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Academic literature on the topic 'Autonomous network control'

Author: Grafiati

Published: 31 August 2024

Last updated: 19 July 2025

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Journal articles on the topic "Autonomous network control"

Kim, Jae-Hoon, Seungchul Lee, and Sengphil Hong. "Autonomous Operation Control of IoT Blockchain Networks." Electronics 10, no. 2 (2021): 204. http://dx.doi.org/10.3390/electronics10020204.

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Internet of Things (IoT) networks are typically composed of many sensors and actuators. The operation controls for robots in smart factories or drones produce a massive volume of data that requires high reliability. A blockchain architecture can be used to build highly reliable IoT networks. The shared ledger and open data validation among users guarantee extremely high data security. However, current blockchain technology has limitations for its overall application across IoT networks. Because general permission-less blockchain networks typically target high-performance network nodes with sufficient computing power, a blockchain node with low computing power and memory, such as an IoT sensor/actuator, cannot operate in a blockchain as a fully functional node. A lightweight blockchain provides practical blockchain availability over IoT networks. We propose essential operational advances to develop a lightweight blockchain over IoT networks. A dynamic network configuration enforced by deep clustering provides ad-hoc flexibility for IoT network environments. The proposed graph neural network technique enhances the efficiency of dApp (distributed application) spreading across IoT networks. In addition, the proposed blockchain technology is highly implementable in software because it adopts the Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed methods.

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Alsuwian, Turki, Mian Hamza Usman, and Arslan Ahmed Amin. "An Autonomous Vehicle Stability Control Using Active Fault-Tolerant Control Based on a Fuzzy Neural Network." Electronics 11, no. 19 (2022): 3165. http://dx.doi.org/10.3390/electronics11193165.

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Due to instability issues in autonomous vehicles, the risk of danger is increasing rapidly. These problems arise due to unwanted faults in the sensor or the actuator, which decrease vehicle efficiency. In this modern era of autonomous vehicles, the risk factor is also increased as the vehicles have become automatic, so there is a need for a fault-tolerant control system (FTCS) to avoid accidents and reduce the risk factors. This paper presents an active fault-tolerant control (AFTC) for autonomous vehicles with a fuzzy neural network that can autonomously identify any wheel speed problem to avoid instability issues in an autonomous vehicle. MATLAB/Simulink environment was used for simulation experiments and the results demonstrate the stable operation of the wheel speed sensors to avoid accidents in the event of faults in the sensor or actuator if the vehicle becomes unstable. The simulation results establish that the AFTC-based autonomous vehicle using a fuzzy neural network is a highly reliable solution to keep cars stable and avoid accidents. Active FTC and vehicle stability make the system more efficient and reliable, decreasing the chance of instability to a minimal point.

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S, Ms Rajeshwari, Thilak T M, and Mahanthesh J R. "Autonomous Systems Control Design Using Neuro Evolution." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–8. http://dx.doi.org/10.55041/ijsrem38459.

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This paper explores the application of neuro-evolution techniques in the design of control systems for autonomous systems. Neuro-evolution merges neural networks with evolutionary algorithms to develop adaptive, real-time control strategies for autonomous platforms. This approach is particularly effective in dynamic and complex environments, where traditional control methods struggle to ensure optimal performance. By evolving neural network structures, neuro-evolution provides robust and flexible control solutions, with promising applications in fields such as robotics, autonomous vehicles, and drone navigation. The design of autonomous systems has rapidly advanced, fueled by the need for intelligent, adaptive control in environments that are complex and unpredictable. Traditional control systems often lack the flexibility required to handle dynamic changes, making them less effective in highly variable conditions. Neuro-evolution, which combines neural networks with evolutionary algorithms, offers a promising solution by enabling autonomous systems to develop and refine control strategies on their own. This approach leverages evolutionary principles to optimize neural network architectures, producing control systems that adapt in real-time and improve through experience. Neuro-evolution has gained significant attention for applications in robotics, self- driving vehicles, and aerospace, where autonomous, robust control is essential. This paper explores the role of neuro-evolution in enhancing control design, focusing on its advantages over conventional methods and its potential to drive further innovations in autonomous systems.

