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Sensorless Control of PMSM Drives Using Octant Newton-Raphson Method and Reduced-Order EKF Considering Speed Reversal and Noise

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IEEE Transactions on Power Electronics 2025年第8期40卷 10793-10803页

作者： Xiang, Fuyuan Yu, Ke Chen, Congyan Li, Shihua Southeast University School of Automation and the Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

For back electromotive force-based sensorless control in permanent magnet synchronous motor (PMSM) drives, the conventional phase-locked loop (PLL) exhibits a ±π position estimation error during speed reversal. This paper presents an octant Newton-Raphson method (ONRM) based PLL, which employs a newly defined objective function and a position octant method. It not only ensures the position estimation converges correctly during speed reversal but also improves dynamic performance compared to the conventional PLL. The applicability condition of the ONRM in the presence of noise is also analyzed in detail. Subsequently, due to the noise sensitivity of the ONRM, a reduced-order extended Kalman filter is designed to filter out the non-Gaussian noise in the position estimation while simultaneously estimating the speed. The effectiveness of the proposed sensorless control scheme is verified on a 1.5 kW PMSM drive platform. © 1986-2012 IEEE.

关键词： Extended Kalman filters

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Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1987-1998页

作者： Yisha Li Ya Zhang Xinde Li Changyin Sun School of Automation Southeast UniversityNanjing 210096 Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China

This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic *** a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow *** order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is *** experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.

关键词： Human-machine cooperation mixed domain attention mechanism multi-agent reinforcement learning spatio-temporal feature traffic signal control

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Deposition of thin TiO_(2)films on fluorophlogopite particle surface by CVD in fluidized bed reactor to prepare silver pearlescent pigments

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Particuology 2024年第6期89卷 88-98页

作者： Ming Liu Guoqiang Shao Pengpeng Lv Jun Li Qingshan Zhu State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing 100049China

Two critical parameters:Glossiness and chromaticity space of pearlescent pigments were evaluated based on crystallinity and grain *** the roughness and the vertical distance evolution along the surface of deposited TiO_(2)film are affected by the *** optimal crystallinity of TiO_(2)was deposited at the temperature of 350℃.The TiCl4 concentration effectively changed the grain ***,the high flow rate of fluidizing gas and optimal gas–solid ratio(<0.11 m^(3)/(kg h))restricted the change of TiCl_(4)concentrations in fluidized bed *** this case,the grain size of the deposited TiO_(2)film was about 55 *** on the grain size,the color of pearlescent pigments was adjusted by controlling the film thickness based on TiO_(2)***,the silver pearlescent pigment with high glossiness was prepared by chemical vapor deposition method in a fluidized bed reactor.

关键词： Fluidized bed chemical vapor deposition TiO_(2)film deposition Pearlescent pigment Free powder restrain Crystal growth

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Symmetry-constrained quantum coupling in non-Fermi-liquid transport

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Chinese Physics B 2023年第6期32卷 495-509页

作者：李荣佘振苏 State Key Laboratory for Turbulence and Complex Systems College of EngineeringPeking UniversityBeijing 100871China Key Laboratory of Coastal Environment and Resources of Zhejiang Province School of EngineeringWestlake UniversityHangzhou 310030China

Finding the common origin of non-Fermi liquids(NFLs) transport in high-temperature superconductors(HTSCs)has proven to be fundamentally challenging due to the prominence of various collective ***,we propose a comprehensive non-Hermitian Hamiltonian(NHH) for quantum coupling of multiple scattering mechanisms associated with four types of order *** predicts that the anticommutation symmetry of the spinor fermions constrains the scattering rate to a unified quadrature scaling,i.e.,Γ=Γ_(1)+√Γ_(Q)^(2)+(μk_(B)T)^(2)+(vμ_(B)B)^(2)+(γ_(E)E)^(2).This scaling yields a comprehensive and accurate description of two widespread NFL behaviors in HTSCs,i.e.,a temperature-scaling crossover between quadratic and linear laws and the quadrature magnetoresistance,validated by several dozens of data sets for broad phase *** reveals that the common origin of these behaviors is the spinor-symmetry-constrained quantum coupling of spin-wave and topological excitations of mesoscopic ***,we show that this NHH can be easily extended to other complex quantum fluids by specifying the corresponding *** is concluded that this work uncovers a critical organization principle(i.e.,the spinor symmetry) underlying the NFL transport,thus providing a novel theoretical framework to advance the transport theory of correlated electron systems.

