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Optimal Output Tracking for Switched systems Under DoS Attacks: A Model-Free Adaptive Predictive control Method

IEEE TRANSACTIONS ON CIRCUITS AND systems ii-EXPRESS BRIEFS 2024年第1期71卷 266-270页

作者： Qi, Yiwen Guo, Shitong Tang, Yiwen Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China Shenyang Aerosp Univ Sch Automat Shenyang 110136 Peoples R China

This brief studies a new resilient event-triggered model-free adaptive predictive control (MFAPC) method with anti-attacks for disturbed switched nonlinear systems in non-ideal network. The switched nonlinear systems are transformed into equivalent dynamic data models by dynamic linearization. Considering the denial of service (DoS) attacks in non-ideal network environment, an anti-attacks method based on a hold mechanism and a resilient event-triggering strategy (RETS) is considered, which reduces attacks impact on system performance. A parameter estimator is given to estimate the external disturbance and further obtain accurate system models. In addition, a new tracking error boundedness analysis method is given by using the average dwell time (ADT) technique and Lyapunov function. Finally, motor simulation results are given to verify the applicability of the proposed method.

关键词： Switches Nonlinear systems Predictive models Switched systems Integrated circuit modeling Data models Adaptation models Switched nonlinear systems MFAPC DoS attacks RETS tracking error

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NIOS ii soft-core processor and ethernet controller solution for RPC-DAQ in INO ICAL

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JOURNAL OF INSTRUMENTATION 2025年第1期20卷

作者： Elangovan, Yuvaraj Saraf, Mandar Satyanarayana, B. Upadhya, S. S. Panyam, Nagaraj Shinde, Ravindra Majumder, Gobinda Sil, D. Pathaleswar, S. Thoi, S. Thoi Ravindran, K. C. Tata Inst Fundamental Res Mumbai India

This paper introduces a high-performance Soft-Core Processor based data acquisition system designed for handling Resistive Plate Chambers (RPCs). The DAQ consist of FPGA-based hardware equipped with Soft-Core Processor and embedded hardwired Ethernet controllers named RPC-DAQ, offering a versatile and fast network-enabled data acquisition solution. A soft processor, NIOS, is instantiated within an Intel Cyclone IV FPGA, overseeing control, communication, and data transfer with remote processing units. These integrated RPC-DAQ units, in substantial numbers, connect to a limited set of high-end processing units via LAN switches. This paper provides detailed account of the software implementation scheme for the NIOS processor in the RPC-DAQ system. A remarkable 28,800 RPC-DAQ units will be deployed in proximity to the RPCs, serving the proposed INO-ICAL experiment in Theni-Madurai, Tamil Nadu. The network-enabled RPC-DAQ units controlled by the soft processor offloads FPGA tasks including event data acquisition, periodic health monitoring of RPCs, command interfaces, high voltage control, and data transfer to back-end data concentrators. Communication and data transfer are executed efficiently via TCP and UDP protocols over a 100 Mbps Ethernet interface. This system provides innovative solutions to improve data acquisition and control in large-scale scientific experiments.

关键词： control and monitor systems online Data acquisition concepts Detector control systems (detector and experiment monitoring and slow-control systems, architecture, hardware, algorithms, databases) Trigger concepts and systems (hardware and software)

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SACWOM: Synergistic Adaptive Congestion Window Optimization Mechanism for Self-Clocked Algorithm

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IEEE ACCESS 2025年 13卷 22086-22117页

作者： Zubaydi, Haider Dhia Jagmagji, Ahmed Samir Molnar, Sandor Budapest Univ Technol & Econ Fac Elect Engn & Informat Dept Telecommun & Artificial Intelligence H-1111 Budapest Hungary

Congestion control (CC) is essential in networked systems, especially in environments with strict delay and throughput requirements. While CC algorithms like Self-Clocked Rate Adaptation for Multimedia (SCReAM) and Bottleneck Bandwidth and Round-trip propagation time (BBR) have shown progress, each faces limitations: BBR encounters difficulty in balancing responsiveness and fairness when rapid fluctuations in network conditions arise, which can lead to inefficient performance in shared environments. On the other hand, although SCReAM is designed for multimedia traffic, it struggles to dynamically adapt the congestion window to unpredictable or highly congested networks, leading to inefficient resource utilization. This paper proposes SACWOM, a hybrid congestion window optimization mechanism that combines our novel method with SCReAM's rate control and BBR's bandwidth-delay estimation to enhance throughput stability, fairness, and adaptability by dynamically adjusting the congestion window. Extensive simulations demonstrate SACWOM's significant improvements over SCReAM in managing congestion window and bytes in flight under diverse network conditions. In Phase I, SACWOM achieved up to 11.96-13.27% increase in congestion window and bytes in flight by maintaining higher data flow. Phase ii analysis shows up to 20.76-22.64% improvements with optimized configurations. Finally, Phase iiI, comprising 100 experiments, reveals SACWOM's dynamic adaptability, achieving up to 50-70% improvements. These results highlight SACWOM as a robust mechanism suitable for various applications across diverse network scenarios.

