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第43届中国控制会议

作者： Yu Fang Di Wu Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The vehicle electronic scroll compressors exhibits significant speed fluctuations during low-speed and heavy-load *** periodic fluctuation of load torque leads to speed *** limited sensitivity gain of traditional disturbance observers results in poor performance in suppressing periodic *** response to this issue,this study adopts an adaptive composite disturbance observer to estimate and compensate for load disturbances,in order to reduce speed fluctuations in the low-speed *** adaptive composite disturbance observer has achieved simultaneous and adaptive observation and estimation of periodic and aperiodic slow varying *** adaptive notch filter(ANF) based on variable step-size quantize least mean square(VSQLMS) algorithm is used to estimate the fundamental frequency of periodic *** simulation experiments,the method proposed has improved the control accuracy and dynamic response performance of the system,and has enhanced its robustness.

关键词： Extended State Observer internal model principle ANF multiple disturbance

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End-to-end multi-task reinforcement learning-based UAV swarm communication attack detection and area coverage

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Knowledge-Based systems 2025年 316卷

作者： Yu, Jin Zhang, Ya Sun, Changyin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

This paper proposes an innovative end-to-end multi-task reinforcement learning framework designed to enhance the security and operational stability of UAVs in complex communication attacks environments. In scenarios involving multiple UAV coverage, the framework classifies attack types and predicts UAV behaviors, which enables rapid response and compromised data recovery. First, a spatiotemporal attention mechanism is employed to strengthen the network by capturing temporal dependencies in the communication data and improving the recognition of hybird attack mode, thereby facilitating the integration of detection and repair. Second, the causal inference mechanism introduced in this study extracts true causal relationships from interference, which ensures the accuracy and stability of action predictions for area coverage under attack conditions. Finally, the entire process is modeled as a multi-task Markov decision process, optimizing overall decision-making efficiency. Additionally, the dynamic pheromone induction mechanism is proposed to realize the adaptive regional cooperative coverage of multiple UAVs. The experimental results demonstrate that the proposed algorithm can effectively detect hybrid attacks and predict UAV actions, protecting the multi-UAV cooperative area coverage. © 2025

关键词： Reinforcement learning

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A distributed finite-time economic dispatch algorithm in smart grid

A distributed finite-time economic dispatch algorithm in sma...

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2022 Jiangsu Annual Conference on Automation, JACA 2022

作者： Lin, Luxin Wang, Xiangyu School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education China

ISBN: (纸本)9781839538476

This paper focuses on the design of an initialization-free distributed finite-time algorithm for the economic dispatch problem in smart grid, where the generation units only communicate with its neighbors to minimize the total cost cooperatively. Firstly, the optimal solution of the economic dispatch problem is analyzed in accordance with the saddle-point dynamics. Then, an initialization-free finite-time algorithm is presented to guarantee the active power generation and the incremental cost converge to the optimal solution in a distributed manner. At last, some numerical simulations are preformed to validate the proposed method. Copyright© JACA 2022 All rights reserved.

关键词： Smart power grids

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A novel task-oriented framework for dual-arm robotic assembly task

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Frontiers of Mechanical engineering 2021年第3期16卷 528-545页

作者： Zhengwei WANG Yahui GAN Xianzhong DAI School of Automation Southeast UniversityNanjing 210096China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096China

In industrial manufacturing,the deployment of dual-arm robots in assembly tasks has become a ***,making the dual-arm robots more intelligent in such applications is still an open,challenging *** paper proposes a novel framework that combines task-oriented motion planning with visual perception to facilitate robot deployment from perception to execution and finish assembly problems by using dual-arm *** this framework,visual perception is first employed to track the effects of the robot behaviors and observe states of the workpieces,where the performance of tasks can be abstracted as a high-level state for intelligent *** assembly task and manipulation sequences can be obtained by analyzing and reasoning the state transition trajectory of the environment as well as the ***,the corresponding assembly manipulation can be generated and parameterized according to the differences between adjacent states by combining with the prebuilt knowledge of the *** are set up with a dual-arm robotic system(ABB YuMi and an RGB-D camera)to validate the proposed *** results demonstrate the effectiveness of the proposed framework and the promising value of its practical application.

