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2nd International Scientific conference Intelligent Transport systems: Ecology, Safety, Quality, Comfort, ITSESQC 2024

作者： Mykhailenko, Vladislav Gunchenko, Yurii Leshchenko, Valerii Martynovych, Larysa Korenkova, Hanna National University "Odesa Maritime Academy" Didrikhson Str. 8 Odesa65029 Ukraine Odesa I.I. Mechnikov National University Vsevoloda Zmiienka St. 2 Odesa65082 Ukraine

ISBN: (纸本)9783031873751

The article addresses the shortcomings of traditional methods used for training neural networks in management systems and emphasizes the importance of exploring the capabilities of the neuro-control method with a predictive function. This method was analyzed using the example of an adaptive air control system within a liquid fuel combustion system of a ship’s steam boiler. The neural network training and the modeling of the control system were performed using the Neural network Toolbox in Matlab, showcasing the advanced features of this platform. The results of the analysis of transition processes demonstrated that the neural network control system successfully meets expected performance criteria. Furthermore, the system showed strong adaptability to changes in the operating modes of the steam boiler, confirming the efficiency of neural control with a predictive function in dynamic conditions. This indicates that such neuro-control methods can significantly improve the stability, precision, and overall performance of complex industrial systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Boiler control

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Adaptive Tracking and Synchronization control of Dual-motor Driving Servo System 12

Adaptive Tracking and Synchronization Control of Dual-motor ...

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IEEE 12th Data Driven control and Learning systems conference (DDCLS)

作者： Dang, Jiali Ding, Jiacheng Zhang, Nan Wang, Shtibo Qingdao Univ Coll Automat Qingdao 266071 Peoples R China

ISBN: (纸本)9798350321050

This paper proposes an adaptive tracking and synchronization control scheme for dual-motor driving servo system with nonlinear dead-zone. To achieve the tracking performance, the neural network is used to approximate the unknown dynamics, and the approximation is incorporated into the control design to compensate the unknown dynamics. Then, adaptive dynamic surface controller is designed to improve the tracking performance. Moreover, a robust controller is presented based on the mean deviation coupling strategy to guarantee the synchronous operation of dual motors. Simulation results illustrate the performance of the proposed control strategy.

关键词： Adaptive dynamics surface mean deviation coupling neural network robust control

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Neural network Based Singularity-Free Adaptive Prescribed performance control of Two-Mass systems 1

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19th Chinese Intelligent systems conference

作者： Zheng, Dongdong Sun, Zeyuan Li, Weixing Beijing Inst Technol Beijing 100081 Peoples R China China North Artificial Intelligence & Innovat Res Beijing 100072 Peoples R China

ISBN: (数字)9789819968473

ISBN: (纸本)9789819968497;9789819968473;9789819968466

This paper focuses on the trajectory tracking control problem of two-mass systems, addressing the challenges posed by unknown system dynamics and unknown control gain. To handle these challenges, we first reformulate the system model into a singularity-free form and employ neural networks to approximate the unknown nonlinear functions. To ensure that the tracking errors are bounded by pre-defined performance boundaries and avoid the potential singularity problem inherent in other indirect adaptive control methods, we develop a singularity-free prescribed performance controller. Additionally, to simplify the controller design procedure, we adopt a high-order command filter and abandon the commonly used backstepping control approach. We employ the Lyapunov approach to analyze the stability of the identification and control algorithms, while simulation results demonstrate the efficacy of the proposed algorithms.

关键词： controllers

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Evaluation of control Link Impacts on the IRS Assisted Communication System with Hardware-in-the-Loop Simulation 15

Evaluation of Control Link Impacts on the IRS Assisted Commu...

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15th German Microwave conference (GeMiC)

作者： Zeng, Xianshi Buehlmeyer, Jonas Weigel, Robert Irnstorfer, Florian Friedrich Alexander Univ Erlangen Nurnberg Inst Elect Engn Erlangen Germany

ISBN: (纸本)9783982039732

With the development of Intelligent Reflecting Surface (IRS) technology, the network performance of the control channel has an increasing impact on IRS systems. This paper presents a hardware-in-the-loop (HiL) simulation methodology for the IRS controller using real network devices. We demonstrate the behaviour of a control channel connected to the simulated IRS-based communication platform. This provides test results that correlate with wired and wireless network connection. In addition, previous work has investigated the mobile user tracking (UT) scheme and proposed methods to achieve a higher signal-to-noise ratio (SNR) in the downlink case, which will provide a theoretical upper bound. Our experimental results are analysed and compared with previous simulation methods. The results show that the proposed HiL simulation methodology can validate the functionality of the UT scheme and emphasise the importance of considering the impact of the non-ideal control link on the IRS system.

