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Simulation of Communication network Latency Effects on Vehicle Teleoperation 26

Simulation of Communication Network Latency Effects on Vehic...

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IEEE 26th International conference on Intelligent Transportation systems (ITSC)

作者： Brueckner, Christoph Patino-Studencki, Lucila Nan, Tianxiang Bueyuekoglu, Atakan Heyn, Thomas Fischer, Michael Coburg Univ Appl Sci Fac Mech Engn Coburg Germany Fraunhofer Inst Integrated Circuits IIS Erlangen Germany Valeo Schalter & Sensoren GmbH Wemding Germany

ISBN: (纸本)9798350399462

Teleoperation is a fundamental feature for the development of autonomous vehicles. Not only offers it the possibility of an efficient fallback solution for emerging situations that the autonomous vehicle cannot resolve itself but also it potentially offers new business models for transportation service providers. Fundamental to these considerations, however, is the performance of the communication network and the resulting parameters such as latency and bandwidth. These quantities have a critical impact on the ability of a human to perceive the environment, assess the situation and remotely control the vehicle safely and reliably. In this paper, a framework for simulating and evaluating the impact of network parameters on the performance of a remote human driver is presented. Using an example scenario of a shuttle bus route, the effects of latency for the teleoperation use case is evaluated. The local network and the impact on the human driver are modeled in SUMO (Simulation of Urban MObility) and OMNEST. The latency network simulation is validated against real measurement data. The car-following model Extended Intelligent Driver Model (EIDM) and its human driver model are extended so that latency effects can be quantified. The proposed approach has multiple advantages, on the one hand, it allows to evaluate the network suitability for the teleoperation use case. On the other hand, it limit the number of variables to be evaluated in e.g. user studies.

关键词： autonomous driving latency human-model simulation sumo car-following-model teleoperation remote driving

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A Reinforcement Learning Method in Cooperative Multi-Agent System for Production control System 2

A Reinforcement Learning Method in Cooperative Multi-Agent S...

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2nd IEEE International conference on Data Science and network Security, ICDSNS 2024

作者： Malathy, V. Al-Jawahry, Hassan M. Madhura, G.K. Suganya, G. Rashmi, P. Sr University Department of Ece Warangal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Dr. Sns Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore India Inventuriz Labs Private Ltd Bengaluru India

ISBN: (纸本)9798350373110

Nowadays, in various domains including distributed control, telecommunication, robotics and economics to address the problems, Multi-Agent systems (MAS) is used. To solve with preprogrammed agent behavior complexity of many tasks of these domains making it difficult. The multi- agent systems can limit problem of control in complex system production, to solve more efficiently. However, local optimization tendencies are often shown in iterative development algorithms. So, by using reinforcement learning and considering indicators of global key performance this paper presents a new method for cooperative multi-agent system. To cooperative order agents, for the purpose of this a central deep and double deep learning module its knowledge is being transferred. From the results, subsequent reinforcement learning using memory, the order's experience is stored and increases 50% of average mean cycle. In comparison of production control conventional methods, nonlinear production system and parameters of learning interdependencies between the characteristics are investigated and the performance is evaluated. © 2024 IEEE.

关键词： Deep reinforcement learning

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Prioritized Phase Split Optimization for Coordinated Traffic Signal control in Urban network Using Deep Reinforcement Learning 26

Prioritized Phase Split Optimization for Coordinated Traffic...

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IEEE 26th International conference on Intelligent Transportation systems (ITSC)

作者： Kim, Hyunsoo Jin, Zhixiong Tak, Hyeyoung Yu, Hwapyeong Yeo, Hwasoo Korea Adv Inst Sci & Technol Dept Civil & Environm Engn Daejeon South Korea Univ Gustave Eiffel ENTPE LICIT ECO7 Lyon France Urban Transport Syst Lab LUTS Ecole Polytech Fed Lausann EPFL Ecublens Switzerland