关键词： non-Fermi liquid non-Hermitian Hamiltonian anticommutation symmetry quantum coupling multiple scattering mechanisms

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Dynamic properties of mode Ⅰ and mode Ⅱ fractures of shale under impact loading

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Journal of Rock Mechanics and Geotechnical engineering 2025年第2期17卷 1053-1067页

作者： Zelin Yan Linjuan Wang Jidong Jin Jianxiang Wang State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of EngineeringPeking UniversityBeijing100871China School of Astronautics Beihang UniversityBeijing100191China

Controllable shock wave fracturing is an innovative engineering technique used for shale reservoir fracturing and *** the anisotropic fracture mechanism of shale under impact loading is vital for optimizing shock wave fracturing equipment and enhancing shale oil *** this study,using the well-known notched semi-circular bend(NSCB)sample and the novel double-edge notched flattened Brazilian disc(DNFBD)sample combined with a split Hopkinson pressure bar(SHPB),various dynamic anisotropic fracture properties of Lushan shale,including failure characteristics,fracture toughness,energy dissipation and crack propagation velocity,are comprehensively compared and discussed under mode Ⅰ and mode Ⅱ fracture ***,using a newly modified fracture criterion considering the strength anisotropy of shale,the DNFBD specimen is predicted to be a robust method for true mode Ⅱ fracture of anisotropic shale *** experimental results show that the dynamic mode Ⅱ fracture of shale induces a rougher and more complex fracture morphology and performs a higher fracture toughness or fracture energy compared to dynamic mode Ⅰ *** minimal fracture toughness or fracture energy occurs in the Short-transverse orientation,while the maximal ones occur in the Divider *** addition,it is interesting to find that the mode Ⅱ fracture toughness anisotropy index decreases more slowly than that in the mode Ⅰ fracture *** results provide significant insights for understanding the different dynamic fracture mechanisms of anisotropic shale rocks under impact loading and have some beneficial implications for the controllable shock wave fracturing technique.

关键词： Shale Controllable shock wave fracturing Dynamic fracture property Fracture toughness anisotropy Loading rate effect

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MissionML: A Mission Architecture Modeling Language based on Unified Architecture Framework

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INCOSE International Symposium 2024年第1期34卷 479-502页

作者： Yang, Yilong Song, Wenxiao Chang, Chuangye Gao, Xinghai State Key Laboratory of Complex & Critical Software Environment School of Software Beihang University Beijing China School of Software Beihang University Beijing China Institute of Unmanned Systems Beihang University Beijing China

The missions of complex systems, organizations, or groups can be identified through careful requirements and domain analysis. Mission architecture modeling is a crucial step for enterprise modeling and design. However, mission modeling is lacking from the Unified Architecture Framework (UAF), which system engineers must specify and model from sketch. This paper proposes a Mission Architecture Modeling Language (MissionML). This two-layer architecture language generalizes the general common and special knowledge from four typical missions as a core layer and mission layer. The elements of the core layer are obtained through slight extensions of the operational layer elements in UAFML, and the elements of the mission layer are derived from the elements in the core layer. Moreover, MissionML is implemented as a UAF profile, incorporating numerous domain concepts in its syntax and semantics for mission modeling. Finally, we use five public case studies to demonstrate learnability and extensibility from the viewpoint of system engineers. Copyright © 2024 by Yilong Yang, Wenxiao Song, Chuangye Chang, and Xinghai Gao. Permission granted to INCOSE to publish and use.

关键词： Unified Modeling Language

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Effect of Symmetric Delay on Airfoil Plunging

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Journal of Bionic engineering 2024年第6期21卷 2864-2876页

作者： Mostafa El-Salamony State Key Laboratory of Turbulence and Complex Systems Collaborative Innovation Center of Advanced Aero-EnginePeking UniversityBeijing100871China Institute of Aviation Engineering and Technology CairoEgypt

Delaying the sinusoidal plunging in the middle of the up-and down stroke is *** form of kinematics can appear if the flapping mechanism is malfunctioning,or while large birds fly in some *** of this study is to understand the effect of pausing the airfoil during plunging in the equilibrium *** paused plunging is modelled mathematically by means of a sinusoidal waveform raised to the third *** of this waveform is done on two stages;the wake pattern analysis and the aerodynamic and propulsive *** this waveform reveals a robust way to generate two triads wake pattern instead of the regular reverse von Karman vortex wake *** thrust generation mechanism is *** performance evaluation is done based on the thrust,lift,and power coefficients and the propulsive efficiency at different point in the nondimensional amplitude–reduced frequency *** modeling methods are utilized to stand on the performance of the paused waveform with respect to the regular sinusoidal *** findings underscore the potential of the proposed waveform as a promising alternative for enhancing the aerodynamic performance and propulsive efficiency in the design of Micro Air Vehicles(MAVs),a rapidly evolving field.

