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Artículos de revistas sobre el tema "Autonomous network control"

Autor: Grafiati

Publicado: 31 de agosto de 2024

Última modificación: 31 de julio de 2025

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1

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 suf

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2

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 av

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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, an

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4

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 t

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5

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

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6

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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7

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

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8

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 updati

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9

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-eff

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10

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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11

Nagarjuna Reddy, Tella, and K. Annapurani Panaiyappan. "Intrusion Detection on Software Defined Networking." International Journal of Engineering & Technology 7, no. 3.12 (2018): 330. http://dx.doi.org/10.14419/ijet.v7i3.12.16052.

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Software Defined Networking and programmability on network have established themselves as current trends in IT by bringing autonomous operation with dynamic flow to network. Networks must be programmable, and it must be aware of the application in order to operate autonomously. Networks need to evolve to catch up with the current trends without losing their current status and operation, reliability, robustness, or security, and without distorting current investments. SDN is a transpiring network architecture where network control plane is distinguished from data plane and by that the network i

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12

Yang, Chao, Lihong Huang, and Fangmin Li. "Exponential Synchronization Control of Discontinuous Nonautonomous Networks and Autonomous Coupled Networks." Complexity 2018 (October 17, 2018): 1–10. http://dx.doi.org/10.1155/2018/6164786.

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This paper concerns complex delayed neural networks with discontinuous activations. Based on the framework of differential inclusion theory, we design two novel controllers by regulating a parameter σ 0≤σ<1 which covers both discontinuous and continuous controllers. Then, we investigate a nonautonomous cellular neural network system and autonomous neural network with linear coupling, respectively. By choosing a time-dependent Lyapunov-Krasovskii functional candidate and suitable controllers, some criteria are studied to guarantee the exponential synchronization of the complex delayed dynami

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13

Fujii, Teruo, and Tamaki Ura. "Control with Neural Network For Autonomous Underwater Vehicle." Journal of the Society of Naval Architects of Japan 1989, no. 166 (1989): 503–11. http://dx.doi.org/10.2534/jjasnaoe1968.1989.166_503.

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14

Watanabe, Yuji, Akio Ishiguro, and Yoshiki Uchikawa. "Autonomous Mobile Robot Behavior Control Using Immune Network." Journal of Robotics and Mechatronics 10, no. 4 (1998): 326–32. http://dx.doi.org/10.20965/jrm.1998.p0326.

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Attention has been increasingly focused on behaviorbased artificial intelligence (Al) due to its potential robustness and flexibility toward a dynamically changing world. This approach has yet unsolved problems: (1) how to construct an arbitration mechanism and (2) how to prepare competence modules (simple behavior/action). Biological information processing systems are interesting viewed from an engineering standpoint. Of these, we particularly have focused on the immune system, constructing a decentralized consensus-maker inspired by the immune network hypothesis. To solve the above problems

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15

AHMAD, H. F., H. SUGURI, M. Q. CHOUDHARY, A. HASSAN, A. LIAQAT, and M. U. KHAN. "Autonomous Distributed Congestion Control Scheme in WCDMA Network." IEICE Transactions on Information and Systems E91-D, no. 9 (2008): 2267–75. http://dx.doi.org/10.1093/ietisy/e91-d.9.2267.

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16

Wang, Shaoxuan, Marc Ruiz, and Luis Velasco. "Context-Based e2e Autonomous Operation in B5G Networks." Sensors 24, no. 5 (2024): 1625. http://dx.doi.org/10.3390/s24051625.

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The research and innovation related to fifth-generation (5G) networks that has been carried out in recent years has decided on the fundamentals of the smart slice in radio access networks (RANs), as well as the autonomous fixed network operation. One of the most challenging objectives of beyond 5G (B5G) and sixth-generation (6G) networks is the deployment of mechanisms that enable smart end-to-end (e2e) network operation, which is required for the achievement of the stringent service requirements of the envisioned use cases to be supported in the short term. Therefore, smart actions, such as d

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17

Phan-Tan, Chi-Thang, and Martin Hill. "Decentralized Optimal Control for Photovoltaic Systems Using Prediction in the Distribution Systems." Energies 14, no. 13 (2021): 3973. http://dx.doi.org/10.3390/en14133973.

