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Unknown system dynamics estimator-based impedance control for lower limb exoskeleton with enhanced performance

Control Theory and Technology 2024年第1期22卷 56-68页

作者： Wenhao Zhang Peng Song Mingying Wu Qiang Li Xinmin Mo Pingxin Ji Northwest institute of mechanical and electrical engineering Xianyang712099ShaanxiChina Xi’an Exo-Smart Dynamics Technology Ltd. Xi’an712046ShaanxiChina Department of mechanical and engineering Beijing Institute of TechnologyBeijing100081China

In this article, an unknown system dynamics estimator-based impedance control method is proposed for the lower limb exoskeleton to stimulate the tracking flexibility with the terminal target position when suffering parametric inaccuracies and unexpected disturbances. To reinforce the robust performance, via constructing the filtering operation-based dynamic relation, i.e., invariant manifold, the unknown system dynamics estimators are employed to maintain the accurate perturbation identification in both the hip and knee subsystem. Besides, a funnel control technique is designed to govern the convergence process within a minor overshoot and a higher steady-state precision. Meanwhile, an interactive complaint result can be obtained with the aid of the impedance control, where the prescribed terminal trajectory can be adjusted into the interaction variable-based target position by the force–position mapping, revealing the dynamic influence between the impedance coefficient (stiffness and damping) and the adjusted position magnitude. A sufficient stability analysis verifies the ultimately uniformly bounded results of all the error signals, and even the angle errors can be regulated within the predefined funnel boundary in the whole convergence. Finally, some simulations are provided to demonstrate the validity and superiority including the enhanced interaction flexibility and robustness.

关键词： Trajectory tracking Invariant manifold Funnel control Impedance control Lower limb exoskeleton

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Effect of Active–Passive Deformation on the Thrust by the Pectoral Fins of Bionic Manta Robot

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Journal of Bionic engineering 2024年第2期21卷 718-728页

作者： Yang Lu Shaomin Meng Cheng Xing Yiwei Hao Yonghui Cao Guang Pan Yong Cao School of Marine Science and Technology Northwestern Polytechnical UniversityXi’an710072China Unmanned Vehicle Innovation Center Ningbo Institute of NPUNingbo315103China State Key Lab of Fluid Power and Mechatronic Systems School of Mechanical EngineeringZhejiang UniversityHangzhou310027China zhejiang Key Laboratory of Advanced Manufacturing Technology School of Mechanical EngineeringZhejiang UniversityHangzhou310027China

Bionic manta underwater vehicles will play an essential role in future oceans and can perform tasks,such as long-duration reconnaissance and exploration,due to their efficient *** manta wings’deformation is evident during the swimming *** improve the propulsion performance of the unmanned submersible,the study of the deformation into the bionic pectoral fin is *** this research,we designed and fabricated a flexible bionic pectoral fin,which is based on the Fin Ray®effect with active and passive deformation(APD)*** APD fin was actively controlled by two servo motors and could be passively deformed to variable *** APD fin was moved at 0.5 Hz beat frequency,and the propulsive performance was experimentally verified of the bionic pectoral fins equipped with different extents of *** results showed that the pectoral fin with active–passive deformed capabilities could achieve similar natural biological deformation in the wingspan *** average thrust(T)under the optimal wingspan deformation is 61.5%higher than the traditional passive deformed pectoral *** obtained results shed light on the design and optimization of the bionic pectoral fins to improve the propulsive performance of unmanned underwater vehicles(UUV).

关键词： Manta ray Bionic pectoral fin Fin Ray®effect Active-passive deformation(APD)

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Experimental investigation of vibration pretreatment-microwave curing process for carbon fiber reinforced resin matrix composites

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Journal of Central South University 2024年第6期31卷 1838-1855页

作者： ZHANG De-chao ZHAN Li-hua MA Bo-lin YAO Shun-ming GUO Jin-zhan GUAN Cheng-long LIU Shu Light Alloys Research institute Central South UniversityChangsha 410083China College of mechanical and electrical engineering Central South UniversityChangsha 410083China School of mechanical engineering and Automation Fuzhou UniversityFuzhou 350108China

