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2023 ieee conference on control technology and applications, ccta 2023

ISBN: (纸本)9798350335446

The proceedings contain 172 papers. The topics discussed include: cooperative hoisting with two crawler cranes under rope-velocity constraints;reference governor for constrained data-driven control of aerospace systems with unknown input-output dynamics;lifelong learning control of nonlinear systems with constraints using multilayer neural networks with application to mobile robot tracking;an approach to robust co-design of plant and closed-loop controller;an investigation of heuristic control strategies for multi-electrolyzer wind-hydrogen systems considering degradation;tuning of discrete-time angular droop controllers;path following or tracking model predictive control for towing kites – a question of formulation or learning?;singular dispersal solutions for a multi-agent differential game with angle-based cost function;and control of multiagent networks in the presence of misbehaving nodes without the knowledge of global network objective.

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The Sixth ieee conference on control technology and applications

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ieee control SYSTEMS MAGAZINE 2023年第2期43卷 103-109页

作者： Beghi, Alessandro Dabbene, Fabrizio

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

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The Sixth ieee conference on control technology and applications [conference Reports]

The Sixth IEEE Conference on Control Technology and Applicat...

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作者： Beghi, Alessandro Dabbene, Fabrizio

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The Seventh ieee conference on control technology and applications

The Seventh IEEE Conference on Control Technology and Applic...

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作者： Alleyne, Andrew Barton, Kira

The control cationsSeventh(ieee technology ieeecctaconference and 2023) Appli- tookon place from 16 to 18 August at the Hilton Barbados in Bridgetown, Barbados. Technical workshops were held on Tuesday, 15 August. ...

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Research on Co-Simulation Testing Method for Large-Scale Grid-Forming Wind Farm Based on RT-LAB and DSP 26

Research on Co-Simulation Testing Method for Large-Scale Gri...

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26th International conference on Industrial technology, ICIT 2025

作者： Ma, Xintong Chao, Pupu Hu, Shubo Jin, Yonglin Qiang, Zhang Pei, Zhongchen Electric Power Research Institute State Grid Liaoning Electric Power Co. Ltd Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331521950

Grid-forming control is a promising solution for large-scale wind farms applications as they do not depend on a reference voltage for the PLL. However, when faced with different improved control strategies for wind turbines, it is necessary to test and verify them in combination with actual scenarios. Therefore, the methods of the validation and testing for grid-forming large-scale wind farms is a challenge to ensure the widespread application. This paper proposes a testing method for large-scale wind farms based on RT-LAB and DSP. First, the control strategy of the grid-forming wind turbines is introduced in detail. In addition, the hardware configuration methods for RT-LAB and DSP systems are provided. Finally, based on RTLAB and DSP, a hardware in the loop (HIL) co-simulation testing platform was built to verify that the testing method for large-scale wind farms can effectively reflect the system operation. © 2025 ieee.

关键词： Direct digital control systems

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Research on UAV Data Anomaly Detection and Early Trajectory Warning technology 5

Research on UAV Data Anomaly Detection and Early Trajectory ...

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5th ieee International conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Qian, Yucheng Zhao, Changwei Huang, Weiming Wang, Shudong Institute of Electric Power Science State Grid Anhui Electric Power Co. Ltd Hefei China State Grid Anhui Electric Power Co. Ltd Equipment Management Department Hefei China

ISBN: (纸本)9798331533694

In complex substation environments, the flight safety problem when unmanned aerial vehicles perform inspection tasks becomes more and more prominent. Currently, the flight control system is the main means of UAV flight status monitoring, but it lacks in-depth analysis of flight data, especially flight trajectory data. To make full use of the flight control system data, this paper proposes an anomaly detection and trajectory prediction method. Firstly, the local temporal features of the original data in the subspace are extracted, and the anomaly detection of the flight data is achieved by measuring the changes of the data subspace vectors on the basis of reducing the computational complexity of the data, followed by dynamically adjusting the thresholds of the anomalous data by using one-class support vector machines. And finally, the UAV trajectory prediction is performed by using a time-series based LSTM neural network, focusing on the anomalies that may affect the safety of flights Data Early Warning. © 2025 ieee.

关键词： Support vector machines

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A Novel Rate of Penetration Fusion Modeling Method based on Improved Dung Beetle Optimization Algorithm and Support Vector Regression 26

A Novel Rate of Penetration Fusion Modeling Method based on ...

