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Chattering-Free Fault-Tolerant Cluster Control and Fault Direction Identification for HIL UAV Swarm With Pre-Specified Performance

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 183-197页

作者： Pei-Ming Liu Xiang-Gui Guo Jian-Liang Wang Daniel Coutinho Lihua Xie the Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing the Shunde Graduate School of University of Science and Technology Beijing IEEE Autonomous Intelligent Systems Department Hangzhou Innovation Institute of Beihang University the Department of Automation and Systems Universidade Federal de Santa Catarina the School of Electrical and Electronic Engineering Nanyang Technological University

In this paper, the problem of pre-specified performance fault-tolerant cluster consensus control and fault direction identification is solved for the human-in-the-loop(HIL) swarm unmanned aerial vehicles(UAVs) in the presence of possible nonidentical and unknown direction faults(NUDFs) in the yaw *** control strategy begins with the design of a pre-specified performance event-triggered observer for each individual *** observers estimate the outputs of the human controlled UAVs, and simultaneously achieve the distributed design of actual control signals as well as cluster consensus of the observer *** is worth mentioning that these observers require neither the high-order derivatives of the human controlled UAVs' output nor a priori knowledge of the initial conditions. The fault-tolerant controller realizes the pre-specified performance output regulation through error transformation and the Nussbaum function. It should be pointed out that there are no chattering caused by the jump of the Nussbaum function when a reverse fault occurs. In addition, to provide a basis for further solving the problem of physical malfunctions, a fault direction identification algorithm is proposed to accurately identify whether a reverse fault has occurred. Simulation results verify the effectiveness of the proposed control and fault direction identification strategies when the reverse faults occur.

关键词： Chattering-free cluster consensus fault direction identification human-in-the-loop (HIL) nonidentical and unknown direction faults (NUDFs) pre-specified performance swarm unmanned aerial vehicles (UAVs)

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Data-driven output regulation control for constrained linear systems

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Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Evaluating Carbon Emissions of Hybrid Manufacturing Process: A Case Study on Additive and Subtractive Manufacturing 20

Evaluating Carbon Emissions of Hybrid Manufacturing Process:...

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20th CIRP Conference on Modeling of Machining Operations in Mons, CIRP CMMO 2025

作者： Panagiotopoulou, Vasiliki C. Xydea, Evangelia Stavropoulos, Panagiotis Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras 26504 Greece

All environmental issues, and particularly climate change, have irreversible environmental, social, and financial impacts. Significantly reducing carbon emissions by 2030, as the highest contributor to climate change, is a vital for EU as expressed in the European Green Deal. In this direction, sustainable manufacturing intends to reduce negative impacts by minimizing energy consumption, lowering carbon emissions, and optimizing resource efficiency. Hybrid Manufacturing (HM), combining additive (AM) and subtractive manufacturing (SM) processes, is a very promising process in sustainable manufacturing, in addition of being a novel approach. The aim of this paper is to identify the carbon intensive parts of a HM cell, perform carbon footprint calculations through mathematical modelling and Life Cycle Assessment (LCA) and classify them as either energy or material related carbon emissions. This methodology is implemented in an HM including Direct Energy Deposition (DED) and CNC milling, successively alternating between the two to complete the part. Results indicate that at machine tool level, the material related emissions (4.64 kg CO2-eq), slightly dominate over the energy related emissions (4.51 kg CO2-eq). Powder consumption is almost solely responsible for material related emissions. Among energy related emissions, the AM cell's chiller was the largest contributor (accounting for 28.3% of the total emissions), followed by the AM head motion system (10.9%), and laser machine (9.6%), while the subtractive process emitting considerably less in this case. Future work will aim to optimize process parameters to reduce HM emissions while ensuring high product quality. © 2025 The Author(s).

关键词： Green manufacturing

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Assessing the impact of qualitative and quantitative parameters on additive manufacturing carbon emissions 12

Assessing the impact of qualitative and quantitative paramet...