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Kim, Eric J., and Ruben E. Perez. "Neuroevolutionary Control for Autonomous Soaring." Aerospace 8, no. 9 (2021): 267. http://dx.doi.org/10.3390/aerospace8090267.

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The energy efficiency and flight endurance of small unmanned aerial vehicles (SUAVs) can be improved through the implementation of autonomous soaring strategies. Biologically inspired flight techniques such as dynamic and thermal soaring offer significant energy savings through the exploitation of naturally occurring wind phenomena for thrustless flight. Recent interest in the application of artificial intelligence algorithms for autonomous soaring has been motivated by the pursuit of instilling generalized behavior in control systems, centered around the use of neural networks. However, the topology of such networks is usually predetermined, restricting the search space of potential solutions, while often resulting in complex neural networks that can pose implementation challenges for the limited hardware onboard small-scale autonomous vehicles. In exploring a novel method of generating neurocontrollers, this paper presents a neural network-based soaring strategy to extend flight times and advance the potential operational capability of SUAVs. In this study, the Neuroevolution of Augmenting Topologies (NEAT) algorithm is used to train efficient and effective neurocontrollers that can control a simulated aircraft along sustained dynamic and thermal soaring trajectories. The proposed approach evolves interpretable neural networks in a way that preserves simplicity while maximizing performance without requiring extensive training datasets. As a result, the combined trajectory planning and aircraft control strategy is suitable for real-time implementation on SUAV platforms.

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Botchkaryov, A. "METHOD FOR DECENTRALIZED CONTROL OF ADAPTIVE DATA COLLECTION PROCESSES IN AUTONOMOUS DISTRIBUTED SYSTEMS." Computer systems and network 5, no. 1 (2023): 8–19. http://dx.doi.org/10.23939/csn2023.01.008.

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The problem of monitoring a computer network under conditions of limitations on the use of system resources and high requirements for the survivability of the monitoring system has been considered. An autonomous decentralized computer network monitoring system has been developed, consisting of a team of software agents. Each agent can operate in two modes: main mode and monitoring system management console mode. In the main mode, the agent collects information about the computer network. In management console mode, the agent provides the user with access to information collected by all agents and allows the user to execute commands to manage the monitoring system. The developed monitoring system allows you to obtain more reliable information about the operation of the network with greater efficiency under the conditions of limitations on the use of system resources specified by the user. The autonomous monitoring system is created on the basis of the concept of multi-agent systems, within which a software agent of the system has some initiative for planning and implementing monitoring scenarios. The operation of software agents implements methods for organizing adaptive processes for collecting information using the principles of self-organization and the concept of structural adaptation. A decentralized software architecture for an autonomous monitoring system without a control center has been proposed. This ensures high reliability and survivability of the monitoring system. The software architecture of the autonomous monitoring system implements the SMA application software interface and the corresponding software library, which allows you to collect statistical data on the operation of the computer network and its nodes. The implementation of a software agent and a management console for an autonomous computer network monitoring system has been considered. Key words: computer network monitoring, autonomous system, decentralized control, software agent

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Akimoto, Y., H. Tanaka, H. Ogi, H. Taoka, S. Nishida, and T. Sakaguchi. "Autonomous Distributed Network Architecture for Control System." IFAC Proceedings Volumes 21, no. 12 (1988): 21–27. http://dx.doi.org/10.1016/b978-0-08-036938-9.50009-1.

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Jawad, Luay, Arshdeep Singh-Chudda, Abhishek Shankar, and Abhilash Pandya. "A Deep Learning Approach to Merge Rule-Based and Human-Operated Camera Control for Teleoperated Robotic Systems." Robotics 13, no. 3 (2024): 47. http://dx.doi.org/10.3390/robotics13030047.

