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Global practical stabilization of the double integrator system with an imperfect sensor and subject to a bounded disturbance

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control Theory and Technology 2024年第3期22卷 468-478页

作者： Zhiyuan Zhu Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaP.0.Box 400743CharlottesvilleVA 22904-4743USA

This paper is concerned with global practical stabilization of the double integrator system with an imperfect sensor and subject to an additive bounded output *** imperfect sensor nonlinearity possesses the nonlinear characteristics of saturation and dead *** of the presence of output dead zone and the additive disturbance,the states cannot be expected to driven into an arbitrarily small neighborhood of the *** solve the global practical stabilization problem,we proposes a low gain-based linear dynamic output feedback law,under which the first state enters and remains in a bounded set whose size is depended on the bound of disturbance and the range of dead zone and the second state enters and remains in a pre-specified arbitrarily small set,both in finite *** results illustrate the effectiveness of our proposed control method.

关键词： Output saturation Disturbance rejection Output dead zone Low gain feedback-Linear output feedback Practical stabilization

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Development of a Simple Dielectric Equivalent Circuit Model from Polarization and Depolarization Current Measurements

Development of a Simple Dielectric Equivalent Circuit Model ...

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2024 IEEE IAS Industrial and Commercial Power system Asia, I and CPS Asia 2024

作者： Yutthagowith, Peerawut Nimsanong, Phethai Baba, Yoshihiro King Mongkut's Institute of Technology Ladkrabang School of Engineering Bangkok Thailand Power System Analysis Section Metropolitan Electricity Authority Department of Power System Control Bangkok Thailand Doshisha University Graduate School of Science and Engineering Kyoto Japan

ISBN: (纸本)9798350352290

Assessing insulation performance is vital for sustainable energy systems, which aim to minimize visual impact while ensuring reliability and resilience against weather-related disruptions. With increasing reliance on renewable energy sources, the importance of insulation performance becomes even more pronounced. Regular evaluations of insulation performance are critical for maintaining the safety, reliability, and resilience of electricity networks. By promptly identifying risks such as insulation degradation, utilities can improve maintenance practices, prioritize repairs, and enhance the integration of renewables, thereby boosting overall infrastructure performance. This paper introduces a straightforward and precise technique for determining the equivalent circuit parameters of insulation systems in high-voltage equipment. The proposed method facilitates the accurate determination of the dielectric loss factor from the equivalent circuit. To validate this approach, test cases were utilized to demonstrate its effectiveness. The results obtained through this method were then compared with those obtained using commercial software. The comparison reveals that the proposed approach results in a lower number of depolarization current branches compared to the commercial software. Also, it provides almost the same relative root mean square error and the lower root mean square relative error of the depolarization current. These findings underscore the appeal and effectiveness of the proposed method for determining the equivalent circuit of the dielectric model. © 2024 IEEE.

关键词： Depolarization

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Algorithm for Solving Traveling Salesman Problem Based on Self-Organizing Mapping Network

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 463-470页

作者：朱江辉叶航航姚莉秀蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System Ministry of EducationShanghai 200240China Huawei Technologies Co. Ltd.Shanghai 201206China

Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization *** on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter *** paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic *** show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm *** with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP.

关键词： traveling salesman problem(TSP) self-organizing mapping(SOM) combinatorial optimization neu-ral network

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A generative deep learning framework for inverse design of compositionally complex bulk metallic glasses

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npj Computational Materials 2023年第1期9卷 2196-2203页

作者： Ziqing Zhou Yinghui Shang Xiaodi Liu Yong Yang Department of Mechanical Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongChina City University of Hong Kong(Dongguan) 523000 DongguanChina College of Mechatronics and Control Engineering Shenzhen University518060 ShenzhenChina Department of Materials Science and Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongChina Department of Advanced Design and System Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongChina

The design of bulk metallic glasses(BMGs)via machine learning(ML)has been a topic of active research ***,the prior ML models were mostly built upon supervised learning algorithms with human inputs to navigate the high dimensional compositional space,which becomes inefficient with the increasing compositional complexity in ***,we develop a generative deep-learning framework to directly generate compositionally complex BMGs,such as high entropy *** framework is built on the unsupervised Generative Adversarial Network(GAN)algorithm for data generation and the supervised Boosted Trees algorithm for data *** studied systematically the confounding effect of various data descriptors and the literature data on the effectiveness of our framework both numerically and *** importantly,we demonstrate that our generative deep learning framework is capable of producing composition-property mappings,therefore paving the way for the inverse design of BMGs.

