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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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Nonlinear Model Predictive control Approximation: Applications to Truck-Trailer control System 24

Nonlinear Model Predictive Control Approximation: Applicatio...

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24th International conference on control, Automation and systems

作者： Park, Suyong Nguyen, Duc Giap Jin, Yongsik Park, Jinrak Kim, Dohee Eo, Jeong Soo Han, Kyoungseok Hanyang Univ Dept Automot Engn Seoul 04763 South Korea Kyungpook Natl Univ Dept Mech Engn Daegu 41566 South Korea Elect & Telecommun Res Inst ETRI Robot IT Convergence Res Sect Daegu 42994 South Korea Hyundai Motor Co Dept Electrified Syst Control Res Lab Hwaseong 18280 South Korea

ISBN: (纸本)9798331517939;9788993215380

In this work, we demonstrate the effectiveness of nonlinear model predictive control (NMPC) approximation based on deep neural network (DNN). MPC has been widely adopted in autonomous driving control problems to handle multiple objectives and constraints. We first design the implicit NMPC for the forward and backward motions of a truck-trailer (TT) system, which follows the reference path while maintaining safety between the head truck (HT) and the trailer (TR). However, the computational load in implicit MPC makes it a challenge for real-time implementations. To alleviate the computational burden in implicit NMPC online, an NMPC approximation approach based on DNN is adopted in this study to achieve a parametric function approximation. We conduct a comparative study on the proposed approach and a baseline controller for control performance analysis, and the computational load is evaluated on a hardware-in-the-loop (HIL) experimental system.

关键词： Deep neural network truck-trailer system nonlinear model predictive control hardware-in-the-loop systems control policy approximation

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Learning Empirical Inherited Intelligent MPC for Switched systems With network Security Communication

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6342-6355页

作者： Qi, Yiwen Tang, Yiwen Yu, Wenke Fuzhou University College of Electrical Engineering and Automation Fuzhou350108 China Shenyang Aerospace University School of Automation Shenyang110136 China

This article studies learning empirical inherited intelligent model predictive control (LEII-MPC) for switched systems. For complex environments and systems, an intelligent control method design with learning ability is necessary and meaningful. First, a switching law that coordinates the iterative learning control action is devised according to the average dwell time approach. Second, an intelligent MPC mechanism with the iteration learning experience is designed to optimize the control action. With the designed LEII-MPC, sufficient conditions for the switched systems stability equipped with the event-triggering schemes (ETSs) in both the time domain and the iterative domain are presented. The ETS in the iterative domain is to solve unnecessary iterative updates. The ETS in the time domain is to deal with potential denial of service (DoS) attacks, which includes two parts: 1) for detection, an attack-dependent event-triggering method is presented to determine attack sequence and reduce lost packets;and 2) for compensation, a buffer is used to ensure system performance during the attack period. Last, a numerical example shows the effectiveness of the proposed method. © 2020 IEEE.

关键词： Predictive control systems

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Discriminating Similar Naval Vessels Using YOLOv8 Deep Neural network

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IEEE ACCESS 2024年 12卷 174796-174807页

作者： Mccormick, Sean Richez, Adrien Mwaffo, Violet Costello iii, Donald H. US Naval Acad Weap Robot & Control Engn Dept Annapolis MD 21402 USA US Naval Acad Dept Aerosp Engn Annapolis MD 21402 USA Univ Maryland Clark Sch Engn MATRIX Lab College Pk MD 20619 USA

The next generation of United States Navy uncrewed aerial systems (UASs) is expected to operate in global positioning system and radio frequency-denied maritime environments. In these challenging conditions, these UASs must accurately identify specific surface vessels among multiple similar vessels using passive onboard sensors. This study explores the potential of a deep neural network (DNN) to differentiate between three similar surface vessel classes using actual footage of the vessels underway within their operational environments. The DNN's effectiveness is evaluated using data collected under diverse environmental conditions, including different times of the day and various sky conditions, which imply varying levels of light and visibility. The trained DNN model demonstrated outstanding performance on real-world maritime datasets, achieving a mean Average Precision of 94.2% at an intersection over union of 0.5, effectively distinguishing vessels with minimal false positives. Our findings demonstrate that, with proper training, a DNN model can accurately differentiate between vessels despite their similarity and under challenging conditions.

