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Simultaneous Estimation of Human Motion Intention and Time-Varying Arm Stiffness for Enhanced Human-Robot Interaction

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 510-524页

作者： Wu, Huayang Zhu, Chengzhi Cheng, Long Yang, Chenguang Li, Yanan University of Sussex Department of Engineering and Design BrightonBN1 9RH United Kingdom South China University of Technology Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering Guangzhou510640 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China University of Liverpool Department of Computer Science LiverpoolL69 3BX United Kingdom

Recent advances in physiological human motor control research indicate that human endpoint stiffness magnitude increases linearly with grasp force. Based on these findings, a scheme was proposed in this paper to integrate the linear quadratic estimation (LQE) filter with the stiffness model inferred from grasp force, which can simultaneously estimate the human arm's stiffness and motion intention. Then, an online variable impedance controller (VIC) was designed based on previous estimations for physical human-robot interaction (pHRI). The proposed stiffness model and estimation method were validated through experiments using a planar robotic interface. In order to assess its performance in practical pHRI tasks, the implementation of human arm stiffness and intention estimation combining with VIC was extended to a teleoperation peg-in-hole and robot-assisted rehabilitation tasks. The experimental results demonstrate that the proposed method can effectively estimate human motion intention and arm stiffness simultaneously. Compared to existing methods, the proposed VIC enhances pHRI in terms of increased flexibility, effective guidance, and reduced human effort. © 2016 IEEE.

关键词： Human robot interaction

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Auxiliary Control Method for Reducing Vibration of Drive Train of Wind Turbine Caused by Primary Frequency Regulation 3

Auxiliary Control Method for Reducing Vibration of Drive Tra...

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3rd International Conference on Advanced Electrical and Energy Systems, AEES 2022

作者： Bai, Xuechen Wang, Yingwei Sun, Han University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada The School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

ISBN: (纸本)9781665472081

The primary frequency regulation of the wind turbine can reduce the frequency deviation of the power system to improve the stability of the frequency. However, the primary frequency regulation increases the vibration of the WT's drive train. In this study, auxiliary damping control is proposed to mitigate the vibration of the drive train caused by primary frequency regulation. Firstly, the influence of frequency regulation on the torque of the drive train is analyzed. Then an additional damping control method is proposed, and the influence of the control parameters on the control effect is analyzed. Finally, the effectiveness of the control method is verified by simulation under different wind speeds. The results show that under the same frequency performance, the proposed method can reduce the standard deviation of the drive train torque by more than 50%. © 2022 IEEE.

关键词： Wind turbines

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Output Constrained Adaptive Controller Design for Nonlinear Saturation Systems

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 441-454页

作者： Yongliang Yang Zhijie Liu Qing Li Donald C.Wunsch the Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China the Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA

This paper considers the adaptive neuro-fuzzy control scheme to solve the output tracking problem for a class of strict-feedback nonlinear *** asymmetric output constraints and input saturation are *** asymmetric barrier Lyapunov function with time-varying prescribed performance is presented to tackle the output-tracking error constraints.A high-gain observer is employed to relax the requirement of the Lipschitz continuity about the nonlinear *** avoid the"explosion of complexity",the dynamic surface control(DSC)technique is employed to filter the virtual control signal of each *** deal with the actuator saturation,an additional auxiliary dynamical system is *** is theoretically investigated that the parameter estimation and output tracking error are semi-global uniformly ultimately *** simulation examples are conducted to verify the presented adaptive fuzzy controller design.

关键词： Asymmetric barrier Lyapunov function input saturation Nussbaum function time-varying prescribed performance

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Autoencoder and Teaching-learning-based Optimizer for Mobile Edge Computing System Optimization Problems

Autoencoder and Teaching-learning-based Optimizer for Mobile...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Xu, Dian Zhou, Mengchu Yuan, Haitao Institute of Systems Engineering Macau University of Science and Technology Macao China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark United States School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350337020

By using an autoencoder as a dimension reduction tool, an Autoencoder-embedded Teaching-Learning Based Optimization (ATLBO) has been proved to be effective in solving high-dimensional computationally expensive problems through several widely used function problems. However, the following two crucial issues have not been resolved, 1) ATLBO should be verified by solving real-life optimization problems;and 2) how autoencoder parameters and structures impact AEO's performance. In this work, ATLBO is verified by an energy consumption minimization problem (ECM) in mobile edge computing systems. To design an effective autoencoder for ATLBO, this work proposes a parameter tuning optimization strategy for autoencoders. By using the proposed Autoencoder Parameter Tuning (APT) strategy, ATLBO can enjoy higher robustness than those without it. The experimental results show that it is three to six times better than state-of-the-art methods in solving ECM. We consider the strategy-induced overhead and take the execution time as the primary criterion to evaluate them. In addition, the experimental results show that, against the conventional wisdom that higher-accuracy auto encoders bring higher system performance, lower-accuracy ones can actually assist ATLBO in locating the best solutions. This work promotes a novel application of autoencoders in optimization theory and practice. © 2023 IEEE.

