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A Framework for Prescribed-Time Integral control Design of Feedback Nonlinear Systems

IEEE Transactions on Circuits and Systems II: Express Briefs 2024年第3期71卷 1281-1285页

作者： Chen, Xiandong Li, Jinbao Zhang, Xianfu Chen, Chih-Chiang Shandong Artificial Intelligence Institute Jinan250014 China School of Mathematics and Statistics Jinan250353 China Shandong University School of Control Science and Engineering Jinan250061 China National Cheng Kung University Department of Systems and Naval Mechatronic Engineering Tainan70101 Taiwan

This brief investigates the global prescribed-time regulation problem for a class of uncertain feedback systems subject to matched disturbances and modeling uncertainties with unknown and time-varying system parameters. New integral controllers consisting of an integral dynamic are proposed to make all states of the uncertain systems bounded and prescribed-time convergent to zero. First, by introducing an integral dynamic and a novel state transformation, the uncertain feedback systems with disturbances are converted into a class of generalized feedback nonlinear systems. Second, by virtue of the dynamic gain technique, the problem of designing prescribed-time regulating controllers for the generalized feedback nonlinear systems is transformed into that of designing a dynamic parameter for a class of new systems. At last, following the Lyapunov stability theorem and characteristic of the dynamic parameter, the boundness of the new systems yields the prescribed-time regulation of the generalized feedback nonlinear systems, which indicates the global prescribed-time regulation of the considered systems. The effectiveness of integral controllers is verified from simulation results in a PWM-based DC-DC buck converter. © 2004-2012 IEEE.

关键词： controllers

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Deep Learning Based Fault Diagnosis for Chemical Process with Statistical Feature Fusion 42

Deep Learning Based Fault Diagnosis for Chemical Process wit...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Lei Xia, Hao Cha, Bao School of Control Science and Control Engineering Dalian University of Technology Dalian116024 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610091 China

ISBN: (纸本)9789887581543

The existing fault diagnosis methods based on deep neural networks mostly rely on process data for fault classification and diagnosis, which makes it difficult to distinguish minor faults with similar data features. Therefore, this paper proposes a deep learning-based fault diagnosis model that combines statistical indicators and neural networks. By analyzing and calculating the relevant information entropy to screen the statistical signal features of time domain and frequency domain, we can extract more hidden information from the raw data, and use the convolutional autoencoder to compress the statistical indicators for feature extraction, and use CNN network to extract features from raw data. Finally, the two sets of features are fused and input into the LSTM network to obtain temporal information for fault diagnosis. Tests conducted on the Tennessee Eastman (TE) process show that the proposed model outperforms typical classifiers and improves the diagnostic accuracy of minor faults. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Failure analysis

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Research on Optimization Method of Injection Molding Machine Temperature control System Based on IK-SS

Research on Optimization Method of Injection Molding Machine...

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2024 China Automation Congress, CAC 2024

作者： Zou, Haipeng Ye, Quanxiang Li, Xiaoyu Kong, Xiangsong Zhao, Dan Shao, Zhijiang School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Zhangzhou Hongxingtai Electronics Co. Ltd China College of Control Science and Engineering State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798350368604

Injection molding, as a plastic processing technology, is widely used in the mass production of plastic products with high precision and automation. However, the performance optimization process of this system currently faces some prominent issues, such as time consumption, reliance on experience, and difficulty in achieving optimality. Traditional optimization methods typically rely on manual experience and rules, which are inefficient and time-consuming. To enhance the performance of the control system, this paper focuses on utilizing an improved simplex search method based on the data-driven optimization concept to optimize the parameters of the temperature control system of the injection molding machine. The method introduced in this paper is termed the Knowledge-Informed Simplex Search Method Based on Adjacent Historical Centroid Points Information (IK-SS). The IK-SS algorithm is a variant of the simplex search method that utilizes information from the centroids of neighboring simplices to evaluate the current state of the optimization process and dynamically adjust the iteration step size. This approach aims to improve optimization efficiency while reducing computational complexity and time costs. Simulation and experimental validation demonstrate that the IK-SS algorithm significantly enhances the temperature control performance of the injection molding machine (IMM). This demonstrates the potential of the IK-SS algorithm as a tool for optimizing the temperature control system of injection molding machines. © 2024 IEEE.