关键词： inverse metallic entropy

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Dynamic Modeling of Key Operating Parameters for Supercritical Circulating Fluidized Bed Units based on Data-Knowledge-Driven Method

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Journal of Thermal Science 2024年第3期33卷 1216-1230页

作者： YU Haoyang GAO Mingming ZHANG Hongfu CHEN Jiyu LIU Jizhen YUE Guangxi The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer EngineeringNorth China Electric Power UniversityBeijing102206China CHN Energy New Energy Technology Research Institute Co. Ltd.Beijing102209China Department of Energy and Power Engineering Tsinghua UniversityBeijing100084China

To address the pressing need for intelligent and efficient control of circulating fluidized bed(CFB)units,it is crucial to develop a dynamic model for the key operating parameters of supercritical circulating fluidized bed(SCFB)***,data-knowledge-driven dynamic model of bed temperature,load,and main steam pressure of the SCFB unit has been ***,a knowledge-driven method is employed to develop a dynamic model for key operating parameters of SCFB *** model parameters are determined based on the operating data of the unit and continuously optimized in real ***,Bidirectional Long Short-Term Memory combined with Convolutional Neural Network and Attention Mechanism is utilized to build the dynamic model of bed temperature,load,and main steam ***,a collaboration and integration method based on the critic weight method and the variation coefficient method is proposed to establish data-knowledge-driven model of key operating parameters for SCFB *** model displays great accuracy and fitting ability compared with other methods and effectively captures the dynamic characteristics,which can provide a research basis for the design of intelligent flexible control mode of SCFB unit.

关键词： circulating fluidized bed dynamic model data-knowledge-driven method deep learning power plant

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Relationship Between the Number of Agents and Sparse Observability Index

IEEE Open Journal of Control Systems

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IEEE Open Journal of control systems 2025年 4卷 144-155页

作者： Shinohara, T. Namerikawa, T. KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm100 44 Sweden Keio University Department of System Design Engineering 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan

The state estimation problem in the presence of malicious sensor attacks is commonly referred to as a secure state estimation problem. Central to addressing this problem is the concept of the sparse observability index, defined as the largest integer δ for which the system remains observable after the removal of any δ sensors. This index plays a critical role in quantifying the resilience of the system, as a higher δ enables unique state reconstruction despite the presence of more compromised sensors. In this study, for undirected multi-agent systems consisting of n agents, we analyze the relationship between the number of agents n and the sparse observability index δ for effective secure state estimation. In particular, we consider four typical graph structures: path, cycle, complete, and complete bipartite graphs. Our analysis reveals that δ does not increase monotonically with n, and that resilience is intricately tied to the underlying network structure. Notably, we demonstrate that the system exhibits enhanced resilience when the number of agents n is a prime number, although the specifics of this relationship vary depending on the graph topology. © 2022 IEEE.

关键词： Multi agent systems

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Fault Detection in Electric Drives Based on LSTM Autoencoder Model Machine Learning Approach 25

Fault Detection in Electric Drives Based on LSTM Autoencoder...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Demidova, Galina Privalov, Daniil Semenov, Denis Lukichev, Dmitry Liu, Zhitao Anuchin, Alecksey Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Electric Drives Moscow Power Engineering Institute Moscow Russia

ISBN: (纸本)9798350389234

Fault detection in electric drives is crucial for ensuring operational reliability and minimizing downtime. This paper provides a brief overview of the methods based on machine learning used for fault detection in electric drives. Additional this research presents a machine learning approach that utilizes the Long Short-Term Memory Autoencoder model for fault detection deal with bearing faults. A flowchart illustrating the implementation steps of the Long Short-Term Memory Autoencoder algorithm is included. Additionally, a case study on bearing faults in an electric drive system is presented to demonstrate the advantage of the proposed approach. By training the Long Short-Term Memory Autoencoder on operational data, accurate prediction of bearing faults is achieved, enabling timely maintenance interventions. It is shown that in the 7th epoch of training, the model achieved a notably high accuracy more than 99%. The results highlight the potential of Long Short-Term Memory Autoencoder models as a valuable tool for fault detection in electric drives, thereby improving system reliability and performance. © 2024 IEEE.