关键词： dual-arm assembly Al reasoning intelligent system task-oriented motion planning visual perception

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A Novel Hybrid Transformer-Based Framework for Solar Irradiance Forecasting under Incomplete Data Scenarios

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IEEE Transactions on Industrial Informatics 2024年第6期20卷 8605-8615页

作者： Zhang, Hanjin Li, Bin Su, Shun-Feng Yang, Wankou Xie, Liping Southeast University School of Economics and Management Nanjing210096 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518063 China

Accurate prediction of solar irradiance is crucial for the effective utilization of solar energy. However, in real-world scenarios, complex irradiance patterns and prevalent incomplete data pose challenges to precise forecasting, resulting in additional uncertainties and instability. To address these issues, this study proposes a novel irradiance forecasting model that integrates a Mask-Transformer data imputation module and a prediction module centered around the typical patterns representation mechanism. The Mask-Transformer leverages a mask modeling mechanism to model the context of missing data, facilitating accurate estimation of missing values and reducing noise and uncertainty in the input data. The typical patterns representation mechanism comprises a series decomposition module and a feature fusion module, providing the module with the capability to mitigate nonlinearity and nonstationarity in solar irradiance data. This enhancement leads to improved short-term forecasting performance while maintaining long-term forecasting capabilities. Experimental results on two datasets demonstrate that the proposed model exhibits sufficient robustness and accuracy, making it effective in scenarios with incomplete data. © 2005-2012 IEEE.

关键词： Forecasting

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Multi-task Learning Based Single Temporal Remote Sensing Image Change Detection 12

Multi-task Learning Based Single Temporal Remote Sensing Ima...

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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Yang, Peixuan Cao, Yichao Lu, Xiaobo College of Software Engineering Southeast University JiangSu NanJing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education JiangSu NanJing China School of Automation Southeast University JiangSu NanJing China

ISBN: (纸本)9798400707988

Change Detection is a classic and fundamental task in remote sensing(RS), and has accomplish remarkable achievement on bitemporal image based methods. However, bitemporal images are very expensive and needs professional researchers to align and label the paired images. Furthermore, most bitemporal image based methods are only semantic-aware not instance-aware, which indicates these methods only use paired images' semantic information. Thus, we propose a method that only needs unpaired RS images with semantic labels. In this paper, we introduce multi-task learning into change detection progress, which comprises four major parts: Boundary-Aware Module, Contextual-Aware Module, Segmentation Module, Change Detection Module. At first, we construct Boundary-Aware Module and Contextual-Aware Module to extract sharp boundary and dense contextual features. Then we feed the feature maps to Change Detection Module and Segmentation Module to accomplish the subtasks. The comprehensive experiments on a single temporal image dataset demonstrate the efficiency of our approach over other bitemporal image based methods, and the experimental results shows our approach is competitive with other methods while all methods was trained on bitemporal image dataset. © 2023 ACM.

关键词： Change detection

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keypoints-guided Lightweight Network for Single-view 3D Human Reconstruction

Keypoints-guided Lightweight Network for Single-view 3D Huma...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yuhang Chen Chenxing Wang School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Single-view 3D human reconstruction has been a hot topic due to the potential of wide applications. To achieve high accuracy, existing works usually take computationally intensive models as backbone for exhaustive underlying features and then directly estimate human mesh vertices. These factors lead to redundant parameters, large calculations and low efficiency, while lightweight solutions to address these challenges are relatively scarce. In this work, based on the problems studied above, we propose a keypoints-guided lightweight network with an encoding-decoding framework. As the input is an image, a lightweight backbone named multi-stage and global feature enhanced network is designed for 2D encoding, where some operations of multi-scale fusion and frequency domain filtering are performed to extract more informative but low-resolution features. As the output is mesh of human body, we construct a keypoints-based 3D human template, with which the 2D low-resolution features can be mapped to 3D space to guide the 3D decoding with high efficiency and high accuracy. Extensive experiments on popular benchmarks 3DPW and Human3.6M illustrate the favorable trade-off between the accuracy and complexity of our method. Our code is publicly available at https://***/ChrisChenYh/***.