关键词： Molding

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Near-perfect replication on amorphous alloys through active force modulation based on machine learning/neural network parameter prediction

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science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 177-187页

作者： Senkuan Meng Zheng Wang Ruisong Zhu Ruijie Liu Jiang Ma Lina Hu Weihua Wang Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials(Ministry of Education) School of Materials Science and EngineeringShandong UniversityJinan 250061China College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060China Songshan Lake Materials Laboratory Dongguan 523808China Institute of Physics Chinese Academy of SciencesBeijing 100190China

As a microforming technique,micro/nano-structural replication possesses advantages of high precision and *** the remarkable superplasticity in the supercooled liquid region,amorphous alloys or metallic glasses(MGs)are regarded as ideal materials for miniature ***,due to the intrinsic metastable nature of supercooled liquids,the design of imprinting processes for MGs poses a *** the past,process parameters have largely relied on trial-and-error *** this work,a low-frequency active force modulation system is employed to apply a stable,precise,and real-time feedback stress field for imprinting of MG ***-frequency vibrations can facilitate the filling of microstructures on the template surface by reducing the effective viscosity of the supercooled *** the dataset composed of orthogonal experiments,a machine learning strategy based on back-propagation(BP)neural networks was utilized to construct a 3D parameter space for temperature,stress,and time,and to predict the corresponding filling ***,the optimal combination of imprinting process parameters was identified,and its filling ratio was experimentally validated to reach as high as *** near-perfect replication of microstructures confirms the superiority of the active force modulation system and the data-driven strategy of machine learning-assisted parameter *** the same time,this one-step microforming process provides a new approach to addressing the accuracy-cost trade-off dilemma in precision manufacturing.

关键词： metallic glass amorphous alloy microforming imprinting machine learning

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Intelligent Reflecting Surface Enabled in D2D Millimeter Wave Communication 6

Intelligent Reflecting Surface Enabled in D2D Millimeter Wav...

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6th IEEE International Conference on Smart Internet of Things, SmartIoT 2022

作者： Han, Bingjie Chen, Xin Guo, Dongchao Jiao, Libo School of Control Engineering Beijing Information Science & Technology University Beijing China School of Control Science Beijing Information Science & Technology University Beijing China

ISBN: (数字)9781665479523

ISBN: (纸本)9781665479523

We propose an intelligent reflecting surface (IRS) enabled millimeter wave (MMW) deviceto-device (D2D) communication in the future B5G/6G scenario to improve throughput. This scheme not only maintains the high transmission rate and low transmitting power of MMW D2D communication, but it also greatly reduces the blocking probability in the presence of assisting IRS. In this paper, we build the system model of IRS-enabled MMW D2D communication and convert it into an IRS activation and selection (IAS) problem which is to find the maximize effective transmission rate through the IRS distribution, then prove that the IAS problem is an NP-hard problem. In addition, we propose an approximation solution named after IAS-A to solve IAS problem efficiently. The experimental results show that the IRS-enabled D2D MMW communication outperforms other cases in terms of throughput and blocking probability, demonstrating the superiority of IAS-A algorithm. The experiment results show that our proposed IAS-A algorithm is 16.59% and 76.17% higher than the shortest distance and random strategies with respect to the total throughput. © 2022 IEEE.

关键词： Millimeter waves

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Design of Anti-interference Defibrillator with Short Delay 16

Design of Anti-interference Defibrillator with Short Delay

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16th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Meng, Fanchang Ding, Bozhi Wang, Jiali Xu, Feng Chen, Yuguo Li, Ke School of Control Science and Engineering Shandong University Jinan China Qilu Hospital Shandong University Jinan China

ISBN: (纸本)9798350327144

Ventricular fibrillation, atrial fibrillation, and other malignant arrhythmias are the main causes of human death. Defibrillator is an effective device to terminate abnormal heart rhythms and to deliver an electric shock to control heart activity. However, most existing defibrillation devices can only treat ventricular fibrillation and cannot handle abnormal rhythms such as atrial fibrillation that require synchronized defibrillation. A few devices that can perform synchronous defibrillation also have some obvious drawbacks, including significant synchronization time delay and limitations in reliability and anti-interference. In this study, we designed a novel synchronous defibrillator with high anti-interference and short delay for treating abnormal heart rhythms that require synchronous defibrillation. This defibrillator could collect electrocardiogram signals in real-time, recognize the R-waves, and release defibrillation energy at appropriate time. The discharge of this device occurred in the absolute refractory period of the heart, rather than during the ventricular vulnerable period near the peak of the T wave. The experimental results showed that this new synchronous defibrillator accurately recognized the R waves even in severe baseline drift and high-frequency interference situations. The synchronous defibrillator only took 1ms to recognize the R-wave and complete the discharge, significantly lower than the other commercialized defibrillators. This innovative defibrillator can be used in multiple scenarios of emergency and may promote the first-aid capacity for cardiovascular disorders. © 2023 IEEE.