关键词： Machine learning

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Novel Method for Selective and controlled Online Mass Removal Using Laser Beam for In Situ Balancing of Flexible Rotor Bearing system 16th

Novel Method for Selective and Controlled Online Mass Remova...

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16th International Conference on Vibration engineering and Technology of Machinery, VETOMAC 2021

作者： Wankhede, A.K. Shaik, Karimulla Singh, Munendra Sharma, Archana Fernandis, B.G. Department of Engineering Sciences HBNI Anushakti Nagar Mumbai India BARC Mumbai Trombay India Control System Development Division BARC Mumbai Trombay India Department of Electrical Engineering IITB Mumbai Powai India

ISBN: (纸本)9789819989850

Balancing flexible rotors is a tedious process as compared to rigid rotor bearing systems due to shaft bending. Flexible rotors are allowed to run near their bending critical speeds and (stops) prevent the system from balancing from correction planes. Prediction of unbalance accurately has been the topic of research for a long time. To avoid the difficulties of present time-consuming balancing procedures, a new methodology was developed to balance rigid and flexible rotors. This paper presents a new methodology where in situ balancing is possible to perform by removal of mass from the rotor which is running in vacuum using a LASER beam. An experimental test setup was developed to demonstrate the mass removal from an overhung flexible rotor, which is running in vacuum. An electronics system for the measurement of position and synchronization of the LASER beam with the rotor speed is demonstrated. The method of generation of synchronization pulse and desired arc so as to fire the LASER beam in synchrony with the rotating disc is also explained. The system trial with various operating speeds is conducted and the quantity of mass removal and resolution is calculated. The experimental results are also presented with the identification of critical issues to be addressed for application in actual rotor systems for in situ balancing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Laser beams

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Motor Fault Diagnosis Under Complex Working Conditions Based on Signal Fusion and CA-CNN

Motor Fault Diagnosis Under Complex Working Conditions Based...

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2024 China Automation Congress, CAC 2024

作者： Cai, Mingxuan Liu, Qi Liu, Yuxiang Yang, Bo Department of Automation Shanghai Jiao Tong University shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350368604

Accurate fault diagnosis plays a key role in the safe and efficient operation of electrical motors. Aiming at multi-class motor fault diagnosis under complex working conditions, a signal fusion-based fault diagnosis framework is proposed. Measurement parameters from different sensors are first transformed by wavelet denoising and fast Fourier transform (FFT). Then, multiple parameters spectrum and working condition information are integrated and converted into high-dimension matrices. Finally, a channel-attention-based convolution neural network (CA-CNN) is proposed to extract fault patterns from a reconstructed matrix and output diagnosis results. Experiment results on both simulated and experimental datasets verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Convolutional neural networks

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A joint sparse Bayesian learning for wideband DOA estimation

A joint sparse Bayesian learning for wideband DOA estimation

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2024 Conference on Spectral Technology and Applications, CSTA 2024

作者： Zhao, Yonghong Liu, Jisong Wang, Junlong Dong, Shuxin Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing Department of Automation and Information Engineering Xi'an University of Technology Shaanxi Xi'an710048 China

ISBN: (纸本)9781510683082

Wideband direction of arrival (DOA) estimation using sensor array is a noteworthy problem frequently occurring in many applications involving radar, sonar, and communication. We present a wideband DOA method based on a sparse Bayesian learning of multiple sub-bands jointly with the redundant dictionary matrix at the reference frequency. Explicitly enforcing the focusing operation and the singular value decomposition (SVD) to sensor measurements is motivated by a desire to decrease the computational complexity and storage capacity. A joint sparse Bayesian learning framework is proposed to deal with multiple sub-bands under one segment to obtain the DOA information. Moreover, the proposed method can be directly extended to multiple segments. Comparing to other wideband DOA estimation methods, the proposed method has the advantages of increased resolution, higher accuracy, and correlation of the sources, as well as not requiring the initial angles for the focusing matrix. Simulations are provided to show the effectiveness and improved performance of the proposed method. © 2024 SPIE.

关键词： Direction of arrival

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