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A Sensorless Method based on Zero-sequence Back-EMF for Five-phase Open-end Winding PMSM with Deadtime Compensation

IEEE Transactions on Energy Conversion 2025年

作者： Jiang, Chunyang Wang, Yingjie Liu, Hongchen Wheeler, Pat Bao, Danyang Northeast Forestry University College of Computer and Control Engineering Harbin 150001 China Harbin Institute of Technology School of Electrical Engineering and Automation Harbin 150001 China University of Nottingham Department of Electrical and Electronics Engineering Nottingham NG7 2RD United Kingdom Shenzhen Polytechnic University School of Mechanical and Electrical Engineering Shenzhen 518000 China

This paper proposes a sensorless method using the zero-sequence back electromotive force (ZSB-EMF) for five-phase open-end winding permanent magnet synchronous machine (OEW-PMSM) with common dc bus. The output of the zero-sequence controller, which is applied to suppress the zero-sequence current (ZSC), is used to reconstruct the ZSB-EMF. Then the position of the rotor is extracted from the output signal of the zero-sequence controller by single phase locked loop. A compensation of the deadtime is proposed to eliminate the negative effect for the position estimation and increase the effective modulation index. The proposed method needs no parameters of the motor, like the resistance, d-q inductance, which are vulnerable and might affect the accuracy of the estimation for the conventional sensorless methods based on the ZSB-EMF. It is easy to implement and the resistance network and complex calculation of conventional methods are also avoided. Experimental results on the five-phase open-end winding PMSM drive system are shown to validate the proposed sensorless method. © 1986-2012 IEEE.

关键词： Common dc Bus Deadtime Compensation Five-phase Open-end Winding Permanent Magnet Synchronous Machine Sensorless method

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Barrier Offset Varying-Parameter Dynamic Learning Network for Solving Dual-Arms Human-Like Behavior Generation

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Zhang, Zhijun Zhang, Mingyang Guo, Jinjia He, Haotian School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Key Library of Autonomous Systems and Network Control Ministry of Education China Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China Guangzhou510335 China Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shanxi University of Technology Hanzhong723001 China School of Information Science and Engineering Changsha Normal University Changsha410100 China School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China School of Computer Science and Engineering Jishou University Jishou416000 China

Enabling robots to imitate human actions and perform tasks with high precision while avoiding potential obstacles in the environment can effectively enhance the interaction between social robots and humans. In this paper, to achieve higher precision trajectory tracking and obstacle avoidance for dual-arm humanoid robots, the barrier offset varying-parameter dynamic learning neural (BOVDL) network method is proposed and applied to dual-arm humanoid behavior generation scheme. To do so, a dual-arms humanoid robot model is set up, and transformed into a constrained time-varying quadratic programming (TVQP) problem. Secondly, by using lagrangian multiplier method and Karush-Kuhn-Tuchker condition, the inequality constrained TVQP is converted as a time-varying equation with a barrier parameter. Thirdly, a varying-parameter dynamic learning network is presented to solve the time-varying equation with a barrier parameter. computer simulation experiments are conducted to verify the feasibility, accuracy, and safety of the proposed BOVDL network method. Experimental results show that all 14 joints of the humanoid robot's arms are within the motion range of each real human arm's physical constraints. The maximum position error and velocity error between the desired trajectory and the actual trajectory of the end effector are less than 10-6m magnitude and 10-7m magnitude, respectively, representing a reduction of 5 orders of magnitude compared to the traditional varying-parameter convergent-differential neural network. Furthermore, the proposed method also enables the dual-arm humanoid robot to avoid collisions with obstacles while performing tasks, demonstrating the superiority of the proposed BOVDL network scheme. © 2016 IEEE.

关键词： Anthropomorphic robots

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A recovery algorithm based on minimum distance redundant nodes in fault management in WSNs

International Journal of Control and Automation

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International Journal of control and Automation 2013年第2期6卷 175-184页

作者： Jia, Song Bailing, Wang Xiyuan, Peng Jianfeng, Li Cheng, Zhong School of Information and Electrical Engineering Harbin Institute of Technology at Weihai Shandong China School of Computer Science and Technology Harbin Institute of Technology at Weihai Shandong China Automatic Test and Control Institute Harbin Institute of Technology Harbin China

Fault recovery is vital to WSNs since node death is a typical fault. Aiming to recover the "coverage hole", a Recovery Algorithm based on Minimum Distance Redundant Nodes (MDRN) is proposed in this paper. By employing redundant nodes carefully, the recovery algorithm is deployed on the sink node with unconstrained energy consumption which knows the locations of all active nodes and redundant nodes in the WSNs. Simulation results demonstrate that, by choosing appropriate number of redundant nodes, this algorithm will have great recovery accuracy and coverage quality, also achieve the purpose of prolonging the lifecycle of WSNs.

