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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Jordan P. Sausen Carlos H. Barriquello Bruno K. Hammerschmitt Joelson L. Paixão Alzenira R. Abaide Paulo S. Sausen Center of Excellence in Energy and Power Systems (CEESP) Federal University of Santa Maria (UFSM) Santa Maria Brazil Group of Industrial Automation and Control (GAIC) Northwestern Regional University of Rio Grande do Sul (UNIJUI) Ijuí Brazil

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

This research addresses the growing importance of rechargeable battery technology in urban mobility solutions amid concerns over environmental impact. Electric Vehicles (EVs) emerged as a disruptive distributed energy resource, with the potential to revolutionize the electricity sector's dynamics. While private chargers remain popular, the availability of infrastructure for opportunity charging during business hours is crucial for promoting electric mobility, especially in emerging markets like Brazil. This study focuses on optimizing charging processes in smart parking facilities to meet recharging needs, reduce costs, and preserve battery life on bidirectional applications. Challenges related to uncoordinated EV charging and battery degradation are discussed, with a proposed comprehensive optimization model aiming to address these concerns while minimizing operational costs. The research highlights the importance of considering battery compensation to users in optimizing EV charging strategies.

关键词： Degradation Costs Europe Tariffs Automated parking Electric vehicle charging Batteries Distributed power generation US Department of Defense Vehicle dynamics

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Real-Time Marker Localization Learning for GelStereo Tactile Sensing

Real-Time Marker Localization Learning for GelStereo Tactile...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Liu, Shujuan Cui, Shaowei Zhang, Chaofan Cai, Yinghao Wang, Shuo University of Science and Technology Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences Institute of Automation Beijing100190 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665481090

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection/localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better realtime performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks. © 2022 IEEE.

关键词： Chemical detection

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Hydrodynamic Modeling and Parameter Identification of a Bionic Underwater Vehicle:RobDact

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Cyborg and Bionic systems 2022年第1期2022卷 164-176页

作者： Qiyuan Cao Rui Wang Tiandong Zhang Yu Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of A utomationChinese Academy of SciencesBeijingChina School of Artific ial Itelligence University of Chinese Academy of Sciences BeijingChina Center for Excellence in Brain Science and Itelligence Technology Chinese Academy of SciencesShanghaiChina

In this paper,the hydrodynamic modeling and parameter identifcation of the RobDact,a bionic underwater vehide inspired byDactylopteridae,are carried out based on computational fluid dynamics(CFD)and force measurement ***,thePaper briely describes the RobDact,then establishes the kinematis model and rigid body dynamics model of the RobDactaccording to the hydrodynamic force and moment *** CFD simulations,the hydrodynamic force of theRobDact at diferent speeds is obtained,and then,the hydrodynamic model parameters are ***,themeasurement platform is developed to obtain the relationship between the thrust generated by the RobDact and the inputfluctuation parameters,Finally,combining the rigid body dynamics model and the fin'thrust mapping model,thehydrodynamic model of the RobDact at diferent motion states is constructed.

关键词： dynamics(CFD) equations hydrodynamic

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Learning to Manipulate Tools Using Deep Reinforcement Learning and Anchor Information

Learning to Manipulate Tools Using Deep Reinforcement Learni...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Wei, Junhang Cui, Shaowei Hao, Peng Wang, Shuo The School of Future Technology University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Chinese Academy of Sciences Beijing100049 China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665481090

Endowing robots with tool manipulation skills helps them accomplish challenging tasks. While robots manipulate tools to achieve goals, the alignment of tools and targets is a noise-sensitive and contact-rich task. However, it is difficult to access the accurate pose of the tool and the target. When there is unknown noise in the observations, reinforcement learning can't be sure to perform well. In this paper, we define the easier-to-obtain accurate task-related information as anchor information and introduce a tool manipulation method based on reinforcement learning and anchor information, which can perform well when the observations include unknown noise. To evaluate the method, we build a simulated environment ToolGym, which includes four different kinds of tools and different noise sampling functions for each tool. Finally, we compare our method with baseline methods to show the effectiveness of the proposed method. © 2022 IEEE.

关键词： Reinforcement learning

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Visuotactile Feedback Parallel Gripper for Robotic Adaptive Grasping

Visuotactile Feedback Parallel Gripper for Robotic Adaptive ...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhang, Boyue Cui, Shaowei Zhang, Chaofan Hu, Jingyi Wang, Shuo School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665481090

In robot grasp and dexterous manipulation tasks, tactile sensing is important for the control adjustment of the manipulator. In this paper, we present a novel low-cost parallel gripper with high-resolution tactile sensing, named the GelStereo Gripper. Furthermore, an adaptive grasp strategy is proposed to endow the gripper with tactile-feedback grasp stability-maintaining ability. We install the gripper on our robot platform and conduct various grasp experiments by utilizing proposed control methods. Experimental results verify the reliability of the GelStereo gripper and also prove the effectiveness of the proposed strategy for experimental objects with different features. © 2022 IEEE.

