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IEEE International Conference on engineering Technologies and Applied Sciences (ICETAS)

作者： Zied Ben Hazem Zafer Bingül Department of Mechatronics Engineering College of Engineering (COE) University of Technology Bahrain Bahrain Department of Mechatronics Engineering Automation and Robotics Laboratory Kocaeli University Kocaeli Turkey

This paper presents a study of the development of anti-swing control of a triple link rotary pendulum system (TLRPS). The motion nonlinear equations of the TLRPS are obtained using the Euler-Lagrange method based on the kinematic model and Denavit-Bartenberg (DH) convention. In this study three anti-swing controllers, linear-quadratic regulator (LQR), fuzzy linear-quadratic regulator (FLQR), and radial basis neuro-fuzzy linear quadratic regulator (RBNFLQR) are developed to control and eliminate the swing angles of the three links during the rotation. Moreover, the performance characteristics of the controllers are compared and examined in terms of their time response specifications parameters and swing angle elimination. Moreover, the robustness of controllers is tested under external torque disturbance. The simulation and experiment results of the response of the TLRPS are presented in time domains. Simulation and experiment results exhibit excellent control performance and robustness in terms of position control and damping oscillations elimination.

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A CFD Based Optimization of Natural Ventilation Strategy for Yungang Grottoes 8th

A CFD Based Optimization of Natural Ventilation Strategy for...

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Xu, Xinrui Yan, Hongbin Liu, Tingzhang Zhang, Shaoyou Gao, Lunrui Department of Automation College of Mechatronics Engineering and Automation Shanghai University Shanghai China Cultural Heritage Protection and Monitoring Center Yungang Research Institute Shanxi Datong China

ISBN: (纸本)9789819602933

Natural ventilation is not only an energy-saving ventilation method, but also the most common way to protect cultural relics. This study aims to explore the reasonable natural ventilation modes by switching doors and windows on the eave of cave 9 and cave 10, which are located in Yungang Grottoes. Using the data at 10:00 a.m. on August 21, 2023, 324 different natural ventilation strategies are carried out to simulate the humidity field and the velocity field through the CFD software FLUENT. Finally, the random forest algorithm is used to analyze the importance of doors and windows to the environment changes inside the cave. In conclusion, transom window 2, transom window 6 and door 1 are the main factors that influence the humidity field while transom window 2 and door 1 are also the decisive factors that affect velocity field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Caves

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Tailoring polysulfide trapping and kinetics by engineering hollow carbon bubble nanoreactors for high-energy Li-S pouch cells

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Nano Research 2021年第5期14卷 1355-1363页

作者： Lei Wang Shuangke Liu Jin Hu Xianan Zhang Xin Li Guanhua Zhang Yujie Li Chunman Zheng Xiaobin Hong Huigao Duan National Engineering Research Center for High Efficiency Grinding College of Mechanical and Vehicle EngineeringHunan UniversityChangsha410082China College of Aerospace Science and Engineering National University of Defense TechnologyChangsha410073China School of Mechatronics Engineering and Automation Foshan UniversityFoshan528225China

Despite great progress of lithium-sulfur(Li-S)battery performance at the laboratory-level,both key parameters and challenges at cell scales to achieve practical high energy density require high-sulfur-loading cathodes and lean ***,a novel carbon foam integrated by hollow carbon bubble nanoreactors with ultrahigh pore volume of 6.9 cm3·g−1 is meticulously designed for ultrahigh sulfur content up to 96 wt.%.Tailoring polysulfide trapping and ion/electron transport kinetics during the charge-discharge process can be achieved by adjusting the wall thickness of hollow carbon *** a further in-depth understanding of electrochemical reaction mechanism for the cathode is impelled by the in-situ Raman *** a result,the as-prepared cathode delivers high specific capacitances of 1,269 and 695 mAh·g−1 at 0.1 and 5 C,***,Li-S pouch cells with high areal sulfur loading of 6.9 mg·cm−2 yield exceptional practical energy density of 382 Wh·kg−1 under lean electrolyte of 3.5µL·mg−1,which demonstrates the great potential for realistic high-energy Li-S batteries.

