Artículos de revistas: "Autonomous network control"

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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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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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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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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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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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9

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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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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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 in behavior-based Al, we apply our proposed method to behavior arbitration for an autonomous mobile robot in experiments using a real robot. We also study adaptation that automatically creates an artificial immune network using reinforcement signals.

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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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13

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 dynamical network. Finally, two numerical examples are given to illustrate our theoretical analysis.

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14

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 is directly programmable. This control, was initially bound in every network devices, enabled in the network to be abstracted for applications and services. Security is a major challenge for organizational and campus networks. The future of Internet depends on virtualization which is to provide numerous networks hosted the same physical hardware. This proposal takes a great advantage of the programmability provided by SDN to utilize Intrusion Detection System.

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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 dynamic capacity allocation, flexible functional split, and dynamic slice management need to be performed in tight coordination with the autonomous capacity management of the fixed transport network infrastructure. Otherwise, the benefits of smart slice operation (i.e., cost and energy savings while ensuring per-slice service requirements) might be cancelled due to uncoordinated autonomous fixed network operation. Notably, the transport network in charge of supporting slices from the user equipment (UE) to the core expands across access and metro fixed networks. The required coordination needs to be performed while keeping the privacy of the radio and fixed network domains, which is important in multi-tenant scenarios where both network segments are managed by different operators. In this paper, we propose a novel approach that explores the concept of context-aware network operation, where the slice control anticipates the aggregated and anonymized information of the expected slice operation that is sent to the fixed network orchestrator in an asynchronous way. The context is then used as the input for the artificial intelligence (AI)-based models used by the fixed network control for the predictive capacity management of optical connections in support of RAN slices. This context-aware network operation aims at enabling accurate and reliable autonomous fixed network operation under extremely dynamic traffic originated by smart RAN operation. The exhaustive numerical results show that slice context availability improves the benchmarking fixed network predictive methods (90% reduction in prediction maximum error) remarkably in the foreseen B5G scenarios, for both access and metro segments and in heterogeneous service demand scenarios. Moreover, context-aware network operation enables robust and efficient operation of optical networks in support of dense RAN cells (>32 base stations per cell), while the benchmarking methods fail to guarantee different operational objectives.

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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 optimization for a group of PV inverters in a network of existing residential loads and autonomous inverters. The interaction of independent DOC groups in the same network is considered. The limit of PV inverter power factor is included in the control. The DOC is done by the power flow calculation and an autoregression prediction model for estimating maximum power point and loads. Overvoltage caused by prediction errors resulting in non-optimal commands from the DOC is avoided by switching to autonomous droop control (ADC). The DOC and ADC operate at different time scales to take account of communication delays between PV inverters and decentralized controller. The simulation of different scenarios of network control has proved the effectiveness of the control strategies.

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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 unlined roads, at speeds of up to 20 miles per hour.

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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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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 in the form of SDN IP, so that SDN can be connected and exchange routing information autonomously. This study focuses on the design and implementation of SDN IP using the Open Network Operating System (ONOS) on the Border Gateway Protocol (BGP). The results show that the design and implementation of SDN IP based on ONOS and BGP can be done well, where SDN can connect and exchange routing information with the Autonomous System (AS) native BGP-based network.  Key word(s): Autonomous System (AS) Border Gateway Protocol (BGP) Open Network Operating System (ONOS) Software-Defined Networking (SDN) SDN IP

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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 real-time processing of external events and the correction of errors are required for safety. Furthermore, an efficient autonomous driving system is required because it is a mobile environment with resource constraints. This study proposes neural network models as a machine-learning method for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the range data measured with the LiDAR sensor. We designed six neural network models to be evaluated according to the number of input data points. In addition, we made an autonomous vehicle based on the Raspberry Pi for driving and learning and an indoor circular driving track for collecting data and performance evaluation. Finally, we evaluated six neural network models in terms of confusion matrix, response time, battery consumption, and driving command accuracy. In addition, when neural network learning was applied, the effect of the number of inputs was confirmed in the usage of resources. The result will influence the choice of an appropriate neural network model for an indoor autonomous vehicle.

