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International Conference on advanced systems and Electric Technologies (IC_ASET)

作者： Zouhaira Ben Mahmoud Adel Khedher LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d’Ingénieurs de Sousse Sousse Tunisia

A key limitation of the lookup table-based direct power control strategy is that the switching frequency varies with the distinct active and reactive powers combinations, resulting in an undesirable broadband harmonic spectrum range. The current study introduces a direct power control that is based on predictive approach (P-DPC) for grid-tied VSI. This approach is established through the fusion of the DPC technique and the predictive one, providing high dynamic performance and constant switching frequency. Rather than applying one voltage vector in the DPC scheme, the recommended P-DPC makes use the sequence of voltage vectors throughout a switching period. The strategy's principle involves two essential steps. The first one consists to determine the adequate sequence of vectors which contains zero and non-zero vectors and then to arrange them so that switching losses are kept to a minimal while still supervising the exchanged powers with the grid. Secondly, the power errors are eliminated by the prediction of the duration times. A grid-connected inverter model is used for numerical testing to validate the feasibility of the suggested P-DPC algorithm. The obtained results show high steady state and dynamic performances.

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Process planning and contour-based error compensation for precision grinding of miniature scalpels

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先进制造进展（英文版） 2024年第1期12卷 108-123页

作者： Cheng Fan Cao-Yang Xue Jun Zhao Wei Jiang Wen-Ge Han Lei Zhang Li-Ning Sun Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electrical EngineeringSoochow UniversitySuzhou 215021JiangsuPeople's Republic of China Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical and Electrical EngineeringZhejiang UniversityHangzhou 310027People's Republic of China State Key Laboratory of Intelligent Equipment and Manufacturing School of Mechanical EngineeringZhejiang University of TechnologyHangzhou 310023People's Republic of China

Miniature scalpels are mainly used in microsur-geries such as ophthalmic and cardiovascular *** size of a miniature scalpel is only a few millimeters,and the precision of the blade shape is high,which makes pro-duction of miniature scalpels extremely *** study proposes a new sharpening process for grinding miniature scalpels on a four-axis machine tool.A post-processing algo-rithm for a four-axis grinding machine based on a kinemat-ics model is *** then propose a corresponding parameter calibration method for the parameters used in the kinematics *** of possible errors in the param-eter calibration,a contour-based error compensation method is proposed for accurate adjustments to the edge shape fol-lowing *** can solve the problem of large devia-tions between the actual edge shape after grinding and the ideal edge *** effectiveness of the proposed process planning and error compensation method is verified experi-mentally,and the grinding process parameters of the minia-ture scalpel are optimized to improve its surface processing *** sharpness of the optimized miniature scalpel is less than 0.75 N,and the blade shape is symmetrical,which meets the technical requirements of miniature scalpels.

关键词： Miniature scalpel Sharpening Kinematics Error compensation Grinding

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Knapsack algorithm for data communication description and energy management in Internet of Things System: Smart Grid

Knapsack algorithm for data communication description and en...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Ferdaws Ben Naceur Achraf Jabeur Telmoudi Mohamed Ali Mahjoub LATIS - Laboratory of Advanced Technology and Intelligent Systems Sousse University National Engineering School of Sousse Sousse Tunisia LISIER Laboratory Higher National Engineering School of Tunis University of Tunis Tunis Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

A Smart-Grid (SG) represents an advanced electrical network designed for intelligent and efficient management across its entire infrastructure, facilitating seamless communication and coordination among its interconnected components, including IoT-enabled devices, and leveraging real-time data transfer mechanisms. This paper focuses on examining the local level within the SG, primarily tasked with monitoring energy consumption. To gain a comprehensive understanding of these concepts and the strategies employed for effective energy management, an energy management system has been devised. The primary objective of this system is twofold: first, to minimize the overall energy consumption within the SG, ensuring it remains within or below the received energy quantity; and second, to optimize the utility of appliances while ensuring they do not surpass the total energy capacity supplied by the Photovoltaic Panels (PVPs). To achieve this, we employ the Knapsack algorithm, wherein the locally produced energy serves as the algorithm's capacity, and the devices seeking energy consumption are treated as objects. Each device's weight, fixed consumption, and utility values are considered as attributes defining these objects, aiding in the algorithm's decision-making process. Through this approach, we aim to develop a robust energy management framework capable of efficiently allocating resources while maximizing overall utility within the SG ecosystem.