关键词： IRS Hardware-in-the-loop control Link Communication System

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Modeling, Positioning, and Deep Reinforcement Learning Path Following control of Scaled Robotic Vehicles: Design and Experimental Validation

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 9856-9871页

作者： Caponio, Carmine Stano, Pietro Carli, Raffaele Olivieri, Ignazio Ragone, Daniele Sorniotti, Aldo Gruber, Patrick Montanaro, Umberto Univ Surrey Sch Mech Engn Sci Guildford GU2 7XH England Polytech Bari Dept Elect & Informat Engn I-70125 Bari Italy Polytech Univ Turin Dept Mech & Aerosp Engn I-10129 Turin Italy

Mobile robotic systems serve as versatile platforms for diverse indoor applications, ranging from warehousing and manufacturing to test benches dedicated to evaluating automated driving (AD) functions. In AD systems, the path following (PF) layer is responsible for defining steering commands to follow the reference path. Recently explored approaches involve artificial intelligence-based methods, such as Deep Reinforcement Learning (DRL). Despite their promising performance, these controllers still suffer from time-consuming training phases and may experience performance degradation when deviating from training conditions. To address these challenges, this paper proposes novel DRL controllers addressing the simulation-to-reality gap in unknown scenarios by: (i) training via an expert demonstrator which also speed up the learning phase;and (ii) a weight adaptation strategy for the resulting neural network (NN) to strengthen controller robustness and enhance PF performance. In addition, an experimentally validated vehicle model is used for training the proposed DRL algorithm and as a model for a federated extended Kalman filter (FEKF) system employed for sensor fusion in vehicle localisation. The proposed DRL-based PF controllers are experimentally evaluated through key performance indicators across multiple maneuvers not considered during training, and it is shown that they outperform benchmarking model-based controllers from the literature. Note to Practitioners-This paper presents a comprehensive toolchain for controlling mobile robots, which includes: (i) a simple yet effective two-stage least-square approach for parameter identification of the longitudinal and lateral dynamics of scaled robotic vehicles;(ii) the utilisation of a no-reset FEKF to enhance positioning leveraging all sensors commonly available on scaled robotic vehicles;(iii) the inclusion of an expert demonstrator to expedite the training phase and address the simulation-to-reality gap resulting from

关键词： Training Robots Robot sensing systems Vehicle dynamics Accuracy Artificial neural networks Laser radar Context modeling Robustness Kalman filters Automated vehicles vehicle modelling position estimation path following deep reinforcement learning

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Implementation of Plant Model of an Air Vented Dryer system using Data Driven Approach in MATLAB/Simulink

Implementation of Plant Model of an Air Vented Dryer system ...

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control Instrumentation System conference (CISCON) - Guiding Tomorrow - Emerging Trends in control, Instrumentation, and systems Engineering

作者： Gonde, Dhanashree Y. Thosar, Archana G. Sethi, Itishree COEP Technol Univ Dept Elect Engn Pune Maharashtra India Whirlpool Corp Software Engn Pune Maharashtra India

ISBN: (纸本)9798350375480;9798350375497

Now a days, physics-based models are extensively employed across diverse domains to accurately replicate the real-world physics of systems. These systems use mathematical equations and rules derived from physics laws. However, the deployment of physics-based models in simulation environment poses significant challenges because they are high fidelity and computationally complex models. There is a need to leverage these models in control and validation environments to ensure accuracy, stability, and optimal performance. One approach to leverage these models is through the use of Functional Mockup Interface (FMI) standards, which bridges the gap between physical modeling and control system design. But there are also challenges while deploying them in a simulation environment. To address the challenges associated with it, a new approach of data driven modeling using neural network has been studied in this paper. The developed neural network model aims to capture the dynamic behavior of the Air-vented dryer system, enabling smooth integration with MATLAB for Model-In-Loop and Hardware-In-Loop simulation. This approach helps better for simulating high fidelity systems, benefiting various industries which need dynamic system simulations.

关键词： Artificial neural network Functional mock-up unit Feedforward neural network MATLAB

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Evaluation of the performance of a multiphase queuing system with retrial orbit using neural network model 8

Evaluation of the performance of a multiphase queuing system...

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8th International conference on Information, control, and Communication Technologies, ICCT 2024

作者： Dang, Minh Cong Moscow Institute of Physics and Technology Russia

ISBN: (纸本)9798331517564

Multiphase queuing systems are suitable model for evaluating the performance of wireless networks with linear topology. Analytical studies of such queuing systems had been provided in previous researches. However, for a variant of multiphase queuing system with single common retrial orbit queue, exact analytical results are not available and simulation method is slow in the case of complex model. We investigate the possibility of using a neural network model to predict the performance of a multiphase queuing system with single common retrial orbit queue. Experiment results have shown that by choosing suitable neural network architecture for the corresponding queuing system model, the accuracy of neural network model was significantly improved. © 2024 IEEE.