关键词： hollow carbon bubble nanoreactors ultrahigh pore volume tunable wall thickness high energy density lithium-sulfur batteries

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A study of advanced mathematical modeling and adaptive control strategies for trajectory tracking in the Mitsubishi RV-2AJ 5-DOF Robotic Arm

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Discover Robotics 2025年第1期1卷 1-38页

作者： Ben Hazem, Zied Guler, Nivine Altaif, Ali Husain Automation and Sustainability Research Centre (ASRC) Department of Mechatronics Engineering College of Engineering (COE) University of Technology Bahrain Salmabad Kingdom of Bahrain Department of Computer Science University of Central Asia Naryn Kyrgyzstan

This study is focused on the advanced mathematical modeling and control strategies for a Mitsubishi RV-2AJ 5-DOF Robotic Arm, with an emphasis on achieving precision in trajectory tracking. Robotic trajectory tracking has long been challenged by the constraints of conventional modeling techniques and control methods. Prior contributions have been limited by the use of traditional dynamic models and classical controllers, which often failed to adapt to complex trajectory requirements. To address these challenges, a comprehensive dynamic model has been developed using Euler–Lagrange equations and validated through a comparison with a 3D CAD Simscape model in MATLAB/Simulink. For the control approach, a PID controller and an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller have been implemented to regulate the robotic arm’s joints. The ANFIS controller has been demonstrated to outperform the PID controller, achieving improved stability and adaptability in trajectory tracking. The robustness of the proposed system has been confirmed through simulation results for various trajectory types, including N-shaped, circular, and complex paths. This study underscores the importance of advanced modeling and hybrid control strategies in overcoming the limitations of traditional methods. By establishing a solid foundation in precise trajectory tracking through advanced mathematical modeling, this work contributes to the development of more adaptive and efficient robotic systems, with potential applications in industrial and research domains.

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PV Generation Monitoring Using Calculated Power Flow from μPMUS

PV Generation Monitoring Using Calculated Power Flow from μ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Hussain, K. Kaliappan, S. Arul Joseph Amalraj, M. Saini, Parvesh Nandha Kumar, S.K. Dhanraj, Joshuva Arockia Sharad Institute of Technology College of Engineering Department of Electrical Engineering Ichalkaranji416121 India Lovely Professional University Division of Research and Development Jalandhar144411 India Veltech Multitech Dr Rangarajan Dr Sakunthala Engineering College Department of Chemistry Chennai600109 India Graphic Era Deemed to Be University Department of Electrical Engineering Dehradun248002 India Psna College of Engineering & Technology Department of Electrical and Electronics Engineering Dindigul624622 India Hindustan Institute of Technology and Science Centre for Automation and Robotics Department of Mechatronics Engineering Chennai603103 India

ISBN: (纸本)9798350383348

The ability of PMUs to provide precise, synchronized readings of voltage, current and frequency has made them valuable for the observation of microgrids. In some microgrids, PMU s are utilized without a current transformer and only measure voltage phasor values. This research outlines a procedure to use μPMU (or micro-PMU) voltage readings to ascertain electric loads or photovoltaic (PV) production through gauging power flow (PF). The results of a study conducted at the Federal University of Paraná's Polytechnic School (UFPR) in Brazil demonstrated that utilizing the power flow calculated by a 'virtual CT' approach, as measured by a standard power meter and with a higher time resolution from a microPMU, is a reliable and efficient method for recognizing events, monitoring PV generation, and non-intrusively monitoring load (NILM). © 2024 IEEE.

关键词： microgrids power flow PV generation μPMU

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An Advanced System for Automatically Segregating Bolts using Thread Detection and Dot Matrix Marking.

An Advanced System for Automatically Segregating Bolts using...

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Global Conference for Advancement in Technology (GCAT)

作者： S Ramkumar M Sridharan G Nareshbabu R Giri Kanagaraj Venusamy R Priyadharshini Department of Robotics and Automation Rajalakshmi Engineering College Thandalam Chennai India Department of Mechatronics Engineering Rajalakshmi Engineering College Thandalam Chennai India

Packaging is a crucial stage in the mass production of components, especially when it comes to threaded components fabrication. Currently, the process of segregating bolts based on their length and thread presence is done manually, leading to numerous errors during packaging. To address this issue and achieve greater accuracy and perfection, we propose a digital segregation method using an electronic control module. By placing this module along the assembly line, it can automatically divide bolts according to their lengths and the presence of threads, eliminating the need for manual intervention and reducing human errors. Implementing this mechatronics system allows for more precise and accurate punching of bolts based on their size, resulting in high-quality packaging that takes into account various segregation factors.

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SSA-TCN-SVM based Short Wind Power Forecast Oriented to Dual Carbon Target

SSA-TCN-SVM based Short Wind Power Forecast Oriented to Dual...