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The high penetration of photovoltaic (PV) systems and fast communications networks increase the potential for PV inverters to support the stability and performance of microgrids. PV inverters in the distribution network can work cooperatively and follow centralized and decentralized control commands to optimize energy production while meeting grid code requirements. However, there are older autonomous inverters that have already been installed and will operate in the same network as smart controllable ones. This paper proposes a decentralized optimal control (DOC) that performs multi-objective

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18

Pomerleau, Dean A. "Efficient Training of Artificial Neural Networks for Autonomous Navigation." Neural Computation 3, no. 1 (1991): 88–97. http://dx.doi.org/10.1162/neco.1991.3.1.88.

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The ALVINN (Autonomous Land Vehicle In a Neural Network) project addresses the problem of training artificial neural networks in real time to perform difficult perception tasks. ALVINN is a backpropagation network designed to drive the CMU Navlab, a modified Chevy van. This paper describes the training techniques that allow ALVINN to learn in under 5 minutes to autonomously control the Navlab by watching the reactions of a human driver. Using these techniques, ALVINN has been trained to drive in a variety of circumstances including single-lane paved and unpaved roads, and multilane lined and u

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19

Pratama, I. Putu Agus Eka. "Design and Implementation of SDN IP Based on Open Network Operating System and Border Gateway Protocol." Bulletin of Computer Science and Electrical Engineering 2, no. 2 (2021): 56–66. http://dx.doi.org/10.25008/bcsee.v2i2.1145.

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The development of computer network technology in the form of Software Defined Networking (SDN), provides many facilities for users to be able to develop network control applications, which can separate the data plane function from the control plane. The existence of this separation on routers and switches makes it easy for developers to centrally develop software and devices according to what is needed by users. However, there were obstacles to implementing SDN on IP networks in a short time. For this reason, it is necessary to implement SDN in stages by adding SDN to the existing IP network

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20

Zhao, Jing, Zhao Lin Han, and Yuan Yuan Fang. "Fuzzy Neural Network Hybrid Learning Control on AUV." Advanced Materials Research 468-471 (February 2012): 1732–35. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.1732.

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A novel controller based on the fuzzy B-spline neural network is presented, which combines the advantages of qualitative defining capability of fuzzy logic, quantitative learning ability of neural networks and excellent local controlling ability of B-spline basis functions, which are being used as fuzzy functions. A hybrid learning algorithm of the controller is proposed as well. The results show that it is feasible to design the fuzzy neural network control of autonomous underwater vehicle by the hybrid learning algorithm.

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21

Kwon, Yonghun, Woojae Kim, and Inbum Jung. "Neural Network Models for Driving Control of Indoor Autonomous Vehicles in Mobile Edge Computing." Sensors 23, no. 5 (2023): 2575. http://dx.doi.org/10.3390/s23052575.

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Mobile edge computing has been proposed as a solution for solving the latency problem of traditional cloud computing. In particular, mobile edge computing is needed in areas such as autonomous driving, which requires large amounts of data to be processed without latency for safety. Indoor autonomous driving is attracting attention as one of the mobile edge computing services. Furthermore, it relies on its sensors for location recognition because indoor autonomous driving cannot use a GPS device, as is the case with outdoor driving. However, while the autonomous vehicle is being driven, the rea

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22

Sarkar, Nurul I., and Sonia Gul. "Artificial Intelligence-Based Autonomous UAV Networks: A Survey." Drones 7, no. 5 (2023): 322. http://dx.doi.org/10.3390/drones7050322.

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Recent advancements in unmanned aerial vehicles (UAVs) have proven UAVs to be an inevitable part of future networking and communications systems. While many researchers have proposed UAV-assisted solutions for improving traditional network performance by extending coverage and capacity, an in-depth study on aspects of artificial intelligence-based autonomous UAV network design has not been fully explored yet. The objective of this paper is to present a comprehensive survey of AI-based autonomous UAV networks. A careful survey was conducted of more than 100 articles on UAVs focusing on the clas

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23

Tanaka, Takayuki, Kazuo Yamafuji, and Hidenori Takahashi. "Development of the Intelligent Mobile Robot for Service Use Report 1: Environmental-Adjustable Autonomous Locomotion Control System." Journal of Robotics and Mechatronics 9, no. 4 (1997): 275–82. http://dx.doi.org/10.20965/jrm.1997.p0275.