The vibration pretreatment-microwave curing process is an efficient,low energy consumption,and high-quality out-of-autoclave curing process for carbon fiber resin matrix *** study aims to investigate the impact of vibration pretreatment temperature on the fiber weight content,microscopic morphology and mechanical properties of the composite laminates by using optical digital microscopy,universal tensile testing machine and thermo-gravimetric ***,the combined mode of Bragg fiber grating sensor and temperature measurement fiber was employed to explore the effect of vibration pretreatment on the strain process during microwave *** study results revealed that the change in vibration pretreatment temperature had a slight impact on the fiber weight content when the vibration acceleration remained *** metallographic and interlaminar strength of the specimen formed at a vibration pretreatment temperature of 80℃ demonstrated a porosity of 0.414% and a 10.69% decrease in interlaminar shear strength compared to autoclave ***,the introduction of the vibration energy field during the microwave curing process led to a significant reduction in residual strain in both the 0°and 90°fiber directions,when the laminate was cooled to 60℃.

关键词： vibration microwave curing porosity interlaminar shear strength thermo-gravimetric analysis curing strain

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Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

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Journal of Energy Chemistry 2024年第3期90卷 120-132,I0005页

作者： Ziyou Zhou Yonggang Liu Chengming Zhang Weixiang Shen Rui Xiong State Key Laboratory of mechanical Transmission for Advanced Equipment&College of mechanical and Vehicle engineering Chongqing UniversityChongqing 400000China Department of Vehicle engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Department of electrical engineering Harbin Institute of TechnologyHarbin 150000HeilongjiangChina School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion *** main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation *** addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging *** incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV *** analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional *** validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 *** this level of precision for OCV estimation requires only around 50 s collection of partial charging *** validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed *** cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV *** method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.

关键词： Lithium-ion battery Open-circuit voltage Health diagnosis Deep learning

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Strategic Contracting for Software Upgrade Outsourcing in Industry 4.0

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Computer Modeling in engineering & Sciences 2024年第2期138卷 1563-1592页

作者： Cheng Wang Zhuowei Zheng College of mechanical engineering Zhejiang University of TechnologyHangzhou310014China Taizhou Research institute Zhejiang University of TechnologyTaizhou318001China

The advent of Industry 4.0 has compelled businesses to adopt digital approaches that combine software toenhance production efficiency. In this rapidly evolving market, software development is an ongoing process thatmust be tailored to meet the dynamic needs of enterprises. However, internal research and development can beprohibitively expensive, driving many enterprises to outsource software development and upgrades to externalservice providers. This paper presents a software upgrade outsourcing model for enterprises and service providersthat accounts for the impact of market fluctuations on software adaptability. To mitigate the risk of adverseselection due to asymmetric information about the service provider’s cost and asymmetric information aboutthe enterprise’s revenues, we propose pay-per-time and revenue-sharing contracts in two distinct informationasymmetry scenarios. These two contracts specify the time and transfer payments for software upgrades. Througha comparative analysis of the optimal solutions under the two contracts and centralized decision-making withfull-information, we examine the characteristics of the solutions under two information asymmetry scenarios andanalyze the incentive effects of the two contracts on the various stakeholders. Overall, our study offers valuableinsights for firms seeking to optimize their outsourcing strategies and maximize their returns on investment insoftware upgrades.

关键词： Software upgrade outsourcing the principal-agent information asymmetry reverse selection contract design

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Cooperative control for heterogeneous multi-agent systems: progress, applications, and challenges

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Science China(Information Sciences) 2024年第5期67卷 318-320页

作者： Bing YAN Peng SHI Jonathon CHAMBERS School of electrical and mechanical engineering Faculty of Science Engineering and TechnologyThe University of Adelaide School of engineering College of Science and Engineering University of Leicester

Cooperative control for heterogeneous multi-agent systems(MASs) is a critical and rapidly evolving field in modern automation and robotics. Characterized by the integration of agents with diverse capabilities and func... 详细信息

关键词： Multi agent systems

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Abnormal mechanochemical effect in ultraprecision machining of an additively manufactured precipitation-strengthened high-entropy alloy

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Journal of Materials Science & Technology 2024年第3期170卷 221-237页

作者： Long Xu Yuchao Bai Yunfa Guo Chang Ren Xiaohua Tan Yandong Jia Gang Wang Hao Wang institute of Materials Shanghai UniversityShanghai 200444China zhejiang institute of Advanced Materials Shanghai UniversityJiashan 314100China Department of mechanical engineering National University of SingaporeSingapore 117575Singapore