关键词： Heuristic algorithms Delays Bandwidth Throughput Mathematical models Adaptive systems Real-time systems Optimization Media Estimation BBR congestion control congestion window hybrid optimization rate adaptation SCReAM self-clocked

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Adaptive Neural network-Based Fixed-Time control for Trajectory Tracking of Robotic systems

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IEEE TRANSACTIONS ON CIRCUITS AND systems ii-EXPRESS BRIEFS 2023年第1期70卷 241-245页

作者： Liu, Zhuang Zhang, Ouyang Gao, Yabin Zhao, Yue Sun, Yizhuo Liu, Jianxing Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China

This brief investigates the problem of fixed-time trajectory tracking control of uncertain robotic systems. Firstly, an adaptive radial basis function neural network is designed to estimate the model uncertainties and viscous frictions in robotic systems. Secondly, a segmented terminal sliding mode control (TSMC) variable is adopted to alleviate the singularity problem. To improve the tracking performance, a new second-order fixed-time reaching law is designed. Then, in order to make the tracking errors converge to a small neighborhood of the origin in a fixed-time independent of the initial state, a novel fixed-time non-singular TSMC based on the adaptive neural network is proposed. Finally, the experimental results demonstrate the effectiveness and advantage of the proposed control method.

关键词： Fixed-time control nonsingular terminal sliding mode control neural networks robotic systems

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Assisted Physical Interaction: Autonomous Aerial Robots with Neural network Detection, Navigation, and Safety Layers

Assisted Physical Interaction: Autonomous Aerial Robots with...

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International conference on Unmanned Aircraft systems (ICUAS)

作者： Berra, Andrea Sankaranarayanan, Viswa Narayanan Seisa, Achilleas Santi Mellet, Julien Gamage, Udayanga G. W. K. N. Satpute, Sumeet Gajanan Ruggiero, Fabio Lippiello, Vincenzo Tolu, Silvia Fumagalli, Matteo Nikolakopoulos, George Soto, Miguel Angel Trujillo Heredia, Guillermo CATEC Adv Ctr Aerosp Technol Seville Spain Lulea Univ Technol Dept Comp Elect & Space Engn Robot & AI Team Lulea Sweden Univ Naples Federico II Naples Dept Elect Engn & Informat Technol PRISMA Lab Naples Italy Tech Univ Denmark Dept Elect Engn & Photon Automat & Control Grp Lyngby Denmark Univ Seville Robot Vis & Control Grp Sch Engn Seville Spain

ISBN: (纸本)9798350357899;9798350357882

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.

关键词： Aerial Physical Interaction UAVs control Barrier Function Neural network Edge Computing

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Data-centric predictive control with tuna swarm optimization-backpropagation neural networks for enhanced wind turbine performance

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RENEWABLE ENERGY 2024年 237卷

作者： Li, Wei Pandit, Ravi Kumar Cranfield Univ Ctr Life Cycle Engn & Management Wharley End England

Wind energy is a significant renewable resource, but its efficient harnessing requires advanced control systems. This study presents a Data-Centric Predictive control (DPC) system, enhanced by a Tuna Swarm OptimizationBackpropagation Neural network (TSO-BPNN) for predictive wind turbine control. It's like a smart tool that uses innovative fusion of deep learning, predictive control, and reinforcement learning. Unlike traditional control methods, the proposed approach uses real-time data to optimize turbine performance in response to fluctuating wind conditions. The system is validated using simulations on the FAST platform, which demonstrate its superior performance in two critical operational regions. Specifically, in Region ii, where the objective is to maximize power extraction from the wind, the DPC achieves a 1.07 % reduction in overshoot and an improvement of 36.14 units in steadystate error compared to traditional methods. The response time remains comparable to existing Model Predictive control (MPC) strategies, ensuring real-time applicability without sacrificing efficiency. In Region iiI, where maintaining constant power output is crucial, the DPC outperforms both the baseline and MPC methods, reducing overshoot by 0.58 % and improving accuracy by 17.27 units compared to the baseline method. These results highlight the effectiveness of the proposed DPC system in optimizing turbine performance under variable wind conditions, offering a significant improvement over traditional methods in both accuracy and control precision.

关键词： Deep neural networks TSO-BPNN Model predictive control Reinforcement learning Wind turbine control

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Artificial Neural network Based Virtual Sensor for Distributed controlled DC Microgrids 7

Artificial Neural Network Based Virtual Sensor for Distribut...

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IEEE 7th International conference on Industrial Cyber-Physical systems (ICPS)

作者： Devakumar, Annavaram Mishra, Sukumar IIT Delhi Dept Elect Engn New Delhi India

ISBN: (纸本)9798350363029;9798350363012

The measured output current data of distributed energy resources is crucial in realizing cyber-physical DC microgrids (DCMG) and achieving distributed control objectives. This paper proposes an artificial neural network-based output current estimation at the secondary control level. The estimated output current value is used to implement distributed control successfully for the DCMG. The proposed artificial neural network (ANN) acts as a virtual sensor to alleviate problems associated with physical sensors, such as faults and biasing effects. The ANN's input features and training performance are detailed for the considered DCMG. The performance of the proposed virtual sensor-based distributed controlled DCMG is validated on an experimental hardware setup under multiple load changes and communication delays.