关键词： Electrocardiograms

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Tuning the primary selective nanochannels of MOF thin-film nanocomposite nanofiltration membranes for efficient removal of hydrophobic endocrine disrupting compounds

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Frontiers of Environmental science & engineering 2022年第4期16卷 1-13页

作者： Ruobin Dai Hongyi Han Yuting Zhu Xi Wang Zhiwei Wang State Key Laboratory of Pollution Control and Resource Reuse Shanghai Institute of Pollution Control and Ecological SecuritySchool of Environmental Science and EngineeringTongji UniversityShanghai 200092China Tongji Architectural Design(Group)Co. Ltd.Environmental Engineering BranchShanghai 200092China

Metal organic framework(MOF)incorporated thin-film nanocomposite(TFN)membranes have the potential to enhance the removal of endocrine disrupting compounds(EDCs).In MOF-TFN membranes,water transport nanochannels include(i)pores of polyamide layer,(ii)pores in MOFs and(iii)channels around MOFs(polyamide-MOF interface).However,information on how to tune the nanochannels to enhance EDCs rejection is scarce,impeding the refinement of TFN membranes toward efficient removal of *** this study,by changing the polyamide properties,the water transport nanochannels could be confined primarily in pores of MOFs when the polyamide layer became ***,the improved rejection of EDCs was dependent on the water transport channels of the TFN *** low monomer concentration(i.e.,loose polyamide structure),the hydrophilic nanochannels of MIL-101(Cr)in the polyamide layer could not dominate the membrane separation performance,and hence the extent of improvement in EDCs rejection was relatively *** contrast,at high monomer concentration(i.e.,dense polyamide structure),the hydrophilic nanochannels of MIL-101(Cr)were responsible for the selective removal of hydrophobic EDCs,demonstrating that the manipulation of water transport nanochannels in the TFN membrane could successfully overcome the permeability and EDCs rejection *** results highlight the potential of tuning primary selective nanochannels of MOF-TFN membranes for the efficient removal of EDCs.

关键词： Porous metal organic framework Thin-film nanocomposite membrane Primary selective nanochannels Nanofiltration Endocrine disrupting compounds

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Efficient Real-Time Recognition Model of Plant Diseases for Low-Power Consumption Platform

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2040-2054页

作者： Deng, Songyun Wu, Wanneng Zou, Kunlin Qin, Hai Cheng, Lekai Liang, Qiaokang Hunan University College of Electrical and Information Engineering Changsha410082 China Changsha University of Science and Technology School of Electrical and Information Engineering Changsha410114 China Hunan University National Engineering Research Center for Robot Vision Perception and Control College of Electrical and Information Engineering Changsha410082 China

Recognition and early warning of plant diseases is one of the keys to agricultural disaster prevention and mitigation. Deep learning-based image recognition methods give us a new idea for plant disease identification. Due to the harsh conditions in agricultural environment, recent research has focused on exploring ways to lightweight the recognition model for deployment on low-power devices. In this article, we propose an efficient and feature-guided real-time plant disease recognition model with a multiclassifier architecture, specifically designed for low-power devices. By comparing with other advanced methods, our model reaches the state of the art in the combined metrics of recognition accuracy, the number of parameters, and inference speed. First, we propose AMI-NanoNet based on Roofline theory to significantly reduce the number of parameters and computational complexity. This model can achieve 99.8343% accuracy on PlantVillage by using a feature-guided curriculum learning with stepwise training strategy. Moreover, we design another training strategy suitable for lightweight ensemble models. Based on this strategy, our model only needs to integrate the classifiers at the end of the network to achieve 99.8708% identification accuracy, and it hardly increases the number of operations and parameters of the network. Extensive evaluations on this dataset demonstrate the effectiveness of our ensemble learning method. Furthermore, we tested our proposed methods on another dataset from other domains to validate its applicability to different scenarios. Overall, our research provides a basis for rapid and intelligent identification of plant diseases. © 2023 IEEE.

关键词： Real time systems

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

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Bipartite Consensus for Discrete-Time Signed Networks Subject to Saturation Constraints 5th

Bipartite Consensus for Discrete-Time Signed Networks Subje...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Chen, Baicheng Yao, Hui Du, Mingjun Yan, Zhiguo Shandong Jinan250353 China School of Control Science and Engineering Shandong University Shandong Jinan250061 China

ISBN: (纸本)9789811939976

The discrete-time bipartite consensus problem under signed network with saturation constraints is investigated. With the information of neighbors, a distributed control protocol is given. By the properties of the signed digraph, a model transformation is given. With the help of model transformation, the discrete bipartite consensus issue is converted into a discrete stability issue. By the Lyapunov stability theory, the discrete stability issues of the corresponding system are demonstrated. Through numerical simulation examples, we prove the validity of our results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Distributed parameter control systems

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