ISBN: (纸本)9798350399462

In traffic signal control (TSC), Deep Reinforcement Learning (DRL) has demonstrated its superiority to traditional approaches. However, there remain two challenges in DRL-based traffic signal control. How to cooperatively control decentralized intersection agents and how to apply TSC methods to the real world are still open. To address the aforementioned challenges, we propose a novel DRL-based strategy that optimizes the phase split for each intersection. We prioritize each intersection for signal control and adjust its phase split in accordance with its assigned priority. To be more specific, we start by optimizing the phase split of the intersection with the highest priority and then control the intersection with the following priority based on the outcome from the first intersection. In this manner, the coordinated optimization is taken into account. Additionally, we maintain the pre-defined order and definition of phases, and a fixed cycle time during the optimization process to support practical application. The proposed method yields an improvement in the network efficiency, ranging from 8-16%, compared to the traditional method when tested in real urban network scenarios. This study also tests the impact of the control priority on the model performance.

关键词： Reinforcement learning

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Application of FACTS Devices for Transient Stability Enhancement in the Adama ii Grid-Connected Wind Farm 10th

Application of FACTS Devices for Transient Stability Enhance...

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10th EAI International conference on Advancements of Science and Technology, EAI ICAST 2022

作者： Jember, Yosef Berhan Workineh, Tewodros Gera Yetayew, Tefera T. Dinkayow, Meresa Bahir Dar Ethiopia Adama Science and Technology University Adama Ethiopia

ISBN: (纸本)9783031411724

The fluctuating wind speed affects the power output of WECS (Wind Energy Conversion systems), which might lead to problems with power quality when it is integrated into the grid network. Numerous difficulties have emerged as a result of the tremendous proliferation of interconnected power systems. These include oscillations, power swings, blackouts, and outages caused by natural disasters, and system flaws at transmission lines, load centers, and generation sites. This can lead to power system instability, which makes it difficult to plan for and schedule maintenance. FACTS (flexible alternating current transmission system) contribute to the enhancement of transient stability as well as the damping of oscillations, in addition to the control of active and reactive power. It must, however, be placed in a specific power network, be appropriately sized, and use the best FACTS device. The purpose of this study is to examine the impact of integrating FACTS into the 153 MW Adama ii wind farm with the national grid of Ethiopia. The proposed wind farm is modeled using primary data while the grid side is represented by an IEEE 14-bus test system. Using the MATLAB/PSAT software tool, the ideal type of FACTS and their corresponding locations are determined by simulating the power flow in the combined WECS-IEEE 14 bus network. The IPFC (interline power flow controller) and UPFC (unified power flow controller) are added, together with the system response without FACTS, to compare performance. These devices’ effects on improving transient stability, reducing loss, damping power system oscillation, and supporting the voltage profile from the WECS to the distribution system have all been studied including under fault scenarios. The addition of IPFC and UPFC to the overall test systems significantly improves transient stability, dampens the multi-machine oscillations, and shortens the oscillation transient period. For this case study, FACTS devices decrease power losses in underperf

关键词： Flexible alternating current transmission system Interline power flow controller Transient stability Unified power flow controller Wind farm

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Optimizing Energy Saving for Wireless networks via Offline Decision Transformer 59

Optimizing Energy Saving for Wireless Networks via Offline D...

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59th Annual IEEE International conference on Communications (IEEE ICC)

作者： Xu, Yi Tian Wu, Di Jenkin, Michael Jang, Seowoo Liu, Xue Dudek, Gregory Samsung AI Ctr Montreal PQ Canada Samsung Elect Suwon South Korea

ISBN: (纸本)9781728190549

With the global aim of reducing carbon emissions, energy saving for communication systems has gained tremendous attention. Efficient energy-saving solutions are not only required to accommodate the fast growth in communication demand but solutions are also challenged by the complex nature of the load dynamics. Recent reinforcement learning (RL)-based methods have shown promising performance for network optimization problems, such as base station energy saving. However, a major limitation of these methods is the requirement of online exploration of potential solutions using a high-fidelity simulator or the need to perform exploration in a real-world environment. We circumvent this issue by proposing an offline reinforcement learning energy saving (ORES) framework that allows us to learn an efficient control policy using previously collected data. We first deploy a behavior energy-saving policy on base stations and generate a set of interaction experiences. Then, using a robust deep offline reinforcement learning algorithm, we learn an energy-saving control policy based on the collected experiences. Results from experiments conducted on a diverse collection of communication scenarios with different behavior policies showcase the effectiveness of the proposed energy-saving algorithms.