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Controlling a laparoscopic camera during robotic surgery represents a multifaceted challenge, demanding considerable physical and cognitive exertion from operators. While manual control presents the advantage of enabling optimal viewing angles, it is offset by its taxing nature. In contrast, current autonomous camera systems offer predictability in tool tracking but are often rigid, lacking the adaptability of human operators. This research investigates the potential of two distinct network architectures: a dense neural network (DNN) and a recurrent network (RNN), both trained using a diverse dataset comprising autonomous and human-driven camera movements. A comparative assessment of network-controlled, autonomous, and human-operated camera systems is conducted to gauge network efficacies. While the dense neural network exhibits proficiency in basic tool tracking, it grapples with inherent architectural limitations that hinder its ability to master the camera’s zoom functionality. In stark contrast, the recurrent network excels, demonstrating a capacity to sufficiently replicate the behaviors exhibited by a mixture of both autonomous and human-operated methods. In total, 96.8% of the dense network predictions had up to a one-centimeter error when compared to the test datasets, while the recurrent network achieved a 100% sub-millimeter testing error. This paper trains and evaluates neural networks on autonomous and human behavior data for camera control.

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Gao, Youtao, Zhicheng You, and Bo Xu. "Integrated Design of Autonomous Orbit Determination and Orbit Control for GEO Satellite Based on Neural Network." International Journal of Aerospace Engineering 2020 (January 21, 2020): 1–13. http://dx.doi.org/10.1155/2020/3801625.

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In order to improve the autonomy of a maneuvered GEO satellite which is a member of a navigation satellite system, an integrated design method of autonomous orbit determination and autonomous control was proposed. A neural network state observer was designed to estimate the state of the GEO satellite, with only the intersatellite ranging information as observations. The controller is determined autonomously by another neural network based on the estimated state and the preset correction trajectory. A gradient descent learning method with a forgetting factor was used to derive the weight updating strategy which can satisfy the system’s stability and real-time performance. A Lyapunov method was used to prove the stability of both the observer and the controller. The neural network observer can reduce the influence of control on autonomous orbit determination. The neural network controller can improve the robustness of the maneuvered GEO satellite. The simulation results show the effectiveness of this method.

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Kumar, Dr A. Dinesh. "Underwater Gripper using Distributed Network and Adaptive Control." Journal of Electrical Engineering and Automation 2, no. 1 (2020): 43–49. http://dx.doi.org/10.36548/jeea.2020.1.005.

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Underwater identification and grasping of objects is a major challenge faced by the marine engineers even today. Nowadays, almost all underwater operations are either autonomous or tele-operated. In fact remotely operated vehicles (ROVs) are used to deal with inspection tasks and industrial maintenance whenever there is need for intervention. However, the field of autonomous underwater vehicle (AUV) is a blooming filed with research involving proper moving base control and forces interacting which leads to complicated configuration. Hence the presented work is focused implementation of end-effector with appropriate control and signal processing resulting in autonomous manipulation of movement under water.

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Jain, Ankur, and B. K. Roy. "Online Control of a Nonlinear Autonomous Vehicle in the Presence of Network Delay." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (2019): 344–51. http://dx.doi.org/10.5373/jardcs/v11sp12/20193230.

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Dissertations / Theses on the topic "Autonomous network control"

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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Abdulazeez, Abdulhakeem. "Development of an autonomous control protocol for a UAV fleet network using machine learning." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG107.