Recently,researchers have explored the use of precipitation strengthening and finer microstructures with high-density dislocations in additive manufacturing to produce high-entropy alloys(HEAs)with adjustable ***,the inherent surface roughness and lack of machinability research in AMed HEAs limit their engineering *** this study,we systematically investigated the microstructural characteristics,mechanical properties,and machinability of Fe_(29.3)Co_(28.7)Ni_(28.6)Al_(6.8)Ti_(6.6)(at.%)HEAs with three different structures:single FCC phase cellular(SPC),dual precipitation-strengthened(DPS),and single precipitation-strengthened(SPS).These structures were fabricated by selective laser melting and isothermally annealing at 780 and 940℃.Compared to SPC HEA,DPS HEA exhibits a significant increase in yield strength and ultimate tensile strength but with a dramatic sacrifice in *** HEA exhibits similar mechanical properties to SPC HEA due to the pronounced coarsening of L21 *** ultraprecision machining micro-cutting test showed that SPC HEA had a significant mechanochem-ical effect,as evidenced by a sharp drop in cutting force for inked workpieces,but not DPS *** abnormal finding was that the negligible reflection of cutting force for SPS HEAs suggested a negative mechanochemical effect,even though SPS HEA had equally excellent plasticity like SPC *** was found that nanocrystallization-induced strength enhancement and ductility reduction of SPS HEA lead to chips’deformation dominated by shear avalanche rather than chip folding of SPC HEA,which involves the reduction of surface energy and friction of chips’***,these results and our research findings may guide the machining of AMed precipitation-strengthened HEAs and accelerate their engineering ap-plication.

关键词： High-entropy alloy Selective laser melting Precipitation strengthening Machinability Mechanochemical effect

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Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

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Computers, Materials & Continua 2024年第1期78卷 1329-1343页

作者： Bowen Yu Chunli Xie College of mechanical and electrical engineering Northeast Forestry UniversityHarbin150040China

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment *** analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing *** defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall *** research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization *** proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical *** have conducted validation of the proposed method using datasets derived from gearbox and bearing *** empirical outcomes underscore the superior defect detection capability of our *** demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

关键词： Industrial defect detection deep learning intelligent manufacturing

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Multi-Time-Scale Resource Allocation Based on Long-Term Contracts and Real-Time Rental Business Models for Shared Energy Storage Systems

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 454-465页

作者： Yuxuan Zhuang Zhiyi Li Qipeng Tan Yongqi Li Minhui Wan College of electrical engineering Zhejiang UniversityHangzhouChina China Southern Power Grid Power Generation Company Energy Storage Research institute GuangzhouChina IEEE

The push for renewable energy emphasizes the need for energy storage systems(ESSs)to mitigate the unpre-dictability and variability of these sources,yet challenges such as high investment costs,sporadic utilization,and demand mismatch hinder their broader *** response,shared energy storage systems(SESSs)offer a more cohesive and efficient use of ESS,providing more accessible and cost-effective energy storage solutions to overcome these *** enhance the profitability of SESSs,this paper designs a multi-time-scale resource allocation strategy based on long-term contracts and real-time rental business *** initially construct a life cycle cost model for SESS and introduce a method to estimate the degradation costs of multiple battery groups by cycling numbers and depth of discharge within the ***,we design various long-term contracts from both capacity and energy perspectives,establishing associated models and real-time rental ***,multi-time-scale resource allocation based on the decomposition of user demand is *** analysis validates that the business model based on long-term contracts excels over models operating solely in the real-time market in economic viability and user satisfaction,effectively reducing battery degradation,and leveraging the aggregation effect for SESS can generate an additional increase of 10.7%in net revenue.

关键词： Capacity allocation long-term contracts shared energy storage system stochastic programming

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Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

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Journal of Energy Chemistry 2024年第5期92卷 605-618页

作者： Qingguang Qi Wenxue Liu Zhongwei Deng Jinwen Li Ziyou Song Xiaosong Hu College of mechanical and Vehicle engineering Chongqing UniversityChongqing 400044China School of mechanical and electrical engineering University of Electronic Science and Technology of ChinaChengdu 611731SichuanChina Department of mechanical engineering National University of SingaporeSingapore 117575Singapore

Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion *** numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery *** challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity *** fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field *** proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction ***,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation ***,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV ***,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 *** results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing *** work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

关键词： Electricvehicle Lithium-ion battery pack Capacity estimation Machine learning Field data

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