关键词： Offline reinforcement learning energy saving on/off control 5G cellular network

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Cooperative control of Vehicle Platoons with Unknown Dynamics using Neural network-Enhanced Reinforcement Learning

Cooperative Control of Vehicle Platoons with Unknown Dynamic...

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2025 IEEE International conference on Mechatronics, ICM 2025

作者： Yazdani Bejarbaneh, Elham Du, Haiping School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2500 Australia

ISBN: (纸本)9798331533892

This study proposes an optimal cooperative control framework for heterogeneous vehicle platoons using a neural network (NN)-based reinforcement learning (RL) algorithm in an identifier-critic-actor framework. Typically, determining the optimal control policy requires solving the Hamilton-Jacobi-Bellman (HJB) equation, but the presence of nonlinear terms within the HJB equation makes deriving analytical solutions challenging. While RL algorithms can overcome this issue, existing NN-based RL approaches are inherently complex as their update rules are derived from gradient descent optimization of the squared approximation of the HJB equation. This complexity makes their implementation challenging for nonlinear multi-vehicle systems with unknown dynamics. The proposed control strategy for platooning leverages an NN-based RL algorithm, where the update rules are directly derived from the negative gradient of a positive function that is mathematically equivalent to the HJB equation. Additionally, an NN-based identifier is incorporated into the platooning control design to dynamically estimate unknown vehicle dynamics in real time. The methodology is experimentally validated using the high-fidelity Mixed Traffic Simulator (MiTaS) co-simulation platform, which combines the Simulation of Urban Mobility (SUMO) microscopic traffic simulator with MATLAB environment. Simulations results demonstrate the efficacy of the learned strategy in achieving optimal leader-tracking performance. © 2025 IEEE.

关键词： Optimal control systems

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Process Bases for Ensuring Interoperability and Synchronization of Multifunctional network-Centric systems

Process Bases for Ensuring Interoperability and Synchronizat...

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2024 systems of Signal Synchronization, Generating and Processing in Telecommunications, SYNCHROINFO 2024

作者： Kozlov, S.V. Kubankov, A.N. Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Moscow Russia Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798350373448

The article discusses the process aspects of the complex problem of interoperability in the creation and operation of automated control systems as multifunctional integrated systems based on network-centric principles. It is noted that the integration of functional systems vertically (in terms of including several levels of the management hierarchy) and horizontally of interaction (in terms of expanding the variety of combined functional systems), as their complexity increases, leads to a rapid increase in the dimension of the network-centric system, which, in turn, expands the subject area of the problem of interoperability of its constituent functional systems and at the same time increases the complexity of the analysis of its problem area. On the other hand, in the field of general management, there is a tendency to move from automation of functional tasks to automation of management processes that combine the performance of several interrelated tasks, and, ultimately, to automation of organizational processes (business processes). It is noted that the need to apply a process approach to the development of network-centric systems as integrated multifunctional systems is determined, on the one hand, by the complexity of the traditional functional approach to ensuring the interconnected implementation of an increasing number of management tasks using network-centric systems as a qualitatively new high-tech infocommunication basis and, on the other hand, the actual transition from automation of (most often disconnected) management tasks to automation of business processes. The necessity of clarifying the composition of the process system in the life cycle of network-centric systems with the allocation of complete groups of target functional processes and supporting processes of system engineering is shown. The ontology of the subject and problem areas of the network-centric system as an integrated management system is presented and an integrated approach to the app

关键词： Interoperability

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Design of Exponentially Stabilizers for Distributed control systems Subject to Cyber Disconnections 32

Design of Exponentially Stabilizers for Distributed Control ...