关键词： autoencoder Autoencoder-embedded teaching-learning based optimization (ATLBO) High-dimensional expensive problems (HEPs) Mobile edge computing systems

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Improving the Educational Value of Operating Systems 23

Improving the Educational Value of Operating Systems

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Sava, Cezar Deaconescu, Razvan Kroning, Martin Monti, Antonello Turcanu, Dinu POLITEHNICA of Bucharest Computer Science and Engineering Department Bucharest Romania RWTH Aachen University Institute for Automation of Complex Power Systems Aachen Germany Fraunhofer Center Digital Energy Fraunhofer FIT Aachen Germany University of Moldova Department of Software Engineering and Automatics Technical Chişinǎu Moldova

ISBN: (纸本)9798331540388

Educational operating systems are crucial for facilitating learning in the digital age, as they exist at the intersection of technology and education. While their technical role is well understood, improving their educational value requires enhancing traditional approaches. This paper explores ways to enrich educational operating systems not just through technological advancement, but also by integrating pedagogical principles. In an ever-changing world, the need for an adaptive and responsive educational operating system to meet diverse student needs is crucial. Through case studies, we analyze how design, interactivity, and accessibility can transform these systems into effective and appealing learning environments. We propose a series of recommendations for the future development of educational operating systems, focusing on enhancing the learning experience and promoting student autonomy, thereby offering a fresh perspective on how technology and education can blend harmoniously and efficiently. © 2024 IEEE.

关键词： Contrastive Learning

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Multiobjective Sequential Transfer Optimization: Benchmark Problems and Preliminary Results 13

Multiobjective Sequential Transfer Optimization: Benchmark P...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Xue, Xiaoming Feng, Liang Yang, Cuie Liu, Songbai Song, Linqi Tan, Kay Chen City University of Hong Kong Department of Computer Science Hong Kong College of Computer Science Chongqing University Chongqing China Northeastern University State Key Lab of Synthetic Automation for Process Industries Shenyang China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350308365

In cases of frequent problem-solving of multiobjective optimization tasks from a domain due to changing conditions or problem features, a growing number of individual tasks will be solved and stored in a database, providing an opportunity for a target task at hand to achieve better optimization performance through knowledge transfer from the previously-solved tasks, which is also known as sequential transfer optimization. Despite a variety of transfer algorithms that have been developed over the years, the research on the design of benchmark problems for evaluating such algorithms received far less attention. Oftentimes, the source and target tasks in existing test problems are manually assembled or extended from specific practical problems, limiting their ability to represent the diverse yet complex source-target similarity relationships in real-world problems. In light of this, we propose design methods to generate multiobjective sequential transfer optimization problems (MSTOPs) systematically in this work, wherein the Pareto manifolds of individual tasks and the manifold-based similarity between the tasks can be customized with ease, enabling a broad spectrum of representation of the diverse similarity relationships between the source-target Pareto manifolds of MSTOPs. Lastly, a benchmark suite with 12 test problems is developed using the proposed methods, which would serve as an arena for electing superior multiobjective sequential transfer optimization algorithms. The source code is available at https://***/XmingHsueh/MSTOP. © 2024 IEEE.

关键词： Optimization algorithms

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Adaptive rapid defect identification in ECPT based on K-means and automatic segmentation algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 1-18页

作者： Huang, Xuegang Yin, Chun Dadras, Sara Cheng, Yuhua Bai, Libing Hypervelocity Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang621000 China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Electrical and Computer Engineering Department Utah State University LoganUT84321 United States

To enhance the detection efficiency in eddy current pulsed thermography, an adaptive feature extraction algorithm for defect identification is developed in this paper. The proposed algorithm involves four stages, namely, the thermal image segmentation, the variable interval search, the distance correlation clustering analysis and the between-class distance decision making. The transient thermal responses (TTRs) with similar characteristics are collected into one data block. The thermal image segmentation and variable interval search can help reduce the repetitive calculation in defect identification by choosing local optimums in each data block. The global optimum that has the largest sum of the between-class distance, is derived by first classifying the local optimums and then calculating the correlation distance of the thermal responses with the center points of each class. Finally, the TTRs with the largest between-class distance are regarded as the typical ones which can be used to identify the discriminative defect features of infrared image sequence. Finally, the comparison experiments are carried out to demonstrate the effectiveness and advantages of the proposed approach. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： K-means clustering

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Exploring the Cutting-Edge of Energy Aggregation Approaches and Business Models 20th

Exploring the Cutting-Edge of Energy Aggregation Approaches ...