关键词： Unsteady aerodynamics Wake structure Plunging Flapping Fluid structure interaction Vortex MAV

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Complementary Learning System Based Intrinsic Reward in Reinforcement Learning 48

Complementary Learning System Based Intrinsic Reward in Rein...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gao, Zijian Xu, Kele Jia, Hongda Wan, Tianjiao Ding, Bo Feng, Dawei Mao, Xinjun Wang, Huaimin National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

ISBN: (纸本)9781728163277

Deep reinforcement learning has achieved encouraging performance in many realms. However, one of its primary challenges is the sparsity of extrinsic rewards, which is still far from solved. Complementary learning system theory suggests that effective human learning relies on two complementary learning systems utilizing short-term and long-term memories. Inspired by the fact that humans evaluate curiosity by comparing current observations with historical information, we propose a novel intrinsic reward, namely CLS-IR, which aims to address the problems caused by sparse extrinsic rewards. Specifically, we train a self-supervised predictive model with short-term and long-term memories via exponential moving averages. We employ the information gain between the two memories as the intrinsic reward, which does not incur additional training costs but leads to better exploration. To investigate the effectiveness of CLS-IR, we conduct extensive experimental evaluations;the results demonstrate that CLS-IR can achieve state-of-the-art performance on Atari games and DeepMind Control Suite. © 2023 IEEE.

关键词： complementary learning system Deep reinforcement learning exploration intrinsic reward

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Diversifying Message Aggregation in Multi-Agent Communication Via Normalized Tensor Nuclear Norm Regularization 48

Diversifying Message Aggregation in Multi-Agent Communicatio...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhai, Yuanzhao Xu, Kele Ding, Bo Feng, Dawei Gao, Zijian Wang, Huaimin National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

ISBN: (纸本)9781728163277

The use of graph attention networks (GAT) in communication-enhanced multi-agent reinforcement learning (Comm-MARL) has become prevalent. While successful, GAT can lead to homogeneity in the strategies of message aggregation, which can severely limit multi-agent coordination. To address this challenge, we study the adjacency tensor of the communication graph. Then we define a new nuclear tensor rank and its convex surrogate, the normalized tensor nuclear norm to measure the homogeneity of message aggregation. Leveraging the norm, we further propose a plug-and-play regularizer on the adjacency tensor, named Normalized Tensor Nuclear Norm Regularization (NTNNR), to actively enrich the diversity of message aggregation during the training stage. NTNNR is agnostic to specific Comm-MARL algorithms and can be flexibly integrated with different graph-attention methods. Empirical results demonstrate that aggregating messages using NTNNR-enhanced GAT can improve the efficiency of the training and achieve higher asymptotic performance than existing message aggregation methods. © 2023 IEEE.

关键词： Tensors

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Progressive Diversifying Policy for Multi-Agent Reinforcement Learning 48

Progressive Diversifying Policy for Multi-Agent Reinforcemen...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sun, Shaoqi Zhai, Yuanzhao Xu, Kele Feng, Dawei Ding, Bo National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

ISBN: (纸本)9781728163277

Multi-Agent Reinforcement Learning (MARL) has recently achieved promising performance in many collaborative decision making tasks. However, one of the main bottleneck challenges for MARL is the sparsity of the team reward, which can lead to the homogenization of agents' behaviors. To address these issues, we propose a Progressive Diversifying Policy (PDP) algorithm in this paper. Specifically, we propose to actively amplify the diversity between agents' policies during learning and exploit diversity as an additional intrinsic reward for MARL. Furthermore, we propose a progressive diversity boosting policy to find a better team policy. Leveraging the aforementioned improvements, our method can handle sparse team rewards and alleviate the homogeneous behaviors of agents. We conduct experiments on widely-used MARL environments and the results show that PDP can provide state-of-the-art performance while maintaining a competitive convergence speed. © 2023 IEEE.

关键词： Homogeneous behavior Multi-agent reinforcement learning Policy diversity Sparse reward

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