关键词： Marine vehicles Feature extraction Training Object recognition Artificial neural networks Navigation Lighting Data models Sea surface Global Positioning System Deep neural networks (DNN) military operations GPS/RF-denied environments maritime operations surface vessel identification uncrewed aerial systems (UASs)

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A GNN-based Decentralized Path Planning for Agricultural Robot Team: Work in Progress 24

A GNN-based Decentralized Path Planning for Agricultural Rob...

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24th International conference on control, Automation and systems

作者： Jo, Yuseung Son, Hyoung Il Chonnam Natl Univ Dept Convergence Biosyst Engn Gwangju 61186 South Korea Chonnam Natl Univ Interdisciplinary Program IT Bio Convergence Syst Gwangju 61186 South Korea Chonnam Natl Univ Res Ctr Biol Cybernet Gwangju 61186 South Korea

ISBN: (纸本)9798331517939;9788993215380

In a wide, dense, and unstructured agricultural environment, the deployment of autonomous mobile robots is an attractive option. In such the environment, large robot systems are subject to physical limitations such as communication distance and sensor measurements. This limitation is solved effectively with distributed path planning and coordination. A graph neural networks (GNNs) are an effective approach for efficient communication of multi-robot systems. In this paper, we propose a GNN-based decentralized path planning framework for agricultural robot team. The proposed model used a graph neural network for responsiveness to dynamic environmental changes, scalability, and efficient local information exchange among the adjacent agents. A graph neural network takes as input the observable features (e.g., states, subgoal, obstacle) of each agent for a partial observation scenario. As the action policy to predict the behavior of the agents, the model trained the tradition optimal multi-agent pathfinding algorithm, conflict-based search algorithm. Through the simulation-based validation, the model was confirmed to have performance comparable to existing expert algorithms, responsiveness to dynamic environments, and scalability.

关键词： Agricultural Robot Multi-UGV Path Planning Graph Neural network Decentralized

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network Intrusion Detection System using Machine Learning Algorithms 8

Network Intrusion Detection System using Machine Learning Al...

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8th International conference on Inventive systems and control, ICISC 202

作者： Kumar, Shivam Bhavana, Boyapati Shiblee, Safuwan Pranathi, Kanchinadham Sri Bhargavi, Maridu Department of Cse Guntur AP Vadlamudi India

ISBN: (纸本)9798350386578

In the face of increasing cyber threats and the expansion of computer connections and applications, strong protection against cyber-attacks has become necessary. Intrusion Detection systems (IDS) are necessary to detect anomalies and dangers in computer networks and thus increase network security. This research combines machine learning (ML) with artificial intelligence (AI) to inform data-driven access to research. performance of three different classification methods (decision trees, K-nearest neighbors (KNN), and X G Boost + LGBM integration) in identifying effects in the network. Before using the particle separator, the particle s warm optimization (PSO) algorithm is used to reduce residue. The popular KDD-CUP 99 database was used to evaluate the performance of various methods, including parameters such as the ability to detect network interference. Before using the particle separator, the particle swarm optimization (PSO) algorithm is used to reduce residue. Using the popular KDD-CUP 99 database, the performance of this method is evaluated based on metrics such as s pecificity, recall, f1 score, precision, accuracy, and consistency across many types of cyberattacks. The results highlight the effectiveness of the proposed strategy to improve network security by accurately identifying and reducing network crimes. © 2024 IEEE.

关键词： network intrusion

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Game Theory-Based Power control in Cooperative Wireless networks

Game Theory-Based Power Control in Cooperative Wireless Netw...