关键词： Energy consumption Costs Heuristic algorithms Production Predictive models Real-time systems Smart grids Classification algorithms Optimization Resilience

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Monolithically integrated triaxial high-performance micro accelerometers with position-independent pure axial stressed piezoresistive beams

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Microsystems & Nanoengineering 2023年第1期9卷 91-102页

作者： Mingzhi Yu Libo Zhao Shanshan Chen Xiangguang Han Chen Jia Yong Xia Xiaozhang Wang Yonglu Wang Ping Yang Dejiang Lu Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesXi’an Jiaotong University710049Xi’anChina Xi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and System Xi’an Jiaotong University710049Xi’anChina Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing 265503YantaiChina

With the increasing demand for multidirectional vibration measurements,traditional triaxial accelerometers cannot achieve vibration measurements with high sensitivity,high natural frequency,and low cross-sensitivity ***,for piezoresistive accelerometers,achieving pure axial deformation of the piezoresistive beam can greatly improve performance,but it requires the piezoresistive beam to be located in a specific position,which inevitably makes the design more complex and limits the performance ***,a monolithically integrated triaxial high-performance accelerometer with pure axial stress piezoresistive beams was designed,fabricated,and *** controlling synchronous displacements at both piezoresistive beam ends,the pure axial stress states of the piezoresistive beams could be easily achieved with position independence without tedious *** measurement unit for the z-axis acceleration was innovatively designed as an interlocking proof mass structure to ensure a full Wheatstone bridge for sensitivity *** pure axial stress state of the piezoresistive beams and low cross-sensitivity of all three units were verified by the finite element method(FEM).The triaxial accelerometer was fabricated and *** showing extremely high sensitivities(x axis:2.43 mV/g/5 V;y axis:2.44 mv/g/5 V;z axis:2.41 mV/g/5 V(without amplification by signal conditioning circuit))and high natural frequencies(x/y axes:11.4 kHz;z-axis:13.2 kHz)were *** approach of this paper makes it simple to design and obtain high-performance piezoresistive accelerometers.

关键词： axial proof independence

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State of Charge Estimation of Lithium-ion Batteries Using Long Short Term Memory Network in Electric Vehicle Applications

State of Charge Estimation of Lithium-ion Batteries Using Lo...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Marwa Gaich Sabeur Jemmali Bilal Manai LATIS- Laboratory of Advanced Technology and Intelligent Systems National School of Engineers of Sousse University of Sousse Sousse Tunisie Department of Electrical Engineering CEGEP de l'Outaouais Gatineau Québec Canada

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Precise State-of-Charge (SoC) estimation is crucial for accurate electric vehicle range prediction and efficient battery charge management. This paper explores a computationally efficient machine learning approach to address SoC-estimation-related issues. A Long Short-Term Memory (LSTM) network is utilized for predicting the SoC of lithiumion batteries, leveraging real-time data on temperature, voltage and current. The proposed LSTM model demonstrates remarkable performance, achieving a mean absolute error of 1.02%, as well as a 1.35% root mean squared error at 25°C, while ensuring low computational overhead, making it suitable for practical implementation in Battery Management systems (BMSs). This work lays a solid foundation for future advancements in BMS design, offering significant improvements in SoC prediction accuracy, reliability and efficiency. The findings suggest that LSTM networks can effectively model the complex dynamics of battery behavior, even when having varied operational conditions.

关键词： Accuracy Computational modeling Estimation Battery management systems Machine learning Voltage Electric vehicles State of charge Vehicle dynamics Long short term memory

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Explosive Electric Actuator and Control for Legged Robots

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Engineering 2022年第5期8卷 39-47页

作者： Fei Meng Qiang Huang Zhangguo Yu Xuechao Chen Xuxiao Fan Wu Zhang Aiguo Ming School of Mechatronical Engineering Beijing Institute of TechnologyBeijing 100081China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China Key Laboratory of Biomimetic Robots and Systems Ministry of EducationBeijing 100081China Department of Mechanical and Intelligent Systems Engineering The University of Electro-CommunicationsTokyo 182-8585Japan

Unmanned systems such as legged robots require fast-motion responses for operation in complex *** systems therefore require explosive actuators that can provide high peak speed or high peak torque at specific moments during dynamic *** hydraulic actuators can provide a large force,they are relatively inefficient,large,and *** electric actuators are incapable of providing instant high *** addition,the constant reduction ratio of the reducer makes it difficult to eliminate the tradeoff between high speed and high torque in a given *** study proposes an explosive electric actuator and an associated control method for legged ***,a high-power-density variable transmission is designed to enable continuous adjustment of the output speed to torque ratio.A heat-dissipating structure based on a composite phase-change material(PCM)is *** integral torque control method is used to achieve periodic and controllable explosive power *** experiments are conducted with typical legged robots to verify the effectiveness of the proposed actuator and control ***-legged,quadruped,and humanoid robots jumped to heights of 1.5,0.8,and 0.5 m,*** are the highest values reported to date for legged robots powered by electric actuators.

关键词： Electric actuator Variable transmission Integral torque control Legged robot

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Position Offset Injection Based Fast and Accurate Maximum Torque Per Ampere (MTPA) Control of Interior PMSMs 27

Position Offset Injection Based Fast and Accurate Maximum To...