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We have developed an intelligent mobile robot for use as an office “secretary/ helper” by day and “security maintenance guard” by night. The robot’s autonomous locomotion control system (ALCS) plans its paths, recognizes absolute positions and learns navigation control. To aid the robot in moving more appropriately and smoothly among human beings and obstacles in an office environment, we studied learning by a fuzzy neural network that tunes membership functions for fuzzy locomotion control, i.e., the intelligent robot learns to move autonomously through its surroundings. Results obtained by c

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24

Sarvi, Batoul. "Multimedia communications for autonomous drones." Boolean 2022 VI, no. 1 (2022): 52–58. http://dx.doi.org/10.33178/boolean.2022.1.9.

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In recent years, there has been significant growth in multimedia communication on drones. The first thing that comes to every researcher’s mind is what requirements are for multimedia communication to be acceptable for existing scenarios on UAVs? Because of the noisy wireless channel and long distance between UAVs, providing reliable and real-time multimedia communications on UAVs stands at the top of the requirements list. To the best of our knowledge, mobile edge computing and cross-layer error control have significant possibilities to provide a better quality of multimedia communication on

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25

Wang, Zhuwei, Yuehui Guo, Yu Gao, Chao Fang, Meng Li, and Yang Sun. "Fog-Based Distributed Networked Control for Connected Autonomous Vehicles." Wireless Communications and Mobile Computing 2020 (November 3, 2020): 1–11. http://dx.doi.org/10.1155/2020/8855655.

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With the rapid developments of wireless communication and increasing number of connected vehicles, Vehicular Ad Hoc Networks (VANETs) enable cyberinteractions in the physical transportation system. Future networks require real-time control capability to support delay-sensitive application such as connected autonomous vehicles. In recent years, fog computing becomes an emerging technology to deal with the insufficiency in traditional cloud computing. In this paper, a fog-based distributed network control design is proposed toward connected and automated vehicle application. The proposed archite

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Ueda, Kiyoshi, and Takumi Miyoshi. "Autonomous Navigation Control of UAV Using Wireless Smart Meter Devices." Journal of Telecommunications and Information Technology 2 (June 28, 2019): 64–72. http://dx.doi.org/10.26636/jtit.2019.132319.

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In preparation for the upcoming home delivery services that rely on Unmanned Aerial Vehicles (UAVs), we developed a new multi-hop radio network that is laid over a smart meter network transferring electric energy information only. In this network, a UAV follows, for navigation purposes, the topology of a virtual network overlaid on the physical smart meter network. We established a service management control method which does not rely on image analysis or map information processing, i.e. processes that consume precious power resources of the UAV. Instead, navigation is based on the routing tec

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27

Milano, Nicola, and Stefano Nolfi. "Autonomous learning of features for control: Experiments with embodied and situated agents." PLOS ONE 16, no. 4 (2021): e0250040. http://dx.doi.org/10.1371/journal.pone.0250040.

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The efficacy of evolutionary or reinforcement learning algorithms for continuous control optimization can be enhanced by including an additional neural network dedicated to features extraction trained through self-supervision. In this paper we introduce a method that permits to continue the training of the features extracting network during the training of the control network. We demonstrate that the parallel training of the two networks is crucial in the case of agents that operate on the basis of egocentric observations and that the extraction of features provides an advantage also in proble

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M. J. Darr, T. S. Stombaugh, and S. A. Shearer. "CONTROLLER AREA NETWORK BASED DISTRIBUTED CONTROL FOR AUTONOMOUS VEHICLES." Transactions of the ASAE 48, no. 2 (2005): 479–90. http://dx.doi.org/10.13031/2013.18312.

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Plebe, Alice, Mauro Da Lio, and Daniele Bortoluzzi. "On Reliable Neural Network Sensorimotor Control in Autonomous Vehicles." IEEE Transactions on Intelligent Transportation Systems 21, no. 2 (2020): 711–22. http://dx.doi.org/10.1109/tits.2019.2896375.

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Gachet, D., J. R. Pimentel, L. Moreno, M. A. Salichs, and V. Fernandez. "Neural Network Approaches for Behavioral Control of Autonomous Systems." IFAC Proceedings Volumes 26, no. 1 (1993): 330–34. http://dx.doi.org/10.1016/s1474-6670(17)49321-9.