关键词： dc microgrid distributed control neural network virtual sensor

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Stochastic Event-Triggered Feedback Physical Watermarks Against Replay Attacks

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IEEE TRANSACTIONS ON network SCIENCE AND ENGINEERING 2025年第2期12卷 814-822页

作者： Zhao, Xudong Xing, Wei Wang, Xinyu Zhao, Ning Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China Bohai Univ Coll Control Sci & Engn Jinzhou 121010 Peoples R China

This paper considers the security of cyber-physical systems (CPSs) subject to replay attacks with measurements of the sensor transmitted to the remote estimator over a wireless communication network. We present a novel stochastic event-triggered feedback physical watermarking technique to effectively mitigate the impact of replay attacks, while simultaneously addressing the performance degradation caused by the inclusion of physical watermarks. This innovative approach dynamically adjusts the probability of adding physical watermarks based on the system's current operational state, striking a balance between optimal performance and effective countermeasures against replay attacks. And, as a result, the probability of adding physical watermarks increases when the system is subjected to malicious replay attacks. Furthermore, the performances of both the system and the detector are thoroughly characterized in two distinct scenarios: (i) the system operating under normal conditions, and (ii) the system being subjected to replay attacks. These scenarios allow for a comprehensive evaluation of the system's capabilities and the detector's efficacy in detecting and mitigating potential security threats. Finally, simulation examples are provided to corroborate and illustrate the theoretical results.

关键词： Watermarking Event detection Time measurement Detectors System performance Kalman filters Estimation Costs Optimization Denial-of-service attack Cyber-physical systems event-triggered feedback physical watermark replay attacks

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A Modular Prescribed performance Formation control Scheme of a High-Order Multi-Agent System with a Finite-Time Extended State Observer

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ELECTRONICS 2025年第9期14卷 1783-1783页

作者： Shi, Zhihan Han, Weisong Zhang, Chen Zhang, Guangming Nanjing Tech Univ Sch Mech & Power Engn Nanjing 211816 Peoples R China Nanjing Tech Univ Coll Transportat Engn Nanjing 211816 Peoples R China Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China

This paper proposes a modular control framework for high-order nonlinear multi-agent systems (MASs) to achieve distributed finite-time formation tracking with a prescribed performance. The design integrates two modules to address uncertainties and safety constraints simultaneously. Module I-Prescribed performance-Based Trajectory Generation: A virtual signal generator constructs collision/connectivity-aware reference trajectories by encoding time-varying performance bounds into formation errors. It ensures network rigidity and optimal formation convergence through dynamic error transformation. Module ii-Anti-disturbance Tracking control: A finite-time extended state observer (FTESO) estimates and compensates for uncertainties within a finite time, while a time-varying surface controller drives tracking errors into predefined performance funnels. This module guarantees rapid error convergence without violating the transient constraints from Module I. The simulations verified the accelerated formation reconfiguration under disturbances, and thus, demonstrated improved robustness and convergence over asymptotic approaches. The framework offers a systematic solution for safety-critical MAS coordination with heterogeneous high-order dynamics.

关键词： multi-agent systems prescribed performance control finite-time extended state observer connectivity maintenance collision avoidance

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performance Analysis of Trickle Timer Parameters on DIO Messages in High-Density network 15

Performance Analysis of Trickle Timer Parameters on DIO Mess...

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15th International conference on Information and Communication systems (ICICS)

作者： Albashayreh, Amjad Yassein, Muneer Bani Jordan Univ Sci & Technol Dept Comp Sci Irbid Jordan

ISBN: (纸本)9798331540777;9798331540760

The trickle algorithm allows network nodes to share the medium for data transmission, making it possible to exchange data in a reliable, energy-efficient, simple, and scalable manner. Consequently, trickle's operation is critical for performance metrics such as packet delivery ratio (PDR), power consumption (PC), and message overhead, particularly in high-density networks. A high-density network is designed to serve a large number of WLAN clients, and the performance analysis of network protocols can be challenging due to its large scale. This paper presents comprehensive analysis results for the RPL protocol in high-density networks with random topology to investigate the effect of PC, and control traffic overhead when the RX ratio varies with node count. Also, this paper compares the most recent RPL performance studies and conducts a detailed study of the effect of trickle timer parameters on DIO messages. The results revealed that parameters significantly affect PDR, PC, and control traffic overhead. The minimum rank with hysteresis objective function (MRHOF) reduced control traffic overhead by transmitting 325 packets with 1.05 traffic overhead, while objective function zero (OF0) showed superior power consumption at 1.5 mW.

关键词： IoT RPL OF0 MRHOF Cooja DODAGs PDR

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