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2025 International conference on Pervasive Computational Technologies, ICPCT 2025

作者： Bhardwaj, Abhijeet Chauhan, Piyush Mahadik, Siddhey Nadarajah, Nalini Marella, Venkatadri Brijwal, Anuradha Maharishi Dayanand University Haryana Rohtak India Symbiosis Institute of Technology Nagpur Campus Pune India Northeastern University MA United States California State University CA United States International Technological University Santa ClaraCA United States Uttaranchal University Uttaranchal Institute of Technology Dehradun248007 India

ISBN: (纸本)9798331508685

Power control based on game theory has been proposed as a tool by which the efficiency and performance of cooperative wireless networks can be achieved. The method applies theoretical concepts from game theory, which focuses on the interactive decisions made by a group of agents to direct power across various nodes on the network effectively. Game theory-based power control aims to achieve such a compromise between individual shocker performance and network-wide performance. Instead of independent agents pursuing their utility, nodes in our solution form a cooperative environment that works towards a common goal whilst maximizing their utility functions. If appropriately designed, cooperative power control can improve network coverage and spectrum efficiency and reduce interference and energy consumption. Dynamic Transmission Power control in CDMA-based systems Using Game Theory Optimal resource allocation and improved overall network performance can be achieved when nodes are able to dynamically adjust their transmit power in response to the characteristics of the observed channel conditions and network dynamics. In this paper, we contribute to the existing literature on the game theory-based cooperative wireless networks with emphasized fundamentals and advantages. This approach is shown to be effective in the sense of optimal power allocation through various simulation results and improving the overall network performance. © 2025 IEEE.

关键词： Energy utilization

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A Software-Defined networks Approach for Cyber Physical systems 37

A Software-Defined Networks Approach for Cyber Physical Syst...

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37th International conference on Information networking (ICOIN)

作者： Khrais, Jumana Al-Issa, Mariam Al-Omari, Reham Al-Hammouri, Ahmad T. Jordan Univ Sci & Technol Dept Network Engn & Secur Irbid 22110 Jordan

ISBN: (纸本)9781665462686

Cyber Physical systems (CPSs), especially Industrial control systems (ICSs), require real-time communication to ensure, first, stability and, second, efficiency, of the operation of the controlled physical processes. Therefore, intelligent control and management of the underlying communication network is needed to achieve lower and predictable network delays. In this paper, we leverage the Software-Defined networking (SDN) to address the delay intolerance issue of ICSs. In particular, we propose, implement, and evaluate a SDN-based application running above the SDN controller that dynamically re-configures the network to improve the performance of the ICS when the network becomes congested.

关键词： Software Defined networking Cyber Physical systems Industrial control systems OpenFlow Mininet MiniCPS Quality of Service

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Federated Learning-Based Distributed Model Predictive control of Nonlinear systems

Federated Learning-Based Distributed Model Predictive Contro...

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American control conference (ACC)

作者： Xu, Zeyuan Wu, Zhe Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117585 Singapore

ISBN: (纸本)9798350382662;9798350382655

This work develops a federated learning-based distributed model predictive control (FL-DMPC) method for nonlinear systems with multiple subsystems to address the privacy-preserving issue of data transmission among subsystems and heterogeneity issue due to non-independent and identically distributed data among subsystems. Specifically, a novel FL framework is proposed to aggregate submodels into a global FL model with a sufficiently small modeling error with provable convergence properties derived based on iteration theory. Subsequently, by incorporating the FL model into a DMPC scheme, an FL-DMPC method is presented to achieve the expected performance of nonlinear systems. Finally, a chemical process network is adopted to demonstrate the effectiveness of the proposed FL-DMPC method.

关键词： Federated learning

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Data-Driven Adaptive Compensation in Droop-Operated Low-Voltage Microgrids 18

Data-Driven Adaptive Compensation in Droop-Operated Low-Volt...

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18th International conference on Probabilistic Methods Applied to Power systems (PMAPS)

作者： Trickett, Lucas Voon, Liming Watson, Jeremy Univ Canterbury Dept Elect & Comp Engn Christchurch New Zealand

ISBN: (纸本)9798350372793;9798350372786

AC microgrids typically use droop-operated gridforming inverters for voltage and frequency regulation as this allows stabilisation of the microgrid without communication. However, in cases with very low X/R line ratios, there is greater coupling between real and reactive power and stability issues can occur. This paper proposes the use of an adaptive data-driven compensator which operates in conjunction with droop-control. The proposed data-driven control uses an LQR formulation for improved performance, and is updated in real-time according to developed criteria in order to adapt to network changes. We demonstrate improved stability regions via a large-scale MATLAB/Simulink study of a two-inverter test system where the line and load parameters are varied randomly.

关键词： data-driven control convex programming grid-forming inverters microgrid control low-voltage

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