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第41届中国控制会议

作者： Shanshan Fu Liling Ma Junzheng Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper is aimed at the second-order linear multi-agent formation *** to the failure of the actuator in a node,the wrong interactive information is transmitted between nodes,so that the entire system cannot complete the expected task.A distributed adaptive fault isolation and fault tolerance method based on consistency theory is *** the consistency error variable is designed according to the connection relationship between nodes and the faulty node,which eliminates the influence of the faulty node on the neighbor ***,an overall distributed adaptive fault isolation and fault tolerance approach is designed according to the local information of neighbor *** stability of the designed approach is proved by constructing a reasonable Lyapunov *** results show that the proposed adaptive control approach has good robustness.

关键词： Fault Isolation Adaptive control Fault Tolerant control Uniformity

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Data-driven finite-time adaptive attitude tracking control for spacecraft

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Advances in Space Research 2025年第12期75卷 8780-8791页

作者： Wang, Jianhui He, Guangping Bian, Guibin Geng, Shixiong College of Mechanical & Energy Engineering Beijing University Of Technology Beijing100124 China Department of Mechanical and Electrical Engineering North China University of Technology Beijing100144 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

This paper proposes a novel data-driven finite-time adaptive control method for the spacecraft attitude tracking control problem with inertial uncertainty. Based on the dynamic regression extension technique, the disturbance unit matrix is introduced, and the parameter estimation law in the form of algebraic equations is presented, which realizes the arbitrarily small convergence of parameter estimation error. The convergence of parameter estimation errors is verified through stability analysis. Globally non-singular fast terminal sliding mode control is designed to achieve finite-time arrival of the sliding surface and convergence of the system state error. The stability analysis of the closed-loop system demonstrates the effectiveness of the proposed method. Furthermore, the effectiveness and superiority of the proposed method are verified through numerical simulations of spacecraft attitude tracking control. © 2025 COSPAR

关键词： Attitude control

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Robust Model Predictive control for PMSM Drives Against Parameter Mismatch 41

Robust Model Predictive Control for PMSM Drives Against Para...

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第41届中国控制会议

作者： Luwei Shao Wei Shen Fan Li Chuanyu Ge State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

As an advanced control method of permanent magnet synchronous motor(PMSM),finite control set model predictive control(FCS-MPC) has earned widespread attention for its advantages of intuitive concept,splendid dynamic performance and capability of handling the constraints and ***,the dependence on the model parameter accuracy is the main barrier for FCS-MPC to be applied to the industry *** order to solve this issue,this paper presents a robust FCS-MPC against parameter *** proposed strategy utilizes the prediction error in the d-and q-axis currents during the past instant to calculate the compensation which should be added to the predicted currents in the next ***,the cost function in the proportional-integral form is designed as ***,the simulation results and experimental results prove the proposed method's effectiveness in suppressing the influence of model parameter mismatch on the control performance of PMSM.

关键词： permanent magnet synchronous motor finite control set model predictive control parameter mismatch

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Improved Model-Predictive Current control of Permanent Magnet Synchronous Motor Drives with Designed Cost Function 41

Improved Model-Predictive Current Control of Permanent Magne...

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第41届中国控制会议

作者： Yi Yang Wei Shen Dan Li Luwei Shao State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Model predictive current control(MPCC) is widely applied in electrical drives and power electronics because of its simplicity and ***,steady-state errors are always present because of the inaccurate prediction induced by changing actual *** paper proposes a simple strategy for improving MPCC performance,which reduces steadystate errors and eliminates the additional prediction *** cost function,which is made up of tracking mistakes,is used in MPCC to choose the best switching *** paper introduces a new cost function that also includes actual current *** is a coefficient of actual current errors that enhances the appropriateness of permanent magnet synchronous motor(PMSM) *** results show superior performance of the proposed MPCC to that of conventional MPCC.

关键词： PMSM MPCC Cost function Current errors

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LAUNet:A Latent Action Units Network for Facial Expression Recognition 41

LAUNet:A Latent Action Units Network for Facial Expression R...

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第41届中国控制会议

作者： Junlin Zhang Kaoru Hirota Yaping Dai Sijie Yin School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The inter-class similarity of facial expressions is one of the key challenges in Facial Expression Recognition(FER).In this manuscript,a Latent Facial Action Units Network(LAUNet) is proposed for the problem of inter-class similarity of facial expressions in *** proposed method recognizes subtle differences between facial expressions by learning Latent Facial Action Units Features(LAUFs).Specifically,LAUNet is composed of two parts:the Latent Facial Action Units Features Extraction Network(LEN) and the Latent Facial Action Units Selection Network(LSN).Firstly,LEN extracts LAUFs from the feature map of the backbone using the spatial attention ***,taking advantage of the channel attention mechanism,LSN captures the latent relationships between features from LAUFs to select effective features for *** are performed on the dataset after removing the invalid data of non-face images from the original FER2013 *** with some previous state-of-the-art methods,LAUNet achieves the highest accuracy rate of 71.31%.Depending on the backbone,LAUNet can improve the accuracy by up to 5.46% compared to the original architecture of the backbone.

