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Formation control and path planning of multi-robot systems via large language models

Science China(Information Sciences) 2025年第5期68卷 72-88页

作者： Dong XUE Xuanjie ZHOU Ming WANG Fangzhou LIU Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology Research Institute of Intelligent Control and Systems School of Astronautics Harbin Institute of Technology

Existing path planning and coordination control methods for multi-robot systems(MRS) typically rely on predefined rules and rudimentary algorithms. However, these methods often struggle to adapt flexibly to complex environments and to adjust motion targets appropriately. To address this challenge, this study presents a large language model(LLM)-assisted framework. By integrating textual descriptions of complex motion constraints, robot information, and local environmental data as inputs, LLMs generate motion objectives and translate them into executable control commands for the robots, thereby achieving coordinated control and path planning. This framework facilitates the generation, maintenance, and reshaping of formations in MRSs during path planning, applicable to both obstacle-free and obstacle-avoidance environments. Simulation results demonstrate that LLM-based control strategies enhance the autonomy, adaptability, flexibility, and robustness of MRS by processing complex information, making intelligent decisions, adapting to environmental changes, and handling disturbances and uncertainties.

关键词： multi-robot systems formation control path planning obstacle avoidance large language models

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A New Strategy to Mitigate Overheating in Solar PV systems Using Adaptive Solar Tracking

A New Strategy to Mitigate Overheating in Solar PV Systems U...

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International Conference on Artificial Intelligence in Renewable Energetic systems, IC-AIRES 2024

作者： Dahli, Khadidja Selam, Karim Cheggaga, Nawal Electrical and Remote Control Systems Laboratory LabSET Faculty of Technology University of Blida 1 Blida Algeria Process Control Laboratory Polytechnical National School Algiers Algeria

ISBN: (纸本)9783031803000

High temperatures pose several challenges for solar power generation in desert environments, such as decreased efficiency, increased resistance, thermal-induced degradation, and potential damage to the panels. This study aims to address these challenges by proposing a solar tracking system that manages excessive heat and does not require cooling systems or additional complex mechanisms. Instead, we present an innovative approach to protect solar systems from increased heat by integrating temperature data readings into conventional solar tracking tools. When the temperature exceeds the panel's recommended threshold, the tracker adjusts its position to misalign the panels from the sun and prevent damage. The system will resume regular tracking once the temperature drops below the critical value. Our research indicates that positioning the tracker vertically at 90° helps protect the panels from overheating and reduces dust accumulation. This innovation has the potential to enhance the performance and longevity of solar power systems in desert environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cooling systems

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UAV longitudinal autopilot design using SLC and TECS controllers

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CEAS Aeronautical Journal 2024年第2期15卷 351-362页

作者： Ghelem, Nourddine Boudana, Djamel Bouchhida, Ouahid Laboratory of Process Control LCP Department of Engineering Control Systems and Applied Mathematics National Polytechnic School ENP of Algiers 10 St Hacen Badi El Harrach Algiers Algeria Department of Electrical Engineering LREA Laboratory University of Medea Medea Algeria

UAVs are becoming increasingly prevalent in a wide range of fields, including surveillance, photography, agriculture, transportation, and communications. Hence, research institutions have developed a range of linear and non-linear controllers that can enhance the stability and efficiency of these UAVs while performing their assigned tasks. In this paper, we present a longitudinal autopilot design for the Sky Sailor solar UAV using two different control techniques. The first technique is a classic controller known as SLC (Successive Loop Closure), which is based on consecutive closed loops using a PID controller. The second technique is the TECS (Total Energy control System) controller, which uses the specified total energy rate and energy distribution rate to regulate the airspeed and altitude of the UAV. After detailing the working principle of each controller and calculating the tuning parameters for each one, we applied the two techniques to the non-linear model that describes the behavior of the Sky Sailor UAV using MATLAB Simulink. The simulation results showed that the TECS controller is superior to the SLC controller in terms of stability and energy efficiency. As such, this technique is an excellent choice for solar drones, as it can increase their endurance. © Deutsches Zentrum für Luft- und Raumfahrt e.V. 2024.

关键词： controllers

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Vision-Based Modeling and control of Quadcopter for Uncooperative Ground Mobile Target Tracking 5th

Vision-Based Modeling and Control of Quadcopter for Uncooper...

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5th International Conference on Electrical Engineering and control Applications, ICEECA 2022

作者： Bouzerzour, Hamza Allam, Ahmed Guiatni, Mohamed Complex Systems Control and Simulators Laboratory Ecole Militaire Polytechnique BP 17 Bordj El Bahri Algiers16111 Algeria Control Process Laboratory Ecole Nationale Plytechnique Algiers Algeria

ISBN: (纸本)9789819700448

This paper proposes a novel approach based vision for modeling and controlling a Quadcopter UAV (QUAV) for uncooperative ground mobile target tracking applications. The proposed method is an Image-Based Visual Servoing (IBVS) approach using the dynamics of the target’s image features, calculated in a virtual camera as well as QUAV’s cinematic and dynamic model elaborated with respect to the virtual camera frame and the image’s features moments. A control scheme based on the Sliding Mode controller (SMC) is used to ensure the QUAV control in the presence of external disturbances and measurement uncertainties. Simulation results are presented to assess the proposed strategy under realistic conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Target tracking

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Observer-based stabilization for discrete nonlinear semi-Markov jump singularly perturbed models with mode-switching delay

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Science China(Information Sciences) 2025年第4期68卷 389-390页

作者： Wenhai QI Runkun LI Ju H.PARK Zheng-Guang WU Huaicheng YAN School of Engineering Qufu Normal University Department of Electrical Engineering Yeungnam University Institute of Cyber-Systems and Control Zhejiang University Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education East China University of Science and Technology

Semi-Markov jump systems(S-MJMs) not only characterize hybrid systems but also address the limitations of Markov jump systems(MJMs) [1–3]. Due to their ability to exhibit multi-time-scale features, singularly perturbed models(SPMs) effectively model practical systems influenced by multiple time-scale phenomena [4]. In this study, the observer-based output feedback controller is asynchronous with the original system due to the time delay in the controller mode switching. A nonlinear plant with singularly perturbed parameters(SPPs) is represented using an interval type-2(IT2) fuzzy model [5].