关键词： Three-dimensional displays Accuracy Image coding Frequency-domain analysis Estimation Benchmark testing Feature extraction

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Distributed Finite-Time Anti-Disturbance Secondary Voltage Regulation for Islanded Alternating-Current Microgrids 41

Distributed Finite-Time Anti-Disturbance Secondary Voltage R...

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第41届中国控制会议

作者： Xuechao Qiu Guanjun Li Xiangyu Wang School of Automation Southeast Universityand Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education China Electric Power Research Institude Company Ltd.

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,a distributed finite-time anti-disturbance secondary voltage regulation problem is investigated for islanded alternating-current(AC) microgrids with unmeasurable states and *** combining the integral-type nonsingular terminal sliding-mode control and finite-time extended-state observer technique together,distributed finite-time anti-disturbance secondary voltage regulation protocols are *** order to estimate the unmeasurable states and disturbances,some finite-time extended-state observers are constructed at ***,according to the estimates of disturbances and unmeasurable states,some integral-type nonsingular terminal sliding-mode surfaces are designed for the ***,secondary voltage regulation protocols are proposed based on the *** the islanded AC microgrid system,these protocols achieve the output voltage consensus in finite *** demonstrate the effectiveness of the proposed secondary voltage regulation protocols.

关键词： Islanded Alternating-Current Microgrids Disturbance Rejection control Secondary Voltage Regulation Output-Feedback control

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Anti-disturbance speed regulation control of PMSM at low speed by extended state observer and kalman filter

Anti-disturbance speed regulation control of PMSM at low spe...

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2023 Jiangsu Annual Conference on Automation, JACA 2023

作者： Xu, Lei Mao, Linxuan Li, Shihua School of Electronic Science and Engineering Southeast University Nanjing210096 China School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9781839539848

This paper presents an enhanced friction compensation anti-disturbance control strategy for improving the speed regulation control performance of a permanent magnet synchronous motor operating in low-speed mode. This is achieved via friction modelling and lumped disturbance compensation by seamlessly integrating extended state observer (ESO) and Kalman filter algorithms. Considering that nonlinear friction torque can not be ignored at low speeds, a piece-wise linearized friction model is first established based on speed estimation of Kalman filter and ESO. Then feed-forward compensation of friction torque and lumped disturbance estimation is conducted to reduce speed fluctuation during load changes at low speeds. Comparative simulation studies in the presence of measurement noises, external disturbances and nonlinear friction torque are conducted to validate the proposed algorithm with improved performance. © The Institution of engineering & Technology 2023.

关键词： Kalman filters

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RSD-Diff: Boundary-Frequency Feature-Enhanced Rail Surface Defect Inspection with Diffusion-Based Transformer Decoder

RSD-Diff: Boundary-Frequency Feature-Enhanced Rail Surface D...

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International Conference on systems and Informatics (ICSAI)

作者： Dezhou Wang Shixiang Su Xiaobo Lu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

Rail surface defect inspection is an essential task for the railway system. However, due to the similarity of the background and defect foreground pixels, uneven textures, irregular shapes and multiple scales of the existing defects, the inspection accuracy is still required to be further improved. In this paper, we first expand our dataset by GAN-based method for the scarcity of the defect samples. Then, we propose a diffusion-based model to perform the classification and segmentation task. The diffusion model predicts the mask from the Gaussian noise broken ground truth by successive designed transformer decoder. Meanwhile, to better distinguish the pixels in the boundary or other illegible regions, a boundary-frequency feature enhancement module (BFM) is proposed to make the model pay more attention to the defect-related features. Experiments on the rail surface defect dataset revealed that the proposed diffusion-based transformer decoder and BFM can work well together and efficiently improve the rail surface defect segmentation accuracy.

关键词： Rails measurement Accuracy Shape Inspection Transformers Rail transportation Decoding Surface texture Informatics

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