关键词： Queueing networks

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Reinforcement Learning Based Position control of 1-DOF Rotational Hydraulic Actuator 24

Reinforcement Learning Based Position Control of 1-DOF Rotat...

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24th International conference on control, Automation and systems

作者： Kang, Hokwon Kim, Jin Tak Park, Sangsin Cho, Jungsan Univ Sci & Technol UST Dept Robot Engn Daejeon 34113 South Korea Korea Inst Ind Technol KITECH Hydraul Robot Lab Robot R&D Grp Ansan 15588 South Korea

ISBN: (纸本)9798331517939;9788993215380

In this study, we propose a reinforcement learning (RL) based position control method for a one-degree-of-freedom (1-DOF) rotational hydraulic actuator. controlling hydraulic actuators is challenging due to their nonlinear characteristics and complex structure. Reinforcement learning offers the advantage of enabling control through learning without requiring a detailed understanding of the model, using virtual environments. However, simulators do not perfectly replicate real-world conditions. To address this, we aim to enhance the fidelity of the simulation and improve the performance of reinforcement learning-based controllers by integrating real-world data into the simulation using deep neural networks (DNN). The reinforcement learning is trained using the Proximal Policy Optimization (PPO) algorithm, and it is validated through experiments with step and sine inputs.

关键词： Artificial Intelligent systems Neural network systems Reinforcement Learning Hydraulic System control Position control

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Novel Slice Admission control Scheme with Overbooking and Dynamic Buyback Process

Novel Slice Admission Control Scheme with Overbooking and Dy...

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4th IEEE conference on network Function Virtualization / 9th Software Defined networks conference (IEEE NFV-SDN)

作者： Yese, Solomon Orduen Berri, Sara Chorti, Arsenia CY Cergy Paris Univ ETIS ENSEA CNRSUMR 8051 F-95000 Paris France

ISBN: (纸本)9798350302547

In 5G and beyond systems, network slicing plays a key role as it enables infrastructure providers (InPs) to create logical networks (slices) and virtually share network resources to their tenants. However, due to the limited nature of network resources of the InP, resource management algorithms like resource allocation and admission control are required to ensure efficient management of the InP's scarce resources. Indeed, admission control algorithms play a critical role of regulating access to the network, by determining whether a slice request should be accepted or not with respect to some standards such as maximizing the InP's revenue and maintaining service level agreements (SLAs). In this paper, we propose an admission control algorithm that employs the concept of overbooking which allows the InP to admit slice requests beyond it's nominal available resources. Moreover, we employ a dynamic queue adaption priority, step-wise pooling and dynamic buyback price mechanism to ensure efficient and profitable admission decision for the InP. We assess the performance of the proposed algorithm against state of the art (SOTA) solution considering different priority schemes. The results show that the proposed solution outperforms the SOTA solution as it yields i) higher revenue, ii) lower buyback cost and iii) higher net revenue for the InP while still maintaining a marginally higher slice acceptance rate.

关键词： Admission control network Slicing 5G/B5G Dynamic Priority Dynamic Pricing

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Radial Basis Function Neural network-Based Super-Twisting Blade Pitch controller for the Floating Offshore Wind Turbine 10

Radial Basis Function Neural Network-Based Super-Twisting Bl...

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10th International conference on control, Decision and Information Technologies (CoDIT)

作者： Didier, Flavie Liu, Yong-Chao Laghrouche, Salah Univ Franche Comte FEMTO ST Inst UMR 6174 CNRS UTBM Belfort France

ISBN: (纸本)9798350373981;9798350373974

The study introduces a novel Radial Basis Function Neural network-based Super-Twisting Sliding Mode Collective Blade Pitch control (RBFNN-STSM-CBPC), designed specifically for semi-submersible platform-based Floating Off-shore Wind Turbines (FOWTs) operating above rated speed (Region iii). The proposed composite controller is developed using a refined nonlinear control-Oriented Model, including lumped unmodeled dynamics and external disturbances. To our knowledge, this is the first time that a neural network STSM-CPBC approach is designed for this application. The RBFNN operates as an adaptive observer for the lumped disturbance, enhancing the robustness and performance of the standard STSM-CBPC for the same gains. Its adaptive law, formulated through the Lyapunov method, ensures stability and convergence by adjusting the adaptive weight. Simulation results demonstrate the superiority of the RBFNN-STSM-CBPC over the standard STSM-CBPC method in regulating rotor speed and mitigating platform motion.

关键词： Lyapunov methods

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