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Dans le contexte de la robotique en réseau sans fil, des missions complexes, telles que la recherche et le sauvetage autonomes, peuvent nécessiter l’utilisation de plusieurs véhicules aériens sans pilote (UAV) déployés en formation afin d’augmenter l’efficacité et la qualité de service (QoS) tout en garantissant des impératifs spécifiques à la mission, tels que la maximisation de la couverture d’une zone en un seul passage de la flotte sur des sous-sections de celle-ci. Pour relever ces défis, un protocole efficace de localisation et de contrôle de formation est nécessaire.Les solutions existantes proposées pour la localisation à faible coût des UAV utilisant les valeurs de l’indicateur de puissance du signal reçu (RSSI) ont été conçues pour des environnements intérieurs, en réponse à la limitation du Global Navigation Satellite System (GNSS) en intérieur, ce qui les rend inapplicables aux environnements extérieurs.Celles développées pour fonctionner en extérieur sont soit inadaptées aux nœuds mobiles, soit inapplicables aux dispositifs aériens déployés dans des environnements complexes avec des effets de propagation variables. De plus, les protocoles de contrôle existants spécifiquement conçus pour le contrôle autonome des flottes de drones ne tiennent pas compte de l’orientation de la formation.Afin de couvrir ces aspects, cette thèse propose un protocole de contrôle de formation basé sur l’apprentissage, comprenant trois mécanismes de contrôle pour aborder les questions d’estimation de distance en extérieur, d’adaptation de la vitesse et d’orientation de la formation. Le protocole adapte le principe d’apprentissage par renforcement multi-agents (MARL) pour autopiloter la structure leader-suiveur de la flotte de drones en utilisant uniquement les valeurs RSSI reçues par les UAV lors des communications sans fil avec leursvoisins. Les suiveurs doivent suivre le leader tout en maintenant la structure leader-suiveur préconfigurée de manière autonome. Tout d’abord, un estimateur de distance basé sur un réseau de neurones profonds (DNN) utilisant le RSSI a été développé pour fournir des estimations plus précises de la distance entre n’importe quelle paire d’UAV en formation afin d’améliorer la localisation. L’estimateur a été entraîné à l’aide d’un ensemble de données collectées par simulation pour diverses configurations de formation en termes de vitesse, d’altitude, de tailles de formation et de modèles de perte de propagation. Le deuxième protocole traite du problème de l’adaptation de la vitesse en utilisant le principe basé sur le MARL, intégrant l’estimateur de distance basé sur le RSSI pour le calcul des distances. Les suiveurs sont formés par l’expérience collective de l’environnement pour ajuster leur vitesse de manière autonome par rapport à celle du leader lors d’un mouvement linéaire. Pour résoudre le problème d’alignement de l’orientation, un protocole de détection de direction et d’alignement de formation a été développé en utilisant le principe basé sur le MARL. Comme l’adaptateur de vitesse, ce protocole intègre également l’estimateur de distance. Les drones suiveurs sont entraînés à détecter la direction de vol du leader et à tourner comme un corps rigide autour de l’axe de lacet pour se réaligner sur le leader. Nous avons implémenté le protocole en utilisant NS3-3.35 intégré à la bibliothèque d’apprentissage automatique mlpack et évalué ses performances à travers plusieurs essais expérimentaux. Les résultats démontrent que le protocole atteint de meilleures performances en termes d’erreur estimée de distance tolérable, de stabilité de formation, d’erreur de déviation de formation et de temps de réponse accru In the context of wireless networked robotics, complex missions, such as autonomous search and rescue may require the use of multiple Unmanned Aerial Vehicles (UAVs) deployed as a formation to achieve higher efficiency and Quality of Service (QoS) while assuring mission-specific imperatives like the maximization of area covered in a single passage of the fleet over area subsections. Addressing these challenges requires an efficient localization and formation control protocol.Existing solutions proposed for low-cost localization of UAVs using Received Signal Strength Indicator (RSSI) values, were designed for indoor environments in response to the limitation of Global Navigation Satellite System (GNSS) indoors, making them not applicable to outdoor environments.Those developed to work outdoors are either unsuitable for mobile nodes or not applicable for aerial devices deployed in complex environments with varying propagation effects. Also, existing control protocols designed specifically for autonomous control of UAV-fleet do not consider the formation orientation. To cover these areas, this thesis presents an efficient learning-based formation control protocol consisting of three control mechanism to address the issue of outdoor distance estimation, speed adaptation, and formation orientation. The protocol adapts the Multi-Agent Reinforcement Learning (MARL) principle to autopilot the UAV fleet leader-follower structure using only RSSI values the UAVs receive during wireless communication with their neighbors. The followersare expected to follow the leader while maintaining the pre-configured formation leader-follower structure autonomously. First, a DNN RSSI-based distance estimator was developed to provide more accurate estimates of the distance between any pair of UAVs in formation for better localization. The estimator was trained using a dataset collected through simulation for varying formation configurations in terms of speed, altitude, formation sizes, and propagation loss models. The second protocol addresses the issue of speed adaptation using the MARL-based principle, incorporating the RSSI-based distance estimator for ranging. The followers are trained through the collective experience of the environment to autonomously adjust their speed relative to that of the leader during a linear movement.To address the orientation alignment problem, a direction detection and formation alignment protocol was developed using the MARL-based principle. Like the speed adapter, this protocol also incorporates the distance estimator. Follower UAVs are trained to detect the direction of flight of the leader and rotate as rigid body along the yaw-axis to realign themselves with the leader. We implemented the protocol using NS3-3.35 incorporated with mlpack machine learning library, and evaluated its performance using several experimental trials. The results demonstrate that the protocol achieves better, tolerable estimated distance error, formation stability, formation deviation error, and increased response time