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31

Wang, Jingyu, Lei Zhang, Yiran Yang, et al. "Network Meets ChatGPT: Intent Autonomous Management, Control and Operation." Journal of Communications and Information Networks 8, no. 3 (2023): 239–55. http://dx.doi.org/10.23919/jcin.2023.10272352.

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32

Ayalew, Melese, Shijie Zhou, Imran Memon, Md Belal Bin Heyat, Faijan Akhtar, and Xiaojuan Zhang. "View-Invariant Spatiotemporal Attentive Motion Planning and Control Network for Autonomous Vehicles." Machines 10, no. 12 (2022): 1193. http://dx.doi.org/10.3390/machines10121193.

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Autonomous driving vehicles (ADVs) are sleeping giant intelligent machines that perceive their environment and make driving decisions. Most existing ADSs are built as hand-engineered perception-planning-control pipelines. However, designing generalized handcrafted rules for autonomous driving in an urban environment is complex. An alternative approach is imitation learning (IL) from human driving demonstrations. However, most previous studies on IL for autonomous driving face several critical challenges: (1) poor generalization ability toward the unseen environment due to distribution shift pr

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33

Xu, J. L., Shi Ming Ji, and Z. P. Fang. "Autonomous Control of Abrasive Flow Precision Machining." Advanced Materials Research 215 (March 2011): 384–88. http://dx.doi.org/10.4028/www.scientific.net/amr.215.384.

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Autonomous nodes distribution, including their relations, real time quality and network communication in abrasive flow precision machining (AFPM) was studied. Autonomous nodes are arranged according to their function. Control program of motor speed is copied into liquid level nodes, pressure nodes, temperature nodes, flux nodes and input/output nodes. If nodes failure has happened, competition algorithm is enabled automatically to select a new node to run important program. Reliability of autonomous control system can be calculated utilizing the formula derived from probability theory. ARM 9 e

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34

Doerfel, Marya L., Yannick Atouba, and Jack L. Harris. "(Un)Obtrusive Control in Emergent Networks: Examining Funding Agencies’ Control Over Nonprofit Networks." Nonprofit and Voluntary Sector Quarterly 46, no. 3 (2016): 469–87. http://dx.doi.org/10.1177/0899764016664588.

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Nonprofit sector organizations tackle intractable problems by seeking support from external funding agencies, resulting in funders holding power through resource control. Nonprofits also access resources and coordinate activities through building networks with other nonprofits. Such networks have been viewed as emergent with an underlying assumption that the nonprofits determine when and with whom to partner. Given the power of funders, however, how much control do the nonprofits have in determining whether or not to partner? Document analysis of 83 application packets used by funders in the U

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Khayyat, Mashael, Abdullah Alshahrani, Soltan Alharbi, Ibrahim Elgendy, Alexander Paramonov, and Andrey Koucheryavy. "Multilevel Service-Provisioning-Based Autonomous Vehicle Applications." Sustainability 12, no. 6 (2020): 2497. http://dx.doi.org/10.3390/su12062497.

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With the recent advances and development of autonomous control systems of cars, the design and development of reliable infrastructure and communication networks become a necessity. The recent release of the fifth-generation cellular system (5G) promises to provide a step towards reliability or a panacea. However, designing autonomous vehicle networks has more requirements due to the high mobility and traffic density of such networks and the latency and reliability requirements of applications run over such networks. To this end, we proposed a multilevel cloud system for autonomous vehicles whi

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Akinwale, O. S., D. F. Mojisola, and P. A. Adediran. "Consensus issues in multi-agent-based distributed control with communication link impairments." Nigerian Journal of Technological Development 21, no. 1 (2024): 85–93. http://dx.doi.org/10.4314/njtd.v21i1.2212.

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In multi-agent systems, achieving consensus among autonomous agents is a fundamental problem with wide-ranging applications, from autonomous robotics to distributed sensor networks. However, the real-world deployment of such systems often involves communication links prone to impairments, including packet loss, delays, and network congestion. These communication challenges present formidable obstacles to achieving consensus reliably and efficiently. In this paper, consensus protocols were introduced for network with and without communication impairments and convergence analysis were provided i

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Devaraju, Shreyas, Shivam Garg, Alexander Ihler, Elizabeth Serena Bentley, and Sunil Kumar. "Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks." Drones 9, no. 2 (2025): 140. https://doi.org/10.3390/drones9020140.

