Relevant bibliographies by topics / Autonomous network control

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Academic literature on the topic 'Autonomous network control'

Author: Grafiati

Published: 31 August 2024

Last updated: 19 July 2025

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Journal articles on the topic "Autonomous network control"

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

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Internet of Things (IoT) networks are typically composed of many sensors and actuators. The operation controls for robots in smart factories or drones produce a massive volume of data that requires high reliability. A blockchain architecture can be used to build highly reliable IoT networks. The shared ledger and open data validation among users guarantee extremely high data security. However, current blockchain technology has limitations for its overall application across IoT networks. Because general permission-less blockchain networks typically target high-performance network nodes with suf

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

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

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Controlling a laparoscopic camera during robotic surgery represents a multifaceted challenge, demanding considerable physical and cognitive exertion from operators. While manual control presents the advantage of enabling optimal viewing angles, it is offset by its taxing nature. In contrast, current autonomous camera systems offer predictability in tool tracking but are often rigid, lacking the adaptability of human operators. This research investigates the potential of two distinct network architectures: a dense neural network (DNN) and a recurrent network (RNN), both trained using a diverse

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

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In order to improve the autonomy of a maneuvered GEO satellite which is a member of a navigation satellite system, an integrated design method of autonomous orbit determination and autonomous control was proposed. A neural network state observer was designed to estimate the state of the GEO satellite, with only the intersatellite ranging information as observations. The controller is determined autonomously by another neural network based on the estimated state and the preset correction trajectory. A gradient descent learning method with a forgetting factor was used to derive the weight updati

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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-eff

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

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Dissertations / Theses on the topic "Autonomous network control"

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

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In this dissertation, we present novel solutions to cooperative control of autonomous multi-agent network topologies pertaining to the area of hostile target tracking by multiple unmanned aerial vehicles (UAVs). The present work assumes an undirected graph comprising point-mass UAVs with time-varying communication topology among agents. The level of information sharing ability among agents in a multi-agent network, i.e. the network connectivity, plays pivotal role in group dynamics. A neighborhood information based decentralized controller is proposed in order to drive UAVs into a s

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

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

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

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A big challenge regarding the development of autonomous vehicles is how to handle complex situations. If an autonomous vehicle ends up in a situation where it cannot make a decision on its own it will cause the car to stop, unable to continue driving. For these situations, human intervention is required. By making it possible to control the car remotely there is no need for an actual human in the car. Instead, a human operator can remotely control one or several cars from a distance. The purpose of this project was to identify such complex situations, evaluate remote control options and implem

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

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This thesis makes a number of new contributions to control and sensing for unmanned vehicles. I begin by developing a non-linear simulation of a small unmanned helicopter and then proceed to develop new algorithms for control and sensing using the simulation. The work is field-tested in successful flight trials of biologically inspired vision and neural network control for an unstable rotorcraft. The techniques are more robust and more easily implemented on a small flying vehicle than previously attempted methods.¶ Experiments from biology suggest that the sensing of image motion or optic flo

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

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

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

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Through the work of researchers and the development of commercially availableproducts, automated guidance has become a viable option for agricultural producers.Some of the limitations of commercially available technologies are that they onlyautomate one function of the agricultural vehicle and that the systems are proprietary toa single machine model.The objective of this project was to evaluate a controller area network (CAN bus)as the basis of an automated guidance system. The prototype system utilized severalmicrocontroller-driven nodes to act as control points along a system wide CAN bus.M

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

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This thesis presents the development of small, inexpensive unmanned aerial vehicles (UAVs) to achieve autonomous fight. Fixed wing hobby model planes are modified and instrumented to form experimental platforms. Different sensors employed to collect the flight data are discussed along with their calibrations. The time constant and delay for the servo-actuators for the platform are estimated. Two different data collection and processing units based on micro-controller and PC104 architectures are developed and discussed. These units are also used to program the identification and control

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

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Dans le contexte de la robotique en réseau sans fil, des missions complexes, telles que la recherche et le sauvetage autonomes, peuvent nécessiter l’utilisation de plusieurs véhicules aériens sans pilote (UAV) déployés en formation afin d’augmenter l’efficacité et la qualité de service (QoS) tout en garantissant des impératifs spécifiques à la mission, tels que la maximisation de la couverture d’une zone en un seul passage de la flotte sur des sous-sections de celle-ci. Pour relever ces défis, un protocole efficace de localisation et de contrôle de formation est nécessaire.Les solutions exista

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

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

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Books on the topic "Autonomous network control"

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

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

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Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI.  The textbook in the field of training "Computer Science and Computer Engineering"

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

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

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in th

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

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

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

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

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

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

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

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Book chapters on the topic "Autonomous network control"

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

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

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

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

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

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

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

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

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

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

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Conference papers on the topic "Autonomous network control"

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

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

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

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

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

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

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

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Abstract This paper aims to determine an optimal allocation of autonomous vehicles in a multi-lane heterogeneous traffic network where the road is shared between autonomous and human-driven vehicles. The fundamental traffic diagram for such heterogeneous traffic networks is developed wherein the capacity of the road is determined as a function of the penetration rate and the headways of autonomous and human-driven vehicles. In this paper, we define two cost functions to maximize the throughput of the network and minimize the variation between flow rates. To solve the proposed optimization prob

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

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

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

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

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Reports on the topic "Autonomous network control"

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

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

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Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust

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

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The goal of the GHZ-2-01 Project is to develop and lab-test a system for monitoring underground pipeline facilities at remote river crossings where access to power and wireline communications is not readily available. A next generation real-time river crossing monitoring solution requires an integration of various sensor types, data computation capabilities, and low power wireless connectivity which would: - utilize proven sensors technologies such as accelerometers, inclinometer strings and float-out buoys to detect dangerous conditions, - be able to recognize and minimize false alarms by exa

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

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Because autonomous vehicles (AVs) equipped with active front steering have the features of time varying, uncertainties, high rate of fault, and high burden on the in-vehicle networks, this article studies the adaptive robust control problem for improving lateral stability in steer-by-wire (SBW) vehicles in the presence of abrupt nonlinear faults. First, an upper-level robust H∞ controller is designed to obtain the desired front-wheel steering angle for driving both the yaw rate and the sideslip angle to reach their correct values. Takagi-Sugeno (T-S) fuzzy modeling method, which has shown the

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