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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 classification of autonomous features, network resource management and planning, multiple access and routing protocols, and power control and energy efficiency for UAV networks. By reviewing and analyzing the UAV networking literature, it is found that AI-based UAVs are a technologically feasible and economically viable paradigm for cost-effectiveness in the design and deployment of such next-generation autonomous networks. Finally, this paper identifies open research problems in the emerging field of UAV networks. This study is expected to stimulate more research endeavors to build low-cost, energy-efficient, next-generation autonomous UAV networks.

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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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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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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 UAVs. Finally, utilizing the aforementioned edge network techniques can increase the efficiency of the overall system, enhance the video quality, maximize the usage of network resources, and save energy in multimedia communication on UAV networks.

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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 architecture combines VANETs with the new fog paradigm to enhance the connectivity and collaboration among distributed vehicles. A case study of connected cruise control (CCC) is introduced to demonstrate the efficiency of the proposed architecture and control design. Finally, we discuss some future research directions and open issues to be addressed.

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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 computer simulation show the proposed method is useful in autonomous robot locomotion control.

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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 problems that do not benefit from dimensionality reduction. Finally, we compare different feature extracting methods and we show that sequence-to-sequence learning outperforms the alternative methods considered in previous studies.

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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 technology. The current distance between the UAV and a node of the smart meter network is measured by means of the radio transmission loss value, therefore determining the position of the UAV. A two-layer network model has been proposed. One layer consists of a network of nodes in a residential area with scattered buildings – a location that is safer to navigate – while the other is an access network of nodes in a densely populated area. Then, we proposed methods to determine the direction of movement towards the next hop node on the data-link layer and the end node on the network layer, which is the target destination. We implemented a software-based test system and veriﬁed the eﬀectiveness of the proposed methods.

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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 problems such as changes in driving views and weather conditions; (2) lack of interpretability; and (3) mostly trained to learn the single driving task. To address these challenges, we propose a view-invariant spatiotemporal attentive planning and control network for autonomous vehicles. The proposed method first extracts spatiotemporal representations from images of a front and top driving view sequence through attentive Siamese 3DResNet. Then, the maximum mean discrepancy loss (MMD) is employed to minimize spatiotemporal discrepancies between these driving views and produce an invariant spatiotemporal representation, which reduces domain shift due to view change. Finally, the multitasking learning (MTL) method is employed to jointly train trajectory planning and high-level control tasks based on learned representations and previous motions. Results of extensive experimental evaluations on a large autonomous driving dataset with various weather/lighting conditions verified that the proposed method is effective for feasible motion planning and control in autonomous vehicles.

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32

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 embedded systems with 32 bit CPU and real time embedded operating system are adopted to realize real time quality and reliability of nodes which run independently. CAN field bus and message type flag are used in network communication structure to ensure the real time quality and reliability of nodes connection. Results reveal that using autonomous decentralized control in AFPM possesses not only the characteristic of real time quality, but also the online fault tolerance, which can ensure that system runs continuously even if some nodes break down. The method can avoid system destruction and improve whole system reliability.

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33

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 United States to collect and assess nonprofit suitability for funding shows significant differences between private- and public-sector control over nonprofits decisions to network. Unlike private-sector foundations, public-agency funding documents mandate awardees to network, which has practical and theoretical implications. Although the idea of building a network implies autonomous acts on the part of nonprofits, some are prone to hierarchical influences through grant-making policy.

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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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35

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 which was built over the Tactile Internet. In addition, base stations at the edge of the radio-access network (RAN) with different technologies of antennas are used in our system. Finally, simulation results show that the proposed system with multilevel clouding can significantly reduce the round-trip latency and the network congestion. In addition, our system can be adapted in the mobility scenario.