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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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Books on the topic "Autonomous network control"

W, Protzel Peter, Palumbo Daniel L, and Langley Research Center, eds. Automatic learning rate adjustment for self-supervising autonomous robot control. National Aeronautics and Space Administration, Langley Research Center, 1992.

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Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

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Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI.  The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems.  Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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Ganchev, Ivan. Autonomous Control for a Reliable Internet of Services: Methods, Models, Approaches, Techniques, Algorithms, and Tools. Springer Nature, 2018.

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Varlamov, Oleg. Mivar databases and rules. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge".  The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems.  Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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Young, Forrest C. Phoenix autonomous underwater vehicle (AUV): Networked control of multiple analog and digital devices using LonTalk. Naval Postgraduate School, 1997.

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Tucci, Mario, and Marco Garetti, eds. Proceedings of the third International Workshop of the IFIP WG5.7. Firenze University Press, 2002. http://dx.doi.org/10.36253/88-8453-042-3.

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Contents of the papers presented at the international workshop deal with the wide variety of new and computer-based techniques for production planning and control that has become available to the scientific and industrial world in the past few years: formal modeling techniques, artificial neural networks, autonomous agent theory, genetic algorithms, chaos theory, fuzzy logic, simulated annealing, tabu search, simulation and so on. The approach, while being scientifically rigorous, is focused on the applicability to industrial environment.

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Ando, Noriaki. Simulation, Modeling, and Programming for Autonomous Robots: Second International Conference, SIMPAR 2010, Darmstadt, Germany, November 15-18, 2010. Proceedings. Springer Berlin Heidelberg, 2010.

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Guillermo, Navarro-Arribas, Cavalli Ana, Leneutre Jean, and SpringerLink (Online service), eds. Data Privacy Management and Autonomous Spontaneous Security: 5th International Workshop, DPM 2010 and 3rd International Workshop, SETOP 2010, Athens, Greece, September 23, 2010, Revised Selected Papers. Springer Berlin Heidelberg, 2011.

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1935-, Lasker G. E., International Institute for Advanced Studies in Systems Research and Cybernetics., and International Conference on Systems Research, Informatics, and Cybernetics. (10th : 1998 : Baden-Baden, Germany), eds. Advances in artificial intelligence and engineering cybernetics: Neural networks, anticipatory systems, the evolution of autonomous agents, multi-agent systems development, intelligent systems in process control, knowledge organization, formal representation of meaning, space-time logic, logic networks, time and threshold dependent logic operators, natural language processing. International Institute for Advanced Studies in Systems Research and Cybernetics, 1999.

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Chapman, Airlie. Semi-Autonomous Networks: Effective Control of Networked Systems Through Protocols, Design, and Modeling. Springer, 2015.

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Book chapters on the topic "Autonomous network control"

Bao, Jie, and Shichao Xu. "Plantwide Control via a Network of Autonomous Controllers." In Plantwide Control. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119968962.ch18.

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Schönberger, Jörn, and Herbert Kopfer. "Approaching the Application Borders of Network Capacity Control in Road Haulage." In Autonomous Cooperation and Control in Logistics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19469-6_5.

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Sekiyama, Kosuke, Katsuhiro Suzuki, Shigeru Fukunaga, and Masaaki Date. "Autonomous Synchronization Scheme Access Control for Sensor Network." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11554028_68.