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20th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2023

作者： Bwalya, Danny Azevedo, Manuel Corchado, Emilio S. PH Energía Porto4150-044 Portugal Institute of Engineering – Polytechnic of Porto Porto4249-015 Portugal Department of Computer Science and Automation University of Salamanca Salamanca Spain

ISBN: (纸本)9783031383175

Energy aggregation business models play a crucial role in maximizing the benefits of renewable energy generation and consumption. In this context, this paper presents an examination of energy system models and their correlation with aggregation business models, emphasizing algorithmic approaches. The distinct characteristics of energy system models in comparison to traditional business models are elucidated. The study encompasses diverse model types employed in the energy sector, including optimization, classification, clustering, and integrated assessment models. Additionally, it investigates the algorithms utilized in aggregation business models, such as demand response, virtual power plants, and peer-to-peer energy trading, highlighting both their advantages and challenges. The paper concludes that the advancement of energy aggregation business models holds significant promise in addressing the intricate complexities of contemporary power systems and the ongoing energy transition. Future research endeavors involve the exploration of Electroencephalography (EEG) techniques within energy aggregation models, inspired by the information processing mechanisms of the human brain. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart power grids

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A Supercapacitor and Fuzzy-PID Controller-based Active Charge Balancing Scheme for Lithium-ion Batteries 48

A Supercapacitor and Fuzzy-PID Controller-based Active Charg...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Samanta, Akash Sharma, Mohit Williamson, Sheldon Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada Weld Aid Automation Kolkata India

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

A hybrid Fuzzy proportional-integral-derivative (Fuzzy-PID)-based control algorithm is introduced for a non-dissipative (active) charge equalization scheme consisting of a supercapacitor and two-stage bidirectional DC-DC converter. The proposed control algorithm intelligently modulates the balancing parameters while realizing the cell-to-pack-to-cell (C2P2C) balancing of a lithium-ion battery pack. The charge imbalance and charging/discharging profile are considered by the Fuzzy-logic algorithm to determine the balancing current that not only enhances the balancing speed but also ensures the operation under a safe operating region and a longer battery life. Accurate determination of cell state of charge (SOC) is highly challenging, here the proposed Fuzzy-based control algorithm eliminates the necessity of accurate SOC determination that eventually reduces the computational cost and necessity of highly precise sensors. The proposed topology requires fewer active components due to the utilization of C2P2C balancing, resulting in a simple control algorithm and lower implementation cost. The supercapacitor is used as an energy buffer to enhance the robustness and life of the balancing circuitry. Simulation studies are conducted in MATLAB-Simscape to verify the effectiveness of the proposed scheme. Further, a comparative study with the state-of-the-art is performed to demonstrate the superiority of the scheme. © 2022 IEEE.

关键词： MATLAB

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LiFi-CFI: Light-weight Fine-grained Hardware CFI Protection for RISC-V 1

LiFi-CFI: Light-weight Fine-grained Hardware CFI Protection ...

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1st IEEE International Conference on Design, Test and Technology of Integrated Systems, DTTIS 2023

作者： Roodsari, Mahboobe Sadeghipour Sheikhshoaei, Fatemeh Maunero, Nicolo Prinetto, Paolo Navabi, Zain University of Tehran School of Electrical and Computer Engineering Tehran Iran Imt School for Advanced Studies Lucca Italy Politecnico di Torino Department of Automation and Information Turin Italy

ISBN: (纸本)9798350316957

In today's open-source industry, RISC-V based embedded devices are used in various applications where security is critical. RISC-V processors are vulnerable to many runtime attacks, including powerful code reuse and code injection in jump/return-oriented programming (JOP/ROP), known as control flow attacks (CFA). For this, several security techniques have been proposed to protect embedded devices from CFA, and one of the most secure categories is Control Flow Integrity (CFI). Alternative techniques consider the tradeoff between runtime hardware overhead and security protection. In particular, hardware-based techniques have attracted more attention in recent years because they consume less power and have less runtime overhead, which is critical in embedded systems. Considering these constraints, we have proposed a novel fine-grained technique for jump-oriented programming, where the required memory is replaced with a short Programmable Array Logic (PAL) to reduce runtime and hardware overhead. Also, a low-overhead shadow stack is proposed for return-oriented programming to reduce the required memory capacity compared to the traditional shadow stack implementation by handling the recursive function calls. The proposed Architecture called LiFi-CFI, is a lightweight hardware monitoring solution that can be attached to any IoT-class soft processor. LiFi-CFI not only reduces power consumption and hardware overhead compared to other advanced solutions but also maintains the security guarantees of the main CFI solution. It is a compact solution as a countermeasure with less than 1% hardware resource utilization for Xilinx Artix7 and 0.5% runtime overhead. © 2023 IEEE.

关键词： Embedded systems

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