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This paper considers a decentralized and autonomous wireless network of low SWaP (size, weight, and power) fixed-wing UAVs (unmanned aerial vehicles) used for remote exploration and monitoring of targets in an inaccessible area lacking communication infrastructure. Here, the UAVs collaborate to find target(s) and use routing protocols to forward the sensed data of target(s) to an aerial base station (BS) in real-time through multihop communication, which can then transmit the data to a control center. However, the unpredictability of target locations and the highly dynamic nature of autonomous

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Kunisch, Karl, and Daniel Walter. "Semiglobal optimal feedback stabilization of autonomous systems via deep neural network approximation." ESAIM: Control, Optimisation and Calculus of Variations 27 (2021): 16. http://dx.doi.org/10.1051/cocv/2021009.

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A learning approach for optimal feedback gains for nonlinear continuous time control systems is proposed and analysed. The goal is to establish a rigorous framework for computing approximating optimal feedback gains using neural networks. The approach rests on two main ingredients. First, an optimal control formulation involving an ensemble of trajectories with ‘control’ variables given by the feedback gain functions. Second, an approximation to the feedback functions via realizations of neural networks. Based on universal approximation properties we prove the existence and convergence of opti

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Zhang, Wei, Gengxin Zhang, Liang Gou, Bo Kong, and Dongming Bian. "Delay Minimization Topology Control in Planetary Surface Network: An Autonomous Systems Approach." International Journal of Distributed Sensor Networks 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/726274.

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This paper investigates the topology control problem in the planetary surface network (PSN) of Interplanetary Internet (IPN) using an autonomous system (AS) approach. We propose a delay minimization topology control (DMTC) algorithm to achieve low time delay and strong connectivity in the planetary surface network. Compared with the most existing approaches where either the purely centralized or the purely distributed control method is adopted, the proposed algorithm is a hybrid control method. In order to reduce the cost of control, the control message exchange is constrained among neighborin

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Sorooshyari, Siamak, and Zoran Gajic. "Autonomous Dynamic Power Control for Wireless Networks: User-Centric and Network-Centric Consideration." IEEE Transactions on Wireless Communications 7, no. 3 (2008): 1004–15. http://dx.doi.org/10.1109/twc.2008.060731.

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Mahdiyah, Lu'lu' Hasna', Jafaruddin Gusti Amri Ginting, and Nanda Iryani. "Analisis Perbandingan Performansi Eksternal Border Gateway Protocol (EBGP) pada Jaringan Konvensional dan Jaringan Software Defined Network." RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) 4, no. 2 (2021): 147. http://dx.doi.org/10.24853/resistor.4.2.147-154.

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Topologi jaringan komputer yang semakin besar akan membuat konfigurasi router menjadi tidak efisien dan juga dibutuhkan peningkatan dalam mempertahankan kinerja kebutuhan internet. Eksternal Border Gateway Protocol (EBGP) salah satu mekanisme yang terdapat pada BGP sebagai penghubung antar Autonomous System dimana adanya pertukaran informasi antar Autonomous System yang berbeda dengan skala network yang lebih besar. Software Defined Network sebuah konsep dalam mengelola jaringan dengan melakukan pemisahan antara control plane dan data plane yang memiliki kemampuan untuk mengatur ribuan perangk

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Paley, Derek A., and Artur Wolek. "Mobile Sensor Networks and Control: Adaptive Sampling of Spatiotemporal Processes." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 91–114. http://dx.doi.org/10.1146/annurev-control-073119-090634.

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The control of mobile sensor networks uses sensor measurements to update a model of an unknown or estimated process, which in turn guides the collection of subsequent measurements—a feedback control framework called adaptive sampling. Applications for adaptive sampling exist in a wide range of settings, especially for unmanned or autonomous vehicles that can be deployed cheaply and in cooperative groups. The dynamics of mobile sensor platforms are often simplified to planar self-propelled particles subject to the ambient flow of the surrounding fluid. Sensor measurements are assimilated into c

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Hai, Huang, Zhang Guocheng, Qing Hongde, and Zhou Zexing. "Autonomous underwater vehicle precise motion control for target following with model uncertainty." International Journal of Advanced Robotic Systems 14, no. 4 (2017): 172988141771980. http://dx.doi.org/10.1177/1729881417719808.