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36

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 in all the cases. The intricate dynamics of consensus issues in multi-agent-based distributed control under the influence of communication link impairments, connectivity and consensus protocol were established. Undirected communication graphs used to model the topology for agents’ connectivity is significant to addressing consensus issues of communicating agents. The paper also discusses the tradeoffs and design considerations in developing consensus strategies resilient to communication failures while optimizing performance. Simulation results show that an isolated agent in a network can achieve consensus only when there is a reference value. It was also established that communication impairments significantly degrade the performance of distributed agents in a network.

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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 optimal stabilizing neural network feedback controllers.

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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 neighboring AS networks. We prove that the proposed algorithm could achieve logicalk-connectivity on the condition that the original physical topology isk-connectivity. Simulation results validate the theoretic analysis and effectiveness of the DMTC algorithm.

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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 continuous or discrete models of the process of interest, which in general can vary in space and time. The variability of the estimated process is one metric to score future candidate sampling trajectories, along with information- and uncertainty-based metrics. Sampling tasks are allocated to the network using centralized or decentralized optimization, in order to avoid redundant measurements and observational gaps.

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40

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 well as on impossibility of their timely repair and maintenance. The authors have proposed solutions to the issue of a reliable communication channel using advanced low-energy types of angle modulation with low-value signal-to-noise ratios and the choice of the best frequency for a given time of the day for specific nodes. There is an overview of issues related to the choice of an antenna type for the autonomous radio network node, taking into account changes of working ranges. The issue of power supply to the radio network node is solved by applying the combination of a photovoltaic panel and wind generator working from a frost-resistant battery. Thermal modes for electronic equipment are provided through the use of a thermostatically controlled container. Authors have presented a possible architecture for the short-wave range radio network node. The study results include advice for developers of autonomous nodes of shortwave radio networks.

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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 perangkat melalui sebuah point of management. Telah dilakukan penelitian mengenai analisis perbandingan performansi EBGP bedasarkan parameter Quality of Service (QoS) delay, jitter, troughput dan packet loss pada arsitektur jaringan konvensional dan jaringan Software Defined Network menggunakan ONOS controller. Dari hasil pengukuran mengunakan beban traffic 7.5 MB, 10 MB dan 12.5 MB didapatkan bahwa performansi dari jaringan Software Defined Network lebih unggul dan stabil dibandingkan jaringan konvensional. Hal ini sesuai dengan data yang didapatkan dari pengujian kedua jaringan tersebut.The larger the computer network topology will make the router configuration inefficient and also required an increase in maintaining the performance of internet needs. External Border Gateway Protocol (EBGP) is one of the mechanisms contained in BGP as a liaison between Autonomous Systems where there is an exchange of information between different Autonomous Systems with a larger network scale. Software Defined Network is a concept in managing a network by separating the control plane and data plane which has the ability to manage thousands of devices through a single point of management. Research has been carried out on comparative analysis of EBGP performance based on the Quality of Service (QoS) delay, jitter, throughput and packet loss parameters on conventional network architectures and Software Defined Network networks using an ONOS controller. From the measurement results using a traffic load of 7.5 MB, 10 MB and 12.5 MB, it is found that the performance of the Software Defined Network is superior and stable compared to conventional networks. This is in accordance with the data obtained from testing the two networks.

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42

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 layout that can be assembled in a short time and installed in most modern automobiles; it can also be used as a stepping stone in the development of autonomous vehicles. Using various actuators, conventional automobiles can be converted into autonomous vehicles. In the context of motor vehicle automation, motors are often used as actuators. In addition to motors, a pneumatic system was developed to automate the predetermined steps. An autonomous vehicle's mechanical arrangement is crucial, and it must be regularly updated and built to be robust in the face of dynamic conditions. We re-implemented two additional convolutional neural networks in an effort to conduct an objective test of their proposed network and compare our system's structure, technical complexity, and performance test during autonomous driving with theirs. This predicted network is around 250 times larger than the Alex Net network and four times larger than Pilot Net after training. Although the complexity and measurement of the publication's system are lower than other models that contribute lower latency and greater speed throughout inference, the operation was claimed by our system, which achieved autonomous driving with an equivalent efficacy as that achieved with two other models. The projected deep neural system reduces the need to infer ultra-fast computational hardware. This is important for cost efficiency, scale, and cost.