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12th CIRP Global Web Conference, CIRPe 2024

作者： Paraskevopoulou, Aikaterini Panagiotopoulou, Vasiliki C. Stavropoulos, Panagiotis Laboratory for Manufacturing Systems & Automation Department of Mechanical Engineering and Aeronautics University of Patras 26504 Greece

The manufacturing sector significantly contributes to Climate Change via its high carbon emissions. This study aims to demonstrate the impact of qualitative (related to type of resources used) versus quantitative (related to the amount of resources used) parameters on the carbon emissions of Additive Manufacturing (AM). Calculation and categorization in different manufacturing levels (process, machine tool and system) is followed by a sensitivity analysis, to determine the impact of quantitative and qualitative parameters on the carbon emissions of different levels. Findings indicate that energy is the main driver at process and machine-tool levels regardless the qualitative or quantitative nature of parameters. In contrast, material type and amount have the highest impact at system level. Generally, the qualitative parameters are significantly more important than the quantity of resources regarding their influence on emissions, regardless the manufacturing level. These insights can support the decision-making procedure towards reduction of AM carbon emissions. © 2025 The Author(s).

关键词： Low emission

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An Explainable Active Learning Approach for Enhanced Defect Detection in Manufacturing 2nd

An Explainable Active Learning Approach for Enhanced Defect ...

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2nd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2024

作者： Nikolakis, Nikolaos Catti, Paolo Alexopoulos, Kosmas Laboratory for Manufacturing Systems and Automation Mechanical Engineering and Aeronautics Department University of Patras Patras26504 Greece

ISBN: (纸本)9783031864889

Artificial Intelligence (AI) can significantly support manufacturing companies in their pursuit of operational excellence, by maintaining efficiency while minimizing defects. However, the complexity of AI solutions often creates a barrier to their practical application. Transparency and user-friendliness should be prioritized to ensure that the insights generated by AI can be effectively applied in real-time decision-making. To bridge this gap and foster a collaborative environment where AI and human expertise collectively drive operational excellence, this paper suggests an AI approach that targets identifying defects in production while providing understandable insights. A semi-supervised convolutional neural network (CNNs) with attention mechanisms and Layer-wise Relevance Propagation (LRP) for explainable active learning is discussed. Predictions but also feedback from human experts are used to dynamically adjust the learning focus, ensuring a continuous improvement cycle in defect detection capabilities. The proposed approach has been tested in a use case related to the manufacturing of batteries. Preliminary results demonstrate substantial improvements in prediction accuracy and operational efficiency, offering a scalable solution for industrial applications aiming at zero defects. © The Author(s) 2025.

关键词： Active learning

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Towards Explicable AI in Systemic Identification of Surrogate Models of Manufacturing Processes 2nd

Towards Explicable AI in Systemic Identification of Surrogat...

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2nd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2024

作者： Papacharalampopoulos, Alexios Papaioannou, Christos Karagianni, Olga Maria Stavropoulos, Panagiotis Laboratory for Manufacturing Systems and Automation Mechanical Engineering and Aeronautics Department University of Patras Patras26504 Greece

ISBN: (纸本)9783031864889

Surrogate models of manufacturing processes are highly useful in the context of digital twins, as they can be considered as a prerequisite for linking between physics and real machines’ cases. However, in many cases of surrogate models, the so-called (hyper)parameters are not easy to be estimated. In this work, the role of AI is investigated in terms of its efficiency in doing that in an (semi) automated way. The case of ARX models is considered for this, where the parameters are clearly related to the physics of the process. In particular, the technique of systemic identification is adopted. The aforementioned investigation is performed in this particular case regarding intuitiveness with respect to the physics, through adopting AI techniques that could be considered to be explicable in some sense. The results indicate the limitations of the AI techniques and their link to the process dynamics as well as their relationships with traditional techniques. © The Author(s) 2025.

关键词： Smart manufacturing

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An autonomous dynamic recovery technology for autonomous underwater vehicles based on unmanned surface vehicles

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Science China(Technological Sciences) 2025年第2期68卷 315-317页

作者： Yueying WANG Yiqi SHI Guanghui WEN Huaicheng YAN School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444China Department of Systems Science School of MathematicsSoutheast UniversityNanjing 210096China School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China

Autonomous underwater vehicles(AUVs) have attracted considerable attention due to their vast potential applications, such as offshore oil exploration, underwater rescue, military reconnaissance, and marine scientific ... 详细信息

关键词： underwater autonomous battery

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Fast Coordinated Power Control for Improving Inertial and Voltage Support Capability of Battery Energy Storage systems

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CSEE Journal of Power and Energy systems 2025年第1期11卷 280-295页