关键词： Energy utilization

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Intermittently Dynamic Fuzzy Learning-Based Tracking control of Amplitude Signals for Vibratory Gyroscopes With Composite Identification

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Xu, Zeyuan Ge, Shuzhi Sam Liu, Weihao Yi, Guoxing Xie, Zhongliang Li, Kuo National University of Singapore Department of Chemical and Biomolecular Engineering Queenstown 117585 Singapore University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia27100 Italy National University of Singapore Department of Electrical and Computer Engineering Queenstown 119077 Singapore Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Northwestern Polytechnical University Department of Engineering Mechanics Xi’an710072 China Yanshan University School of Electrical Engineering Qinhuangdao066004 China

This article investigates an intermittently dynamic fuzzy learning-based tracking control (IDFL-TC) method of amplitude signals for vibratory gyroscopes. Compared with learning-based TC (L-TC) and fuzzy L-TC (FL-TC) methods, the IDFL-TC method with dynamic pruning technology can improve the identification accuracy of gyroscope dynamics and reduce calculation times by intermittently updating the output of nodes and dynamically recruiting or removing nodes. Subsequently, a composite identification mechanism is introduced into the control method to further improve the identification accuracy. A novel tracking controller is developed to well track the reference amplitude signal and guarantee the boundedness of the tracking error. Finally, comparative experimental results demonstrate that the proposed IDFL-TC method can obtain higher control precision of amplitude signals for vibratory gyroscopes with fewer calculation time in comparison to the L-TC and FL-TC methods. © 1963-2012 IEEE.

关键词： Identification (control systems)

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Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking

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IEEE Transactions on Signal Processing 2025年

作者： Chen, Jun Liu, Lina Zhu, Tianyi Liu, Yong Dai, Guang Jiang, Yunliang Tsang, Ivor W. Zhejiang Normal University National Special Education Resource Center for Children with Autism Hangzhou311231 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China China Mobile Research Institute Beijing100032 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China State Grid Corporation of China SGIT AI Lab China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Huzhou University School of Information Engineering Huzhou313000 China Centre for Frontier Artificial Intelligence Research A*STAR Singapore

This paper considers the problem of decentralized optimization on compact submanifolds, where a finite sum of smooth (possibly non-convex) local functions is minimized by n agents forming an undirected and connected graph. However, the efficiency of distributed optimization is often hindered by communication bottlenecks. To mitigate this, we propose the Quantized Riemannian Gradient Tracking (Q-RGT) algorithm, where agents update their local variables using quantized gradients. The introduction of quantization noise allows our algorithm to bypass the constraints of the accurate Riemannian projection operator (such as retraction), further improving iterative efficiency. To the best of our knowledge, this is the first algorithm to achieve an O(1/K) convergence rate in the presence of quantization, matching the convergence rate of methods without quantization. Additionally, we explicitly derive lower bounds on decentralized consensus associated with a function of quantization levels. Numerical experiments demonstrate that Q-RGT performs comparably to non-quantized methods while reducing communication bottlenecks and computational overhead. © 1991-2012 IEEE.

关键词： Iterative methods

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Model reduction and identification of nonlinear reactive sputter processes

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IFAC-PapersOnLine 2017年第1期50卷 13728-13734页

作者： Woelfel, Christian Awakowicz, Peter Lunze, Jan Institute of Automation and Computer Control Ruhr-University Bochum Germany Institute for Electrical Engineering and Plasma Technology Ruhr-University Bochum Germany

A model for nonlinear reactive sputter processes is developed based on a system theoretic analysis. Hence, a physical model from the fields of thin film science and vacuum science is analysed and reduced in order to describe the input/output behavior of the process by a nonlinear Abel differential equation. Hereby the input/output behavior is related to a supercritical Pitchfork-bifurcation. The parameter identification of the reduced model is discussed and an experiment for model validation is presented. The experiment shows high conformity between the reduced model and the measured input/output behavior of the process. The reduced model can be used as basis for control design for process stabilization. © 2017

关键词： Reactive sputtering Differential equations Nonlinear equations Nonlinear systems Input/output behaviors Model reduction Model validation Pitch fork bifurcations Process identification Process stabilization Sputter process Theoretic analysis

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Data-Driven Model Order Reduction for T-Product-Based Dynamical Systems

arXiv

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arXiv 2025年

作者： Mei, Shenghan He, Ziqin Mei, Yidan Mao, Xin Dong, Anqi Wang, Ren Chen, Can Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Division of Decision and Control Systems the Department of Mathematics KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States School of Data Science and Society the Department of Mathematics the Department of Biostatistics University of North Carolina at Chapel Hill Chapel HillNC27599 United States

— Model order reduction plays a crucial role in simplifying complex systems while preserving their essential dynamic characteristics, making it an invaluable tool in a wide range of applications, including robotic systems, signal processing, and fluid dynamics. However, traditional model order reduction techniques like balanced truncation are not designed to handle tensor data directly and instead require unfolding the data, which may lead to the loss of important higher-order structural information. In this article, we introduce a novel framework for data-driven model order reduction of T-product-based dynamical systems (TPDSs), which are often used to capture the evolution of third-order tensor data such as images and videos through the T-product. Specifically, we develop advanced T-product-based techniques, including T-balanced truncation, T-balanced proper orthogonal decomposition, and the T-eigensystem realization algorithm for input-output TPDSs by leveraging the unique properties of T-singular value decomposition. We demonstrate that these techniques offer significant memory and computational savings while achieving reduction errors that are comparable to those of conventional methods. The effectiveness of the proposed framework is further validated through synthetic and real-world *** Codes 93C05, 15A69, 93B30, 65F99 © 2025, CC BY.