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Target following plays an important role in oceanic detection and target capturing for autonomous underwater vehicles. Due to the model nonlinearity and external disturbance, the dynamic model of a portable autonomous underwater vehicle was usually established with parameter uncertainties. In this article, a petri-based recurrent type 2 fuzzy neural network has been built to approximate the unknown autonomous underwater vehicle dynamics. The type 2 fuzzy logic system has been applied to the network to improve the approximation accuracy for systematic nonlinearity, and the petri layer in the ne

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44

Kadeem, Sahar R. Abdul, Ali Naser, Ahmed R. Hassan, and Ghufran Abbas Betti. "Artificial Neural Network-Powered, Driverless Vehicle Concept Development." AlKadhum Journal of Science 1, no. 2 (2023): 17–31. http://dx.doi.org/10.61710/akjs.v1i2.63.

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Autonomous cars are now possible due to significant advances in robotics and intelligent control systems. Before these vehicles can safely operate in traffic and other hostile environments, there are many navigation, vision, and control issues. We want techniques that are both cost-effective and efficient, so that the field of research and academia may fully embrace self-driving cars. Within this scenario, we need something that can convert people to autonomous automobiles and include existing vehicles so that academics and explorers can access them. This study proposes a flexible mechanical l

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Kiba, D. A., A. S. Gudim, N. N. Liubushkina, and S. G. Marushchenko. "Autonomous node in the short wave radio network." Radio industry (Russia) 29, no. 3 (2019): 26–32. http://dx.doi.org/10.21778/2413-9599-2019-29-3-26-32.

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The paper covers questions related to the creation of the comprehensive device designed for long-term autonomous operations as part of the radio networks for various purposes in short-wave bands. Such radio networks are intended for remote monitoring and control over facilities at distances of hundreds and thousands of kilometres. The device and features of functioning of autonomous nodes in short-wave radio networks are based on daily and seasonal characteristics of distribution of radio waves, operation under conditions of self-provision with electricity and in areas of harsh climate, as wel

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Jembre, Yalew Zelalem, Yuniarto Wimbo Nugroho, Muhammad Toaha Raza Khan, et al. "Evaluation of Reinforcement and Deep Learning Algorithms in Controlling Unmanned Aerial Vehicles." Applied Sciences 11, no. 16 (2021): 7240. http://dx.doi.org/10.3390/app11167240.

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Unmanned Aerial Vehicles (UAVs) are abundantly becoming a part of society, which is a trend that is expected to grow even further. The quadrotor is one of the drone technologies that is applicable in many sectors and in both military and civilian activities, with some applications requiring autonomous flight. However, stability, path planning, and control remain significant challenges in autonomous quadrotor flights. Traditional control algorithms, such as proportional-integral-derivative (PID), have deficiencies, especially in tuning. Recently, machine learning has received great attention in

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Swain, Subrat Kumar, Jagat J. Rath, and Kalyana C. Veluvolu. "Neural Network Based Robust Lateral Control for an Autonomous Vehicle." Electronics 10, no. 4 (2021): 510. http://dx.doi.org/10.3390/electronics10040510.

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The lateral motion of an Automated Vehicle (AV) is highly affected by the model’s uncertainties and unknown external disturbances during its navigation in adverse environmental conditions. Among the variety of controllers, the sliding mode controller (SMC), known for its robustness towards disturbances, is considered to generate a robust control signal under uncertainties. However, conventional SMC suffers from the issue of high frequency oscillations, called chattering. To address the issue of chattering and reduce the effect of unknown external disturbances in the absence of precise model in

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Bamgbose, Samuel Oludare, and Xiangfang Li, Lijun Qian. "Trajectory Tracking Control Optimization with Neural Network for Autonomous Vehicles." Advances in Science, Technology and Engineering Systems Journal 4, no. 1 (2019): 217–24. http://dx.doi.org/10.25046/aj040121.

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Luan, Zhongkai, Jinning Zhang, Wanzhong Zhao, and Chunyan Wang. "Trajectory Tracking Control of Autonomous Vehicle With Random Network Delay." IEEE Transactions on Vehicular Technology 69, no. 8 (2020): 8140–50. http://dx.doi.org/10.1109/tvt.2020.2995408.

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Takayama, Satoshi, and Atsushi Ishigame. "Autonomous Decentralized Control of Distribution Network Voltage using Reinforcement Learning." IFAC-PapersOnLine 51, no. 28 (2018): 209–14. http://dx.doi.org/10.1016/j.ifacol.2018.11.703.

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