Includes bibliographical references (p. 93-94). Also available online.

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Pérez, Guirao María Dolores [Verfasser]. "Cross-Layer, Cognitive, Cooperative Pulse Rate Control for Autonomous, Low Power, IR-UWB Networks / María Dolores Pérez Guirao." Aachen : Shaker, 2009. http://d-nb.info/112637864X/34.

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Kannan, Suresh Kumar. "Adaptive Control of Systems in Cascade with Saturation." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7566.

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This thesis extends the use of neural-network-based model reference adaptive control to systems that occur as cascades. In general, these systems are not feedback linearizable. The approach taken is that of approximate feedback linearization of upper subsystems whilst treating the lower-subsystem states as virtual actuators. Similarly, lower-subsystems are also feedback linearized. Typically, approximate inverses are used for linearization purposes. Model error arising from the use of an approximate inverse is minimized using a neural-network as an adaptive element. Incorrect adaptation due to (virtual) actuator saturation and dynamics is avoided using the Pseudocontrol Hedging method. Using linear approximate inverses and linear reference models generally result in large desired pseudocontrol for large external commands. Even if the provided external command is feasible (null-controllable), there is no guarantee that the reference model trajectory is feasible. In order to mitigate this, nonlinear reference models based on nested-saturation methods are used to constrain the evolution of the reference model and thus the plant states. The method presented in this thesis lends itself to the inner-outer loop control of air vehicles, where the inner-loop controls attitude dynamics and the outer-loop controls the translational dynamics of the vehicle. The outer-loop treats the closed loop attitude dynamics as an actuator. Adaptation to uncertainty in the attitude, as well as the translational dynamics, is introduced, thus minimizing the effects of model error in all six degrees of freedom and leading to more accurate position tracking. A pole-placement approach is used to choose compensator gains for the tracking error dynamics. This alleviates timescale separation requirements, allowing the outer loop bandwidth to be closer to that of the inner loop, thus increasing position tracking performance. A poor model of the attitude dynamics and a basic kinematics model is shown to be sufficient for accurate position tracking. In particular, the inner-outer loop method was used to control an unmanned helicopter and has subsequently been applied to a ducted-fan, a fixed-wing aircraft that transitions in and out of hover, and a full-scale rotorcraft. Experimental flight test results are also provided for a subset of these vehicles.

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Jugade, Shriram. "Shared control authority between human and autonomous driving system for intelligent vehicles." Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2507.

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Ce travail aborde le problème de l’autorité de contrôle partagée entre les conducteurs et système de conduite autonome sans retour haptique utilisant la fusion des entrées de conduite. Le développement d’une autorité de contrôle partagée est divisé en différentes étapes : cadre de contrôle partagé, évaluation des facteurs de conduite, prévision du comportement de conduite, processus de fusion, etc. La résolution des conflits est la stratégie de haut niveau introduite dans le cadre permettant de réaliser la fusion. Les entrées de conduite sont évaluées en fonction de différents facteurs tels que le risque de collision, la limitation de vitesse, la prévention de voie / départ, etc., sous la forme d’un degré de confiance dans l’admissibilité d’une entrée de conduite à l’aide de données de capteur. La résolution de conflit est ciblée pour un horizon temporel particulier dans le futur en utilisant une prédiction d’entrée de conduite basée sur un capteur utilisant des réseaux de neurones. Un jeu non coopératif à deux joueurs (comprenant l’admissibilité et l’intention de conduite) est défini comme représentant la résolution du conflit comme un problème de négociation. L’entrée motrice finale est calculée en utilisant l’équilibre de Nash. La stratégie de contrôle partagé est validée à l’aide d’un banc d’essai intégré aux logiciels Simulink et IPG CarMaker. Divers aspects de la stratégie de contrôle partagé, tels que l’accent mis sur l’homme, la prévention des collisions, l’absence de toute information sur la conduite, l’affinement de la conduite manuelle, etc., ont été inclus dans le processus de validation
Road traffic accidents have always been a concern to the driving community which has led to various research developments for improving the way we drive the vehicles. Since human error causes most of the road accidents, introducing automation in the vehicle is an efficient way to address this issue thus making the vehicles intelligent. This approach has led to the development of ADAS (Advanced Driver Assistance Systems) functionalities. The process of introducing automation in the vehicle is continuously evolving. Currently the research in this field has targeted full autonomy of the vehicle with the aim to tackle the road safety to its fullest potential. The gap between ADAS and full autonomy is not narrow. One of the approach to bridge this gap is to introduce collaboration between human driver and autonomous system. There have been different methodologies such as haptic feedback, cooperative driving where the autonomous system adapts according to the human driving inputs/intention for the corrective action each having their own limitations. This work addresses the problem of shared control authority between human driver and autonomous driving system without haptic feedback using the fusion of driving inputs. The development of shared control authority is broadly divided into different stages i.e. shared control framework, driving input assessment, driving behavior prediction, fusion process etc. Conflict resolution is the high level strategy introduced in the framework for achieving the fusion. The driving inputs are assessed with respect to different factors such as collision risk, speed limitation,lane/road departure prevention etc in the form of degree of belief in the driving input admissibility using sensor data. The conflict resolution is targeted for a particular time horizon in the future using a sensor based driving input prediction using neural networks. A two player non-cooperative game (incorporating admissibility and driving intention) is defined to represent the conflict resolution as a bargaining problem. The final driving input is computed using the Nash equilibrium. The shared control strategy is validated using a test rig integrated with the software Simulink and IPG CarMaker. Various aspects of shared control strategy such as human-centered, collision avoidance, absence of any driving input, manual driving refinement etc were included in the validation process

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Ruini, Fabio. "Distributed control for collective behaviour in micro-unmanned aerial vehicles." Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1549.

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The work presented herein focuses on the design of distributed autonomous controllers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complexity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms, that require a high degree of accuracy by the controller. Concerning the ER approach, the experimental setups elaborated have resulted in controllers that have been evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviours based on implicit communication strategies. The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic platforms. A customised implementation of Reynolds’ flocking algorithm has been developed and successfully validated through flight tests performed with the swinglet MAV. It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past. The investigations performed have also shown that complex and real physics-based computer simulators are not a compulsory requirement when approaching the domain of aerial robotics, as long as proper autopilot systems (taking care of the ”reality gap” issue) are used on the real robots.

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Abdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering." Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.

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This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.

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Tanguy, Roger. "Un reseau mobiles autonomes pour l'apprentissage de la communication." Paris 6, 1987. http://www.theses.fr/1987PA066640.

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Presentation de la realisation materielle et logicielle d'un reseau de mobiles autonomes et programmables destine a l'apprentissage d'actions elementaires et de concepts tels que la communication et la coordination

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Dissertations / Theses on the topic 'Autonomous network control'

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