关键词： Eigenvalues and eigenfunctions

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Data-driven control of T-Product-based Dynamical Systems

arXiv

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arXiv 2025年

作者： He, Ziqin Mei, Yidan Mei, Shenghan Mao, Xin Dong, Anqi Wang, Ren Chen, Can Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Division of Decision and Control Systems Department of Mathematics KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States School of Data Science and Society the Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States

Data-driven control is a powerful tool that enables the design and implementation of control strategies directly from data without explicitly identifying the underlying system dynamics. While various data-driven control techniques, such as stabilization, linear quadratic regulation, and model predictive control, have been extensively developed, these methods are not inherently suited for multi-linear dynamical systems, where the states are represented as higher-order tensors. In this article, we propose a novel framework for data-driven control of T-product-based dynamical systems (TPDSs), where the system evolution is governed by the T-product between a third-order dynamic tensor and a third-order state tensor. In particular, we offer necessary and sufficient conditions to determine the data informativity for system identification, stabilization by state feedback, and T-product quadratic regulation of TPDSs with detailed complexity analyses. Finally, we validate our framework through numerical *** Codes 37N35, 93-08, 93A15 © 2025, CC BY.

关键词： Tensors

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Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: Challenges and a Roadmap

arXiv

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arXiv 2025年

作者： Xu, Jianye Alrifaee, Bassam Betz, Johannes Mokhtarian, Armin Mittal, Archak Cai, Mengchi Mangharam, Rahul Shehata, Omar M. Elias, Catherine M. Zaech, Jan-Nico Scheffe, Patrick Jahncke, Felix Ulhas, Sangeet Sankaramangalam Arfvidsson, Kaj Munhoz Department of Computer Science RWTH Aachen University Germany Department of Aerospace Engineering University of the Bundeswehr Munich Germany Professorship of Autonomous Vehicle Systems Technical University of Munich Germany Department of Civil Engineering Indian Institute of Technology Bombay India School of Vehicle and Mobility Tsinghua University China Department of Electrical and Systems Engineering University of Pennsylvania United States Mechatronics Engineering Department Faculty of Engineering and Materials Science The German University in Cairo Egypt Department of Computer Science Engineering The German University in Cairo Egypt INSAIT Sofia University "St. Kliment Ohridski" Bulgaria School for Engineering of Matter Transport and Energy Arizona State University United States Division of Decision and Control Systems KTH Royal Institute of Technology Sweden

This article proposes a roadmap to address the current challenges in small-scale testbeds for Connected and Automated Vehicles (CAVs) and robot swarms. The roadmap is a joint effort of participants in the workshop "1st Workshop on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms," held on June 2 at the IEEE Intelligent Vehicles Symposium (IV) 2024 in Jeju, South Korea. The roadmap contains three parts: 1) enhancing accessibility and diversity, especially for underrepresented communities, 2) sharing best practices for the development and maintenance of testbeds, and 3) connecting testbeds through an abstraction layer to support collaboration. The workshop features eight invited speakers, four contributed papers [1]–[4], and a presentation of a survey paper on testbeds [5]. The survey paper provides an online comparative table of more than 25 testbeds, available at https://***/testbeds-survey. The workshop’s own website is available at https://***/iv24-workshop. Copyright © 2025, The Authors. All rights reserved.

关键词： Testbeds

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Sensor fusion techniques used for learning/identifying features of robot's part assembly tasks

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 1996年第5期210卷 429-435页

作者： Son, C Ahn, T School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia USA Department of Control and Measurement Engineering Wonkwang University Korea

In this paper, sensor fusion techniques, fused vision/optical sensors and tactile/vision sensors are introduced which are used for learning/identifying features of-tasks related to a robot's part-bringing procedure from a part position to a position over a hole for the purpose of a micro-part insertion in partially unstructured environments. An entropy function, which is a useful measure of the variability and the information in terms of uncertainty, including learning, is introduced to measure the overall performance of a task execution related to a part assembly. By employing a learning approach, the uncertainty associated with the part-bringing task is reduced. The above tools are necessary for a robot manipulator to perform complex assembly, material handling, manufacturing or machining tasks, for example remote maintenance and hazardous material handling, in an unstructured environment.

关键词： sensor fusion part assembly learning entropy function

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