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26th International conference on Industrial technology, ICIT 2025

作者： Zhou, Yang Zhang, Zihan Chen, Shipeng Wu, Min Chen, Xin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Future Technology China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798331521950

The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. However, ROP modeling in deep geological drilling is complicated by nonlinearity, diverse working conditions, and high-dimensional variations. To overcome these challenges, a fusion modeling approach for ROP is proposed. First, the fuzzy C-means clustering method is applied to classify drilling data into different working con-ditions. Based on this classification, support vector regression is employed to develop ROP sub-models, effectively addressing nonlinearity. To further enhance model accuracy, an improved dung beetle optimization algorithm (IDBO) is designed to deter-mine optimal model parameters and integrate the sub-models, thereby resolving issues related to multiple working conditions and high-dimensional variations. The IDBO incorporates four key enhancements, average weight, chaos disturbance, modified local search, and re-updating of the best solution, to strength-en its global search capability. Comparative results using the ieee CEC2017 benchmark test functions demonstrate that the proposed algorithm outperforms others in 12 test functions, highlighting its strong global optimization ability. Additionally, results from real-world drilling data validate the effectiveness of the proposed modeling approach in practical applications. © 2025 ieee.

关键词： Clustering algorithms

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Remote State Estimation and Blum–Shub–Smale Machines—A Computability Analysis With applications to Virtual Twinning

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ieee Transactions on Automatic control 2025年第5期70卷 3165-3180页

作者： Boche, Holger Bock, Yannik Deppe, Christian Fitzek, Frank H. P. Technical University of Munich Chair of Theoretical Information Technology MunichD-80290 Germany BMBF Research Hub 6G-Life D-80333 Munich Germany MunichD-80799 Germany MunichD-80807 Germany Technische Universität Braunschweig Institute for Communications Technology BraunschweigD-38106 Germany 5G Lab Germany DresdenD-01062 Germany

This article analyzes aspects of the problem of remote state estimation (RSE) via noisy communication channels for their Blum–Shub–Smale (BSS) computability, motivated by an exemplary application to a formal model of virtual twinning subject to stringent integrity requirements. Computability theory provides a unique framework for the formal and mathematically rigorous analysis of algorithms and computing machines. Therefore, computability theory is essential in the domain of safety- and life-critical technology, where the formal verification of automated systems is necessary. Based on the RSE problem, we establish a simple mathematical model of virtual-twin systems that entails a formal notion of integrity (i.e., a state where the virtual entity accurately mirrors its physical counterpart). The model's notion of integrity is related to the question of whether the system under consideration is capable of computing the communication channel's zero-error capacity and corresponding zero-error codes. While this task is known to exceed the theoretical capabilities of Turing computers, we prove its formal feasibility within the model BSS machines. As different authors have proposed BSS machines as potential model of some forms of analog computing, this article serves as a proof-of-concept for a theoretical analog supremacy of unconventional information-processing hardware. Considering recent advances in the development of such hardware, forms of analog supremacy will likely become relevant in the future of cyber-physical systems and information technology. © 1963-2012 ieee.

关键词： Computability and decidability

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ASTGNN: Adaptive Spatio-Temporal Graph Neural Network for Motor Imagery Recognition

ASTGNN: Adaptive Spatio-Temporal Graph Neural Network for Mo...

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2024 International conference on Mechatronics and Intelligent control, ICMIC 2024

作者： Zhao, Zheng Xue, Haixiao Huang, Yuwen Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Motor imagery electroencephalogram recognition is a key area in brain-computer interfaces, with applications in human-computer interaction, rehabilitation, and virtual reality. Traditional methods often overlook the brain's topological characteristics and the correlations between EEG channels, resulting in suboptimal decoding. To address this, we propose an adaptive spatio-temporal graph neural network (ASTGNN), which constructs a brain graph topology and adaptively learns the topological adjacency matrix, exploring both common and individual electrode connections. The spatial features are extracted using a graph convolutional network, while a gated position-aware self-attention mechanism captures movement information and global dependencies, enhancing temporal feature extraction. Experiments show that ASTGNN significantly improves recognition, achieving an average accuracy of 87.36%. © 2025 SPIE.

关键词： Electroencephalography

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

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13th International conference on Intelligent control and Information Processing, ICICIP 2025

作者： Huang, Zhixuan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 ieee.

关键词： Contrastive Learning

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