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Distributed time-varying formation control with uncertainties based on an event-triggered mechanism

Science China(Information Sciences) 2021年第3期64卷 167-176页

作者： Xiaoduo LI Yumeng BAI Xiwang DONG Qingdong LI Zhang REN Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University Department of Electrical and Computer Engineering University of Wisconsin-Madison Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the distributed time-varying formation(TVF) problems for general linear multi-agent systems(MASs) subject to matched bounded uncertainties based on an adaptive event-triggered mechanism. A TVF protocol was designed with an event-triggered mechanism by introducing adaptive weights into the formation control protocol and triggering condition, and the large chattering phenomenon was avoided by the σ-modification adaptive law. According to the Lyapunov stability theory, proof has been established that the MASs in the presence of uncertainties can realize the expected formation that satisfies the given feasible condition. Finally, an example is provided to verify the effectiveness of the proposed algorithm.

关键词： formation control multi-agent systems event-triggered adaptive control

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Design and Implementation of Adaptive NMPC in industrial applications: case study

Design and Implementation of Adaptive NMPC in industrial app...

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International Carpathian control Conference (ICCC)

作者： Stepan Ozana Filip Krupa Jakub Nemcik Gergely Takacs Jakub Mozaryn Petr Beremlijski Abhaya Singh Aleksandra Kawala-Sterniuk Department of Cybernetics and Biomedical Engineering VSB-Technical University of Ostrava Ostrava Czech Republic Institute of Automation Measurement and Applied Informatics Slovak University of Technology in Bratislava Bratislava Slovak Republic Institute of Automatic Control and Robotics Warsaw University of Technology Warsaw Poland Department of Applied Mathematics VSB-Technical University of Ostrava Ostrava Czech Republic Department of engineering University of Sannio Sannio Italy Department of Electrical Power and Renewable Energy Opole University of Technology Opole Poland

This paper deals with the design and implementation of adaptive nonlinear predictive control (NMPC). The resulting control system is deployed on a softPLC embedded target in real-time via the REXYGEN control system. The paper describes functional prototype using softPLC hosting the NMPC algorithm applied for a nonlinear system consisting of three coupled tanks. The NMPC algorithm is solved via the receding horizon control method (RHC), formulated as a nonlinear programming problem (NLP) and numerically computed using the SOLNP solver. The adaptivity is provided by online identification via moving horizon estimation (MHE), keeping the model updated to avoid a model-plant mismatch. The methodology described in this paper can be easily transferred to a wider group of industrial nonlinear processes. Such created control unit can run in a standalone mode or can be connected to some exiting control system providing a necessary computational power and thus upgrading the quality of control significantly.

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WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equivalent Permittivity

WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equi...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Pengming Hu Weidong Yang Xuyu Wang Shiwen Mao Erbo Shen Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou College of Information Science and Engineering Henan University of Technology Zhengzhou China Knight Foundation School of Computing and Information Sciences Florida International University FL USA Department of Electrical and Computer Engineering Auburn University Auburn AL

Moisture content is one of the important indexes of food storage security. The existing detection methods are time-consuming and high cost such that it is difficult to realize online moisture detection. In this paper, according to the dielectric properties of wheat, we propose a wheat moisture content detection system with commercial Wi-Fi devices, termed WiWm-EP. First, we introduce the relationship between the moisture content of wheat and its dielectric constant. Then, we establish an equivalent permittivity (EP) model to characterize wheat moisture content, where the EP can be calculated from channel state information (CSI) of the dual antenna model. Besides, we build the fitting function between the EP and the moisture content as the wheat moisture detection model. Finally, we evaluate the performance of the system through different experiments. The average relative error of the detection results of five wheat samples with different moisture contents is less than 3%.

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Frequency Measurement Research with Weight Averaging of Pulse Output Signal of Voltage-to-Frequency Converter

SSRN

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SSRN 2022年

作者： Dorozhovets, Mykhaylo Pawlowski, Eligiusz Świsulski, Dariusz Department of Metrology and Diagnostic Systems Faculty of Electrical and Computer Engineering Rzeszow University of Technology ul. Wincentego Pola 2A Rzeszów35-959 Poland Department of Information and Measuring Technology National University Lviv Polytechnic Lviv Ukraine Faculty of Electrical Engineering and Computer Science Lublin University of Technology Lublin Poland Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdańsk Poland

The paper presents investigation of the efficiency of weight averaging of a pulse frequency modulated signal. The sampling effects and the interference influence in the input signal of the voltage-to frequency converter on the result of output frequency weight averaging are analyzed. A general form of the averaging result dependence on the used weighting function, signal value, interference level and modulation parameters is derived. In the case of high interferences whose frequencies are unstable or may change over a wide range, the specified level of their suppression, together with the reduction of the sampling effect, can be achieved using weighting functions, such as trigonometric, with an appropriate level of side lobes. The interference suppression dependently on frequency ranges using selected weighing functions are tested using both the simulation and experimental methods. The obtained results show very good convergence with the values calculated from theoretical formulas. © 2022, The Authors. All rights reserved.

关键词： Weighing

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Wind Power Forecasting Methods Based on Deep Learning:A Survey

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computer Modeling in engineering & Sciences 2020年第1期122卷 273-301页

作者： Xing Deng Haijian Shao Chunlong Hu Dengbiao Jiang Yingtao Jiang School of Computer Science Jiangsu University of Science and TechnologyZhenjiangChina School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjingChina Department of Electrical and Computer Engineering University of NevadaLas VegasUSA

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power *** to provide reference strategies for relevant researchers as well as practical applications,this paper attempts to provide the literature investigation and methods analysis of deep learning,enforcement learning and transfer learning in wind speed and wind power forecasting ***,wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state,which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure,temperature,roughness,and *** an effective method of high-dimensional feature extraction,deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design,such as adding noise to outputs,evolutionary learning used to optimize hidden layer weights,optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for *** establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness,instantaneity and seasonal characteristics.