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27th International Conference on Electrical Machines and systems, ICEMS 2024

作者： Tong, Zhe Wang, Fengyu Lu, Yuting Ding, Beichen Feng, Guodong School of Advanced Manufacturing Sun Yat-sen University Shenzhen China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou China

ISBN: (纸本)9784886864406

For the permanent magnet synchronous machine (PMSM), the maximum torque per ampere (MTPA) control is crucial to improve the torque capability and reduce the copper loss. However, the nonlinearities, such as magnetic saturation and temperature rise, make it challenging to identify the optimal current angle accurately during the online operation. This paper proposes a fast and accurate MTPA angle detection method using the small dc position offset injection. Through the positive and negative position offset injection, the objective function of MTPA control is derived based on the voltage equations, which is not affected by the parameter change and temperature rise. The objective function simplification is conducted to improve the computation efficiency. With the objective function, online polynomial curve fitting is utilized to find the MTPA angle rapidly. Through the comparison with existing methods and experiments on a laboratory PMSM drive, the effectiveness of the proposed method is validated. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Permanent magnets

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A Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

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IEEE Internet of Things Journal 2025年第11期12卷 17791-17806页

作者： Zeng, Hongjian Xiong, Yonghua She, Jinhua Yu, Anjun China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China Tokyo University of Technology School of Engineering Hachioji Tokyo192-0982 Japan Jiangxi Ganyue Expressway Co. Ltd Jiangxi Nanchang330000 China

Sparse Mobile Crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers' mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms. © 2014 IEEE.

关键词： Genetic algorithms

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One-Stage Object Detector Using Feature Fusion and Dual Attention 8

One-Stage Object Detector Using Feature Fusion and Dual Atte...

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8th IEEE International Conference on advanced Robotics and Mechatronics, ICARM 2023

作者： Zhang, Di Zhang, Weimin Li, Fangxing Liang, Kaiwen Yang, Yuhang Zhong, Zhide School of Mechatronical Engineering Beijing Institute of Technology China Beijing Institute of Technology Ministry of Education Key Laboratory of Biomimetic Robots and Systems China Beijing Advanced Innovation Center for Intelligent Robots and Systems China

ISBN: (纸本)9798350300178

Multi-scale object detection and small object detection are important tasks in computer vision and robotics. Traditional one-stage object detection algorithms are fast, but cannot adapt to changes in object scale perfectly and have low accuracy for small object detection. In this paper, we proposed a one-stage object detector using feature fusion and attentional mechanisms. This detector has better adaptability and higher accuracy with marginal extra cost. Our algorithm is based on the CenterNet framework, with modifications and enhancements. First, we used a feature pyramid network with bidirectional cross-scale connections to enable the full integration of lower layers and topmost features, which improved the network's adaptability to scale changes. Second, we proposed a dual attention module that integrates spatial attention and channel attention and added it to every layer of the pyramid, so that the network can focus more on useful information, which enhances the network's capability to detect small objects. Using a lightweight backbone, our algorithm achieves 40.2 mAP on COCO at 33 fps on a Titan Xp GPU, up to 3 mAP compared to CenterNet with the same backbone. Besides, for the small targets in COCO, our algorithm achieves 20.63 mAP, Which is 30% higher than CenterNet. We also tested our algorithm on a body part detection dataset of Pascal, Voc,which further demonstrates the effectiveness of our algorithm in terms of application. © 2023 IEEE.

关键词： Object detection

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Dynamic Obstacle Avoidance for Magnetic Helical Microrobots Based on Deep Reinforcement Learning

Dynamic Obstacle Avoidance for Magnetic Helical Microrobots ...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Qiu, Yukang Hou, Yaozhen Yang, Haotian Gao, Yigao Huang, Hen-Wei Shi, Qing Huang, Qiang Wang, Huaping Beijing Institute of Technology Intelligent Robotics Institute School of Mechatronical Engineering Beijing100081 China Harvard Medical School Laboratory for Translational Engineering CambridgeMA02139 United States Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China Ministry of Education Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798350372601

Magnetic helical microrobots hold immense promise in biomedical domains owing to their compact size and efficient propulsion capabilities. However, navigating these microrobots through dynamic and unstructured environments, particularly when encountering numerous dynamic obstacles, remains a formidable challenge. In the study, a control framework based on deep reinforcement learning (DRL) with the objective of guiding a microrobot through dynamic obstacles towards specified target goals is introduced. Initially, we design and fabricate a microdrill capable of propulsion via external magnetic rotating fields produced by our magnetic actuation system. Subsequently, we construct a custom training environment, adhering to the OpenAI gym interface, to serve as the simulator for training purposes. Utilizing the proximal policy optimization algorithm, we conduct training of the navigation policy within this simulator. Simulations and experimental validations conducted in dynamic environments affirms the efficacy of the proposed method. © 2024 IEEE.

关键词： Microrobots

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