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32nd Mediterranean conference on control and Automation, MED 2024

作者： Massenio, Paolo R. Tipaldi, Massimo Naso, David Rizzello, Gianluca Polytechnic University of Bari Department of Electrical and Information Engineering Bari70126 Italy Saarland University Department of Systems Engi-neering Saarbrucken66123 Germany

ISBN: (纸本)9798350395440

Distributed control architectures are attractive for large-scale interconnected systems as they provide good trade-offs between control complexity and closed-loop performance. In such a context, it becomes crucial to ensure robustness against variations in the communication topology arising from connectivity failures or cyber-attacks. Based on Linear Matrix Inequalities, this paper introduces a novel design approach for distributed controllers that exhibit robustness to changes in the communication topology. The primary objective is to achieve resilience against potential disconnections of subsystems that may occur within the communication network. The control gains are structured, and reflect the nominal communication topology. Exponential stability with a prescribed decay rate is guaranteed within the sub-configurations of the nominal communication topology. The proposed approach does not make any assumptions regarding the connectivity of the communication graph. Moreover, leveraging on the projection lemma, the design of control gains is decoupled from the design of the Lyapunov matrix, thus minimizing the conservativeness of the solution. An illustrative example on a multimachine power system is presented to demonstrate the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Distributed parameter control systems

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SIMULTANEOUS GAS EXPLORATION AND network LOCALIZATION WITH ROBOTIC SWARMS 49

SIMULTANEOUS GAS EXPLORATION AND NETWORK LOCALIZATION WITH R...

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49th IEEE International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Broghammer, Fabio Wiedemann, Thomas Zhang, Siwei Noack, Benjamin German Aerosp Ctr DLR Inst Commun & Nav Cologne Germany Otto von Guericke Univ Inst Intelligent Cooperating Syst Magdeburg Germany

ISBN: (纸本)9798350374520;9798350374513

A common issue in state-of-the-art robotic gas distribution mapping and gas source localization is the fact that the positions of the robots are assumed to be perfectly known and the position uncertainties are not considered in movement strategies. However, for cooperative mobile sensor networks with relative localization systems, the geometry of the network plays an important role for localization accuracy. In this paper, we introduce the problem of Simultaneous Exploration and Localization (SEAL). Our approach incorporates position uncertainties of gas concentration measurements into the estimation of environment features, such as a source position or wind direction, and exploits the position uncertainties to design an exploration strategy for sampling gas distributions with mobile robots. More precisely, the paper presents a swarm control algorithm that improves the estimation by adapting the swarm formation for better localization. In simulations, we analyze how position uncertainties affect estimation and exploration performance and we compare the presented control algorithm with a method unaware of position uncertainties.

关键词： Cooperative positioning Cramer-Rao lower bound Gas source localization Multi-robot control

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Model Free Artificial Neural network for an Induction Machine

Model Free Artificial Neural Network for an Induction Machin...

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IEEE conference on Power Electronics and Renewable Energy (IEEE CPERE)

作者： Norambuena, Margarita Galvez, Danilo Univ Tecn Federico Santa Maria Dept Ingn Eletr Valparaiso Chile

ISBN: (纸本)9781665452335

This paper presents the design, training, and implementation of a Feed-Forward Neural network based on Model-Free Predictive Torque control (MF-PTC) to control an Induction Machine (IM) powered by a two-level Voltage Source Inverter (2L-VSI). This control uses an algorithm capable of estimating the dynamic behavior of an IM without having a detailed knowledge of the system using the past measurements of the control variables (stator currents and flux) and input variables (stator voltages). With this approach, it is possible to design a neural network that mimics the behavior of this control just by measuring the current and estimating the flux. The control topology, neural network architecture, network training, and implementation are detailed in this work. Simulation results are presented for different operating points of the machine to validate the performance of the proposed strategy.

关键词： Model-free Predictive control Induction Motors Parameter Estimation Artificial Intelligence (AI) Intelligent controller Power Electronic systems

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