作者： Zhen Gong Chengxi Liu Filipe Faria Da Silva Yonghao Gui Claus Leth Bak School of Electrical Engineering and Automation Wuhan UniversityWuhan430072China Department of Energy Technology Aalborg University9220 AalborgDemark Department of Electronic Systems Aalborg University9220 AalborgDenmark

This paper proposes a fast coordinated power control method based on two augmented channels(AC)in battery energy storage system(BESS)to improve inertial and voltage support capability,i.e.,a frequency-reactive power channel(FRPC)and a voltage-real power channel(VRPC).For frequency control in the power distribution system with high resistance/inductance ratio,the coupling mechanism between rate of change of frequency(RoCoF)and required reactive power(RRP)of grids is analyzed,indicating RoCoF is proportional to ***,RoCoF is utilized in FRPC to generate reactive power for complementary inertial emulating ***,for voltage control,coupling characteristics between rate of change of voltage(RoCoV)and demanding real power(DRP)of grids is also studied,revealing RoCoV is proportional to ***,it can be adopted in VRPC to generate real power for complementary voltage ***,grid-voltage-modulated direct power control is selected as the inner power control loop to track power references with faster dynamic performance compared with traditional vector-oriented ***,simulations and hardware-in-loop experiments validate improvement in performance of grid frequency and voltage control based on the proposed method.

关键词： Augmented channels direct power control frequency-reactive power channel(FRPC) inertial and voltage support voltage-real power channel(VRPC)

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Reference geometries and indicators for the proce b window development of wire Directed Energy Deposition 1

Reference geometries and indicators for the proce b window d...

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1st International Conference on Digital Sovereignty, ICDS 2024

作者： Tzimanis, Konstantinos Porevopoulos, Nikolas Bourlesas, Nikolas Stavropoulos, Panagiotis Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Rio Patra26504 Greece

The industrial interest for Directed Energy Deposition (DED) proce bes increases;however, intensive experimental work is needed for the determination of proce b inputs each time a new material and machine are investigated, leading to waste of time and resources. Therefore, this work presents a structured way of determining the values of key proce b parameters based on reference geometries such as single tracks, thin walls, overlapping tracks and complex geometries as well as metrological responses from where acceptance thresholds are developed. In the present study, the methodology is implemented in a wire laser-based DED proce b;the working material is stainle b steel AISI316L while monitoring devices that capture the melt pool features, the over-built and finally the proce b performance are retrofitted to accelerate the transformation of proce b data to meaningful information and proce b knowledge. © 2025 The Author(s).

关键词： Velocity measurement

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Industrial Maintenance Optimization Based on the Integration of Large Language Models (LLM) and Augmented Reality (AR) 2nd

Industrial Maintenance Optimization Based on the Integration...

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2nd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2024

作者： Angelopoulos, John Manettas, Christos Alexopoulos, Kosmas Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Rio Patras26504 Greece

ISBN: (纸本)9783031864889

Traditional maintenance procedures often rely on manual data processing and human expertise, leading to inefficiencies and potential errors. In the context of Industry 4.0 several digital technologies, such as Artificial Intelligence (AI), Big Data Analytics (BDA), and eXtended Reality (XR) have been developed and are constantly being integrated in a plethora of manufacturing activities (including industrial maintenance), in an attempt to minimize human error, facilitate shop floor technicians, reduce costs as well as reduce equipment downtimes. The latest developments in the field of AI point towards Large Language Models (LLM) which can communicate with human operators in an intuitive manner. On the other hand, Augmented Reality, as part of XR technologies, offers useful functionalities for improving user perception and interaction with modern, complex industrial equipment. Therefore, the context of this research work lies in the development and training of an LLM in order to provide suggestions and actionable items for the mitigation of unforeseen events (e.g. equipment breakdowns), in order to facilitate shop-floor technicians during their everyday tasks. Paired with AR visualizations over the physical environment, the technicians will get instructions for performing tasks and checks on the industrial equipment in a manner similar to human-to-human communication. The functionality of the proposed framework extends to the integration of modules for exchanging information with the engineering department towards the scheduling of Maintenance and Repair Operations (MRO) as well as the creation of a repository of historical data in order to constantly retrain and optimize the LLM. © The Author(s) 2025.

关键词： Cost reduction

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