关键词： Facial Expression Recognition Attention Mechanism Computer Vision

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An Adversarial Video Moment Retrieval Algorithm 41

An Adversarial Video Moment Retrieval Algorithm

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第41届中国控制会议

作者： Mohan Jia Zhongjian Dai Yaping Dai Zhiyang Jia School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In one-stage methods for video moment retrieval,the common representations indirectly supervised by boundary prediction fail to fully preserve the inherent characteristic of the video and query,which limits the retrieval *** solve this problem,an Adversarial Video Moment Retrieval(AVMR) algorithm is proposed to learn the common representations with modality invariance and cross-modal *** is implemented through the process of adversarial learning between a feature projector and a modality *** feature projector tries to generate a modality-invariant common representation and to confuse the modality *** modality classifier tries to discriminate between different modalities based on the generated representation by the feature *** triplet constraints are further imposed on the feature projector to preserve the underlying cross-modal semantic structure of *** experimental results show that AVMR surpasses the baseline Attentive Cross-modal Relevance Matching(ACRM) by 1.10% and 1.73% in the "mIoU" metric on two public datasets Charades-STA and TACoS,respectively.

关键词： Video Moment Retrieval Cross-modal Retrieval Deep learning Adversarial Learning

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AI4S Based on DeSci: Reference Model and Research Issues

AI4S Based on DeSci: Reference Model and Research Issues

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2023 IEEE International Conference on systems, Man, and cybernetics, SMC 2023

作者： Ding, Wenwen Li, Juanjuan Qin, Rui Guan, Sangtian Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Macau University of Science and Technology Faculty of Innovation Engineering Macau China Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350337020

The rise of Artificial Intelligence for Science (AI4S) has highlighted the importance and urgency of ensuring open-ness, fairness, impartiality, diversity, and sustainability in scientific systems. Existing scientific systems, referred to as Centralized Science (CeSci), are built on centralized organizational structures and top-down institutional frameworks, which are lagging behind the development and practical requirements of AI4S. To address these limitations, AI4S needs to embrace a new scientific organizational and operational paradigm, namely Decentralized Science (DeSci). It can provide strong support to AI4S via effectively addressing issues such as information silos, biases, unfair distribution, and monopolies and promoting multidisciplinary, interdisciplinary, and trans disciplinary cooperation in science. Based on these considerations, this paper presents the framework of AI4S based on DeSci and explores its potential application scenarios and research issues. The research can provide effective guidance for the development of scientific systems. © 2023 IEEE.

关键词： Artificial Intelligence for Science (AI4S) ChatGPT Decentralized Autonomous Organizations and Operations (DAOs) Decentralized Science (De-Sci) Generative Artificial Intelligence (GAI) Intelligent Science

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Non-fragile H∞ Filtering for Fuzzy Discrete-time systems with Markovian Jump and Data Loss 10

Non-fragile H∞ Filtering for Fuzzy Discrete-time Systems wi...

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

作者： Liang, Lun State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang Hangzhou310027 China

ISBN: (纸本)9781665424233

This paper focuses on the design of the non-fragile H∞ filtering of fuzzy discrete-time systems with Markovian jump and data loss. The system is represented by Takagi and Sugeno (T-S) fuzzy model. The imperfect information transmission from the factory to the filter is modeled by a Bernoulli random binary distribution. A non-fragile and full-order H∞ filter with additive gain variations is constructed. The sufficient conditions are derived based on mode-dependent and fuzzy-basis-dependent Lyapunov functional methods, which ensure the developed filtering error system is stochastically stable with given H∞ performance index. The usefulness and effectiveness of the filter design method is certified by a numerical case. © 2021 IEEE.

关键词： Numerical methods

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Heterogeneous Image Knowledge Driven Visual Perception

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 255-257页

作者： Lan Yan Wenbo Zheng Fei-Yue Wang IEEE the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous *** the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

关键词： Visual visual knowledge

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Decomposing Visual and Semantic Correlations for Both Fully Supervised and Few-Shot Image Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1658-1668页

作者： Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Most image classification methods are designed to either boost the classification accuracies with abundant supervision, or cope with the shortage of supervision information. This is often achieved by using the visual and semantic information of other sources. However, these methods use the visual and semantic information within the same class independently, leaving their intrinsic correlations unconsidered. Objects and disturbing components as well as noise exist on the same image. Besides, semantic representations also contain noisy information. To solve the problems mentioned above, we propose a novel method for both fully supervised image classification and few-shot image classification by decomposing visual and semantic correlations. We jointly explore the intrinsic correlations of visual and semantic information of images within the same class. For each class, we decompose its visual and semantic correlations using low-rank and sparse constraint respectively. The decomposed low-rank parts character the intrinsic correlations of images can be used in a linear transformation way. Using the decomposed parts of each class, Classification can be achieved by reconstruction error minimization. We conduct experiments on several datasets for both fully supervised image classification and few-shot image classification. Experimental results well show the effectiveness of the proposed method. © 2020 IEEE.

关键词： Semantics

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