关键词： Grippers

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Multi-Objective DER Planning Framework as a TEP Alternative: An Augmented Epsilon-Constraint Approach

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IEEE Transactions on Industry Applications 2025年

作者： Saber, Hossein Ranjbar, Hossein Mazaheri, Hesam Sharif University of Technology Center of Excellence in Power System Control and Management Electrical Engineering Department Tehran11365-11155 Iran University of Adelaide School of Electrical and Mechanical Engineering Adelaide Australia Texas A&M University Department of Electrical and Computer Engineering College StationTX United States

Recently, the construction of new transmission lines has faced extreme challenges due to ever-increasing environmental concerns and construction costs. Investing in distributed energy resources (DERs) offers a viable alternative to reduce reliance on new transmission lines. This study introduces a multi-objective bi-level DER planning model as an alternative to transmission network reinforcement. The upper-level problem aims to minimize the total cost of DERs, mitigate the congestion cost of the transmission network, and improve the system reliability while ensuring the profitability of the DER investment. In the lower-level problem, the wholesale electricity market is cleared to determine the optimal locational marginal prices and the operational status of the bulk power system. A scenario-based approach is employed to account for uncertainties in future loads, renewable generation, and generator bid prices. The bi-level problem is reformulated as a single-level optimization using the strong duality condition, with non-linear terms linearized through the big-M method and complementary slackness conditions. The augmented epsilon-constraint method is then employed to solve the multi-objective optimization problem. Finally, the effectiveness and applicability of the proposed DER planning model are evaluated using the IEEE 24-bus test system, offering insights from both system operators and DER investors. © 1972-2012 IEEE.

关键词： Multiobjective optimization

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Systematic control of Electro-hydraulic Velocity Servomechanism using Fractional Order Technique

Systematic Control of Electro-hydraulic Velocity Servomechan...

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International Conference on control, Automation and Diagnosis (ICCAD)

作者： Muhammad Majid Gulzar Fahad Usman Khan Daud Sibtain Adnan Shakoor Muhammad Khalid Department of Control & Instrumentation Engineering Department of Electrical Engineering University of Central Punjab Lahore Pakistan Energy Center of Excellence (ECoE) Harbin Electric International Harbin China Electrical Engineering Department Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

Electro-Hydraulic ServoMechanism (EHSM) is a nonlinear system with tentative dynamics, whose control is a difficult task. In this research, an alternative to the existing technique, a fractional order PID control-based approach, is presented. Initially, a SIMULINK based nonlinear model is constructed. The linear model is obtained by approximating the non-linearity using the bilinear transformation. Pole placement is blended with the Ziegler-Nichols tuning method to carry out the controller design and controller parameter tuning. The results are compared with conventional PID, and stability analysis is also analyzed. The results of the fractional order controller show satisfactory improvement than the results of conventional PID.

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Remote Operation with Haptic Force and Virtual Proxy for an Underwater Vehicle-Manipulator System 10

Remote Operation with Haptic Force and Virtual Proxy for an ...

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10th IEEE Data Driven control and Learning systems Conference, DDCLS 2021

作者： Ma, Jin Wang, Yu Wang, Rui Wang, Shuo University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Cas Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

ISBN: (纸本)9781665424233

This paper aims to investigate a smooth teleoperation method for the underwater vehicle-manipulator system. First, a coordinated mapping control method for the vehicle is presented. The haptic force is considered to help assist the operation. Then, two mapping modes are used to teleoperate the manipulator: when the end-effector needs to move in a large area, two virtual points and a spring-damping system are implemented to filter the operator's hand jitter and limit the manipulator's speed;when the end-effector needs to move in a small area, a position increment control method with a small proportional coefficient is used to improve the precision. Finally, the simulation demonstrates the effectiveness of the proposed teleoperation method. © 2021 IEEE.

关键词： Manipulators

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A Novel Two-Stream Model for Human Motor Characteristics Learning

A Novel Two-Stream Model for Human Motor Characteristics Lea...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xiang, Tian-Yu Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Yang, Hong-Jun Feng, Zhen-Qiu Hao, Jian-Long Hou, Zeng-Guang Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Information Shanxi University of Finance and Economics Taiyuan030006 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China Institute of Systems Engineering Macau University of Science and Technology The Joint Laboratory of Intelligence Science and Technology Taipa China

ISBN: (纸本)9781665481090

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios. © 2022 IEEE.

关键词： Convolution

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