关键词： Deep learning reinforcement learning transfer learning wind power forecasting

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Maximum Power Point Tracking Using Cross-Correlation Algorithm for Pv System

SSRN

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SSRN 2022年

作者： Abulanwar, Sayed Shahin, Ahmed Gad, Hesham H. Rizk, Mohammad E.M. Hu, Weihao Electrical Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Engineering Horus University Egypt

This paper suggests an online, rapid, dynamic, and pragmatic cross-correlation (CC) technique to eﬀectively realize maximum power point tracking (MPPT) of the photovoltaic (PV) array. The PV voltage and current are extracted, averaged, and rate of change of the power and terminal voltage dynamics are integrally cross-correlated to regulate the duty ratio of the converter. According to this approach, the maximum power point (MPP) is defned in terms of the polarity of the CC coefcient (rcc). If the CC coefcient is positive, the operating point is at left of the MPP, while a negative CC coefcient indicates that the operating point is located at right of the MPP, otherwise the MPP is attained zero CC coefcient with lower steady-state ﬂuctuations. In this regard, a tracking optimization can be realized with confned drift either under gradual or step solar irradiance changes. The proposed CC technique quickly converges to the MPP without identifcation of PV array parameters. Besides, the proposed CC tracking algorithm tracks MPP with wide-range small step-size iterations since the search area is restricted to the vicinity of the MPP rather than the entire PV curve. Moreover, the proposed technique is featured by robustness against system parameters and load variations with simple hardware implementation as compared to several existing techniques. The eﬀectiveness of the proposed technique is thoroughly explored via time-domain, experimental, and hardware-in-loop investigations and meanwhile compared to the two most popular techniques: P&O INC methods, for both gradual and step irradiance changes and also shadow eﬀects. © 2022, The Authors. All rights reserved.

关键词： Time domain analysis

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SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

arXiv

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arXiv 2023年

作者： Wang, Qi Chen, Li Zhan, Zhiyuan Zhang, Jianhua Yin, Zhong The Department of Control Science and Engineering the School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China The OsloMet Artificial Intelligence Lab Department of Computer Science Oslo Metropolitan University OsloN-0130 Norway The Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai200093 China

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website: https://***/7ohnKeats/SCVCNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

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Robust control of Feeding Speed in Coal Seam Drilling Process Considering Borehole Friction

Robust Control of Feeding Speed in Coal Seam Drilling Proces...

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Chinese Automation Congress (CAC)

作者： Xiao Liu Luefeng Chen Chengda Lu Haipeng Fan Min Wu Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Istanbul Turkey

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

During the coal seam drilling process, the drill string is subject to compressive deformation, compounded by unpredictable variations in formation hardness and borehole wall friction, leading to challenges in maintaining a stable feeding speed. This paper presents a novel approach by introducing uncertain parameters to describe the effects of formation hardness and borehole wall friction. Drill string axial movement model is modeled as a polyhedral system based on a lumped parameter representation. To meet industrial performance requirements, we design a robust $H_{\infty}$ controller to achieve consistent feeding speed control. Our simulation results demonstrate the controller's effectiveness in ensuring system stability despite fluctuations in formation hardness and drill string friction.

关键词： Drilling Robust control Friction Simulation Geology Velocity control Stability criteria Process control Coal Output feedback

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Joint Data Compression and Task Scheduling for LEO Satellite Networks

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IEEE Transactions on Vehicular technology 2025年

作者： He, Lijun Li, Shiyin Jia, Ziye Wang, Juncheng Han, Zhu China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Services Networks Shaanxi Xi'an710071 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210000 China Hong Kong Baptist University Department of Computer Science China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

We investigate a joint data compression and task scheduling problem for Low Earth Orbit Satellite Networks (LEOSNs), to maximize the sum weights of tasks while simultaneously minimizing the total data loss. First, we propose a novel Multi-Resource Conflict Graph (MRCG) model to characterize the intertwined communication and computation allocation conflicts inherent in typical data offloading processes of LEOSNs. Leveraging the proposed MRCG model, we then formulate the studied problem for LEOSNs as a linear integer program, to maximize the normalized weighted sum of both the sum task weight and the total data loss. We further explore the intrinsic structure of the linear integer program to propose an efficient solution using the Semi-Definite Relaxation (SDR) technique. Finally, the simulation results underscore that the synergistic optimization of data compression and task scheduling significantly facilitates the data offloading efficiency of LEOSNs, and the performances surpass existing benchmarks across a broad range of system parameters. © 1967-2012 IEEE.

关键词： Integer programming

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Actuator Fault Reconstruction for a Class of OSL Nonlinear Systems

Actuator Fault Reconstruction for a Class of OSL Nonlinear S...

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International Conference on Sciences and Techniques of Automatic control and computer engineering (STA)

作者： Omar Naifar Hamdi Gassara Mourad Elloumi Assaad Jmal Electrical Engineering Department Control and energy management laboratory (CEM lab) Sfax University Engineering National School Sfax Tunisia Laboratory of Sciences and Technology of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia

ISBN: (纸本)9781665482622

A single study has addressed actuator failure reconstruction for the One-sided Lipschitz (OSL) family of nonlinear systems. The predicted fault vector in that work does not provide any insight into the underlying problematic physical characteristics of the system, which is a significant shortcoming. In this work, we offer a way for estimating the incorrect physical parameters of actuators using an adaptive observer strategy. To demonstrate the utility of the suggested method, a numerical example and simulation research are provided.

关键词： Actuators Adaptation models Additives Computational modeling Observers Numerical models Nonlinear systems

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