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Real-time power flow analysis and management for a long-endurance solar UAV during continuous flight

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CEAS Aeronautical Journal 2023年第4期14卷 1035-1049页

One of the primary challenges for Unmanned Aerial Vehicle (UAV) developers is to improve their endurance while in the air, as their typical flight time is limited to a few hours. One widely used technology to enhance their endurance is harnessing solar energy to power UAV and charge their batteries in flight. This article presents the development of a real-time simulation environment to enable the continuous flight of the Sky Sailor solar UAV. Through the utilization of a solar irradiance model, a solar panel model, real-time power generation is calculated. Simultaneously, the UAV’s full dynamic model, along with the autopilot and propulsion system model, is employed to determine the energy consumption at each moment. The energy management algorithm then compares the acquired energy with the power consumed, and in cases of surplus energy, it is efficiently stored in the battery for nighttime usage. Simulations show that continuous flight of the drone can be achieved during the summer season, when the daylight hours are long enough to fully charge the battery, With the ability to fly continuously during this period, the UAV can be used in various areas, such as monitoring forest fires, agricultural fields, beaches, and security monitoring for oil companies and their life bases in deserts. © 2023, Deutsches Zentrum für Luft- und Raumfahrt e.V.

关键词： Solar energy

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A Multi-Gas Detection System Based on Convolutional Neural Network 43

A Multi-Gas Detection System Based on Convolutional Neural N...

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43rd Chinese control Conference, CCC 2024

作者： Chen, Si-Wen Li, Hong-Yue Hou, Hui-Rang Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

The emission of carbon oxides (COx), nitrogen oxides (NOx), sulfur compounds, and volatile organic compounds (VOCs) from vehicles has significantly impacted the air quality, thus posing threats to the environmental resources and human health. This work presents the development of a non-dispersive infrared (NDIR) multi-gas detection system featuring three broadband light sources, a sealed gas chamber, and three multi-channel pyroelectric detectors with a response time shorter than 32 ms. The proposed system is used for constructing an odor dataset comprising five target gases, including carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), and propane (C3 H8). We perform odor recognition experiments using the traditional machine learning methods, as well as one-dimensional convolutional neural network (1D-CNN) and depthwise separable convolutional neural network (DS-CNN). The results show that the proposed DS-CNN model achieves an accuracy of 94.75% in recognizing different odors, thus outperforming other classification algorithms. This work demonstrates that the proposed detection system exhibits rapid response and precise recognition, thus establishing it as an effective approach for analyzing the primary components of automobile exhaust. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Air quality

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E-Nose-Based Domain-Generalization Recognition of Ignitable Liquids 43

E-Nose-Based Domain-Generalization Recognition of Ignitable ...

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43rd Chinese control Conference, CCC 2024

作者： Song, Tian-Shu Chen, Zhen-Peng Hou, Hui-Rang Li, Hong-Yue Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Detecting ignitable liquids (ILs) at the scene of a fire is crucial for fire investigation. The electronic nose (e-nose) is crucial for detecting ILs due to its affordability and rapid response time. process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network (FMG-Net). This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network (SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs (diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset. © 2024 Technical Committ

关键词： Convolutional neural networks

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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization 43

A Depression Level Classification Model Based on EEG and GCN...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Zhongyi Xu, Chenyang Jin, Licheng Hou, Huirang Meng, Qinghao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Electroencephalography (EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network (GCN) to identify levels of depression. Additionally, to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art (SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolution

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Superhydrophobic ceramic membrane coupled with a biphasic solvent for efficient CO_(2)capture

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Green Energy & Environment 2025年第4期10卷 834-844页

作者： Kaili Xue Zhen Chen Xiaona Wu Heng Zhang Haiping Chen Junhua Li State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Beijing Key Laboratory of Pollutant Monitoring and Control in Thermoelectric Production Process School of EnergyPower and Mechanical EngineeringNorth China Electric Power UniversityBeijing102206China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing102206China

An innovative strategy was proposed by integration of membrane contactor(MC)with biphasic solvent for efficient CO_(2) capture from flue *** accessible fly ash-based ceramic membrane(CM)underwent hydrophobic modification through silane grafting,followed by fluoroalkylsilane decoration,to prepare the superhydrophobic membrane(CSCM).The CSCM significantly improved resistance to wetting by the biphasic solvent,consisting of amine(DETA)and sulfolane(TMS).Morphological characterizations and chemical analysis revealed the notable enhancements in pore structure and hydrophobic chemical groups for the modified *** of wetting/bubbling behavior based on static wetting theory referred the liquid entry pressure(LEP)of CSCM increased by 20 kPa compared to pristine *** with traditional amine solvents,the biphasic solvent presented the expected phase *** experiments demonstrated that the CO_(2) capture efficiency of the biphasic solvent increased by 7%,and the electrical energy required for desorption decreased by 32%.The 60-h continuous testing and supplemental characterization of used membrane confirmed the excellent adaptability and durability of the *** study provides a potential approach for accessing hydrophobic ceramic membranes and biphasic solvents for industrial CO_(2) capture.

关键词： Carbon capture Membrane contactor Hydrophobic modification Membrane wetting Biphasic solvent

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