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43

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 network can improve estimation speed and reduce energy consumption. A petri-based recurrent type 2 fuzzy neural network–based adaptive robust controller has been proposed for target tracking. In the offshore experiments, the proposed controller has not only realized stable position and pose control but also successfully followed mobile target on the surface. In the tank underwater experiments, the pipeline target has been successfully followed to further verify the controller performance.

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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 information, a radial basis function neural network (RBFNN) is employed to estimate the equivalent control. Further, a higher order sliding mode (HOSM) based switching control is proposed in this paper to compensate for the effect of external disturbances. The effectiveness of the proposed controller in terms of lane-keeping and lateral stability is demonstrated through simulation in a high-fidelity Carsim-Matlab Simulink environment under a variety of road and environmental conditions.

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45

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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46

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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48

Shimizu, Masaharu, Takayuki Furuta, and Ken Tomiyama. "Distributed behavior arbitration network: Autonomous behavior control architecture for humanoids." Systems and Computers in Japan 33, no. 6 (2002): 32–43. http://dx.doi.org/10.1002/scj.1130.

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49

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 flying UAVs to desired positions autonomously. In this work, we configure the quadrotor to fly autonomously by using agents (the machine learning schemes being used to fly the quadrotor autonomously) to learn about the virtual physical environment. The quadrotor will fly from an initial to a desired position. When the agent brings the quadrotor closer to the desired position, it is rewarded; otherwise, it is punished. Two reinforcement learning models, Q-learning and SARSA, and a deep learning deep Q-network network are used as agents. The simulation is conducted by integrating the robot operating system (ROS) and Gazebo, which allowed for the implementation of the learning algorithms and the physical environment, respectively. The result has shown that the Deep Q-network network with Adadelta optimizer is the best setting to fly the quadrotor from the initial to desired position.

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Brocklehurst, Callum, and Milena Radenkovic. "Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles." Journal of Sensor and Actuator Networks 11, no. 3 (2022): 35. http://dx.doi.org/10.3390/jsan11030035.

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The increased interest in autonomous vehicles has led to the development of novel networking protocols in VANETs In such a widespread safety-critical application, security is paramount to the implementation of the networks. We view new autonomous vehicle edge networks as opportunistic networks that bridge the gap between fully distributed vehicular networks based on short-range vehicle-to-vehicle communication and cellular-based infrastructure for centralized solutions. Experiments are conducted using opportunistic networking protocols to provide data to autonomous trams and buses in a smart city. Attacking vehicles enter the city aiming to disrupt the network to cause harm to the general public. In the experiments the number of vehicles and the attack length is altered to investigate the impact on the network and vehicles. Considering different measures of success as well as computation expense, measurements are taken from all nodes in the network across different lengths of attack. The data gathered from each node allow exploration into how different attacks impact metrics including the delivery probability of a message, the time taken to deliver and the computation expense to each node. The novel multidimensional analysis including geospatial elements provides evidence that the state-of-the-art MaxProp algorithm outperforms the benchmark as well as other, more complex routing protocols in most of the categories. Upon the introduction of attacking nodes however, PRoPHET provides the most reliable delivery probability when under attack. Two different attack methods (black and grey holes) are used to disrupt the flow of messages throughout the network and the more basic protocols show that they are less consistent. In some metrics, the PRoPHET algorithm performs better when under attack due to the benefit of reduced network traffic.

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