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Power System and Green Energy Conference (PSGEC)

作者： Jianfang Li Li Jia Daogang Peng Yang Zhao Department of Automation College of Mechatronics Shanghai University Shanghai China School of Automation Engineering Shanghai University of Electric Power Shanghai China

Seeking green and low-carbon development has become a common consensus of the international community, and wind energy is one of the best new energy sources for the development of low-carbon economy. However, wind power has strong randomness and volatility, which will bring new challenges to the green and low-carbon operation of new power systems. Relying on the new power system, accurate power prediction of wind turbines can not only optimize the power supply structure of the power system, but also promote energy conservation, emission reduction and ‘low-carbon’ development of the power industry. Therefore, under the above background, this paper proposes a short-term wind power prediction model based on Singular Spectrum Analysis-Temporal Convolutional Network-Support Vector Machine (SSA-TCN-SVM). Aiming at the problem that the Temporal Convolutional Network (TCN) prediction model still has complex modes and the Dense Layer cannot set the weights reasonably, a cascade model based on Temporal Convolutional Network-Support Vector Machine (TCN-SVM) is adopted. The kernel function of Support Vector Machine (SVM) is used to map the vectors output by the convolution kernel channel of each time to the high-dimensional kernel space. The optimal weights are obtained by solving the dual problem, and the prediction ability of the single grouping sequence prediction model is improved. By minimizing the prediction error of the grouping sequence, the prediction accuracy of the whole prediction model is improved. Experiments show that SSA-TCN-SVM has better prediction performance.

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Auxiliary modelling error and probability density function based neuro-fuzzy short-term wind power prediction

Auxiliary modelling error and probability density function b...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Jianfang Li Li Jia Daogang Peng Rui Hou Department of Automation College of Mechatronics Shanghai University Shanghai China School of Automation Engineering Shanghai University of Electric Power Shanghai China

Accurate wind power forecasting not only optimizes the power supply structure of integrated energy system but also promotes the development of a low-carbon power industry. However, modeling and predicting wind power sequence using conventional forecasting methods can be hard due to its high volatility and intermittent nature. Thus this study proposes a hybrid model comprising the subsequent steps to improve the prediction accuracy. Firstly, the wind power sequence is denoised and the primary characteristics of the raw data are found using an ensemble empirical mode decomposition algorithm. Secondly, in order to achieve high precision, this paper propose an auxiliary modelling error and probability density function based Neuro-Fuzzy model (NFM) applied in wind power prediction. In particular, an innovative hybrid parameter identification algorithm is constructed by using auxiliary modelling error and probability density function (PDF) to identify parameters of the proposed NFM. This is auxiliary error converges to the invariant set based on Lyapunov’s global convergence, and then converting parameter updating into shape control of PDF for auxiliary modeling error, which follows a target pdf to update model parameters. The experiment demonstrates a considerable improvement in accuracy in comparison to the separate models.

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Design of Visual Adaptive Control System for Mobile Robot Based on Sliding Mode Speed Tracking 5th

Design of Visual Adaptive Control System for Mobile Robot Ba...

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5th International Conference on Intelligent Computing, Communication and Devices, ICCD 2019

作者： Gao, Qian Xiao, Wei College of Mechanical and Automation Engineering Hohai University Nanjing China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9789811558863

Aiming at the stabilization problem of mobile robot system in a complex environment, the 2D trifocal tensor visual system is used for the real-time estimation of robot position, which can enhance the robustness of robot visual servo system. On this basis, an adaptive control method is used to realize the motion control of mobile robot. The design of the sliding mode speed tracking controller makes the actual speed of mobile robot tend to the expected speed of the adaptive controller, to avoid the shake problem when the speed of mobile robot changes. The simulation results show that the proposed visual adaptive control system has high stability and robustness, and the sliding mode speed tracker is effective. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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automation of Financial Capital Supply Chain Optimization Industry Based on Time Series LSTM Algorithm

Automation of Financial Capital Supply Chain Optimization In...

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International Conference on Developments in eSystems engineering (DESE)

作者： Kangyi Li Yang Zhou Ulink College of Shanghai Shanghai China School of Mechatronics Engineering and Automation Shanghai University Shanghai China

With the development of global economic integration and the increase of economic uncertainty in the future, it is very necessary to predict the future capital of financial supply chain industry in order to ensure that capital optimization is within a controllable range in the current macroeconomic environment and current situation. This paper proposed an automated capital prediction strategy of capital supply chain using time series analysis artificial intelligence methods. First, to analyze the fluctuation and tail risk of financial characteristic, this paper explores the financial characteristic to measure the dynamic VaR from the perspectives of volatility, tail and peak with the Bayesian peaks over threshold (POT) model. Then, in order to make the modeling more refined, the forecast targets are split before the modeling with seasonal Autoregressive Integrated Moving Average (ARIMA) model and Prophet model. Finally, the time series modeling of wavelet Long Short-Term Memory (LSTM) model is carried out by two-part analysis method to get linear separated wavelet and non-linear embedded wavelet parts to predict the strong volatility financial capital. Taking the user capital flow of Yu’e Bao platform, the results prove the feasibility and prediction accuracy of the innovative proposed model.

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