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Zhang, Xiaokai, and Tianfang Yao. "A Study of Network Informal Language Using Minimal Supervision Approach." In Autonomous Systems – Self-Organization, Management, and Control. Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8889-6_18.

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Nodland, David, H. Zargarzadeh, Arpita Ghosh, and S. Jagannathan. "Neural Network-Based Optimal Control of an Unmanned Helicopter." In Advances in Intelligent and Autonomous Aerospace Systems. American Institute of Aeronautics and Astronautics, Inc., 2012. http://dx.doi.org/10.2514/5.9781600868962.0033.0058.

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Isa, Khalid, and M. R. Arshad. "Neural Network Control of Buoyancy-Driven Autonomous Underwater Glider." In Recent Advances in Robotics and Automation. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37387-9_2.

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Anwar, Mohd, Philip W. L. Fong, Xue-Dong Yang, and Howard Hamilton. "Visualizing Privacy Implications of Access Control Policies in Social Network Systems." In Data Privacy Management and Autonomous Spontaneous Security. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11207-2_9.

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Grosspietsch, Karl-Erwin, and Tanya A. Silayeva. "Modified ART Network Architectures for the Control of Autonomous Systems." In Product-Focused Software Process Improvement. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31063-8_24.

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Ming, Yan, Wang Jiaxing, Li Heqi, and Liu Kai. "Research on Direct Lift Landing Control Based on Neural Network." In Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0479-2_234.

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García, Juan Carlos, Marta Marrón, J. A. García, et al. "An Autonomous Wheelchair with a LonWorks Network based Distributed Control System." In Field and Service Robotics. Springer London, 1988. http://dx.doi.org/10.1007/978-1-4471-1273-0_61.

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Conference papers on the topic "Autonomous network control"

Martínez, Ricardo, Carlos Hernández-Chulde, Ramon Casellas, Ricard Vilalta, and Raul Muñoz. "Optimizing Energy Efficiency in Optical Transport Networks through Autonomous AI-Assisted Control Operations." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.tu2h.3.

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This work explores Deep Reinforcement Learning to develop advanced RMSA policies that support autonomous control operations, optimizing the trade-off between network performance and energy efficiency in integrated packet and elastic optical networks.

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Lee, Chanho, Junghwan Gil, and Chulsoo Kim. "Simulation Study of Autonomous Vehicle with Restricted Sensor Network for Autonomous Valet Parking System." In 2024 24th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2024. https://doi.org/10.23919/iccas63016.2024.10773285.

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Shuai, Wang, Wu Wen, Cui ZhaoJing, and Wu Yuhang. "An Autonomous Reconfiguration Method for Satellite Constellation Network Systems." In 2024 IEEE International Conference on Control Science and Systems Engineering (ICCSSE). IEEE, 2024. https://doi.org/10.1109/iccsse63803.2024.10823661.

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Hwang, Bohyun, Changbeom Shim, Seonggun Joe, and Byungkyu Kim. "A Preliminary Study on Autonomous Robotic Colonoscopy via Deep Neural Network." In 2024 13th International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, 2024. https://doi.org/10.1109/iccais63750.2024.10814455.

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Coombes, Matthew, William Eaton, Owen McAree, and Wen-Hua Chen. "Development of a generic network enabled autonomous vehicle system." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915211.

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Karimi Shahri, Pouria, Shubhankar Chintamani Shindgikar, Baisravan HomChaudhuri, and Amir H. Ghasemi. "Optimal Lane Management in Heterogeneous Traffic Network." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9040.

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Abstract This paper aims to determine an optimal allocation of autonomous vehicles in a multi-lane heterogeneous traffic network where the road is shared between autonomous and human-driven vehicles. The fundamental traffic diagram for such heterogeneous traffic networks is developed wherein the capacity of the road is determined as a function of the penetration rate and the headways of autonomous and human-driven vehicles. In this paper, we define two cost functions to maximize the throughput of the network and minimize the variation between flow rates. To solve the proposed optimization problem, an exhaustive search optimization approach is performed. Several numerical examples are presented to highlight the different influence of different design parameters on the allocation of autonomous vehicles.

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Cui, Rongxin, Chenguang Yang, Yang Li, and Sanjay Sharma. "Neural network based reinforcement learning control of autonomous underwater vehicles with control input saturation." In 2014 UKACC 10th International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915114.

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Chapman, Airlie, and Mehran Mesbahi. "Semi-autonomous networks: Network resilience and adaptive trees." In 2010 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010. http://dx.doi.org/10.1109/cdc.2010.5717850.

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Lu, Qiang, and Zhaochen Zhang. "Chaotic Autonomous Developmental Neural Network." In 2019 5th International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2019. http://dx.doi.org/10.1109/iccar.2019.8813424.

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Quader, Niamul, S. M. Masudur Rahman Al-Arif, Md Al Mamun Shaon, Kazi Khairul Islam, and Abdur Raquib Ridwan. "Control of autonomous nanorobots in neural network." In 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2011. http://dx.doi.org/10.1109/bmei.2011.6098609.

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Reports on the topic "Autonomous network control"

Pearl, Judea. Dynamic Network Techniques for Autonomous Planning and Control. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada387551.

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Hovakimyan, Naira, Hunmin Kim, Wenbin Wan, and Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, 2022. http://dx.doi.org/10.36501/0197-9191/22-016.

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Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust adaptive control. By leveraging data shared over a cloud network available to all entities, vehicles proactively adapted to new environments on the proactive level, thus coping with large-scale environmental changes. On the reactive level, control-barrier-function-based robust adaptive control with machine learning improved the performance around nominal models, providing performance and control certificates. The proposed research shaped a robust foundation for autonomous driving on cloud-connected highways of the future.

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Tzonev, Nick. PR-396-183905-R01 Autonomous System For Monitoring Pipeline River Crossings. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0012110.

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The goal of the GHZ-2-01 Project is to develop and lab-test a system for monitoring underground pipeline facilities at remote river crossings where access to power and wireline communications is not readily available. A next generation real-time river crossing monitoring solution requires an integration of various sensor types, data computation capabilities, and low power wireless connectivity which would: - utilize proven sensors technologies such as accelerometers, inclinometer strings and float-out buoys to detect dangerous conditions, - be able to recognize and minimize false alarms by examining a combination of sensors, - alarm on contact with hydrocarbons, - require minimal maintenance, - be easily scalable, both geographically and as a network, - provide seamless integration into Supervisory Control and Acquisition (SCADA) systems, and - be economical. There is a related webinar.

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Event-Triggered Adaptive Robust Control for Lateral Stability of Steer-by-Wire Vehicles with Abrupt Nonlinear Faults. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-5056.

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Because autonomous vehicles (AVs) equipped with active front steering have the features of time varying, uncertainties, high rate of fault, and high burden on the in-vehicle networks, this article studies the adaptive robust control problem for improving lateral stability in steer-by-wire (SBW) vehicles in the presence of abrupt nonlinear faults. First, an upper-level robust H∞ controller is designed to obtain the desired front-wheel steering angle for driving both the yaw rate and the sideslip angle to reach their correct values. Takagi-Sugeno (T-S) fuzzy modeling method, which has shown the extraordinary ability in coping with the issue of nonlinear, is applied to deal with the challenge of the changing longitudinal velocity. The output of the upper controller can be calculated by a parallel distributed compensation (PDC) scheme. Then an event-triggered adaptive fault-tolerant lower controller (ET-AFTC) is proposed to drive the whole SBW system driving the desired steering angle offered by the upper controller with fewer communication resources and strong robustness. By employing a backstepping technique, the tracking performance is improved. The dynamic surface control (DSC) approach is used to avoid the problem of repeated differentiations, and Nussbaum function is adopted to overcome the difficulty of unknown nonlinear control gain. Both the stability of the upper and lower controllers can be guaranteed by Lyapunov functions. Finally, the simulations of Matlab/Simulink are given to show that the proposed control strategy is effectively able to deal with the abrupt nonlinear fault via less communication resources and perform better in ensuring the yaw stability of the vehicle.

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