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A Model-Based Unmatched Disturbance Rejection Control Approach for Speed Regulation of a Converter-Driven DC Motor Using Output-Feedback

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 365-376页

作者： Lu Zhang Jun Yang Shihua Li School of Automation Southeast UniversityKey Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education IEEE Department of Aeronautical and Automotive Engineering Loughborough University

The speed regulation problem with only speed measurement is investigated in this paper for a permanent magnet direct current(DC)motor driven by a buck *** lumping all unknown matched/unmatched disturbances and uncertainties together,the traditional active disturbance rejection control(ADRC)approach provides an intuitive solution for the problem under ***,for such a higher-order disturbed system,the increase of poles for the extended state observer(ESO)therein will lead to drastically growth of observer gains,which causes severe noise *** paper aims to propose a new model-based disturbance rejection controller for the converter-driven DC motor system using *** of estimating lumped disturbances directly,a new observer is constructed to estimate the desired steady state of control signal as well as errors between the real states and their desired steady-state ***,a controller with only speed measurement is proposed by utilizing the *** performance of the proposed method is tested through experiments on *** is further shown via numerical calculations and experimental results that the poles of the observer within the proposed control approach can be largely increased without significantly increasing magnitude of the observer gains.

关键词： Bandwidth widening converter-driven direct current(DC)motor disturbance observer model-based disturbance rejection speed regulation

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Guided Transformative GAN: Joint Face Image Super-Resolution and Frontalization 10

Guided Transformative GAN: Joint Face Image Super-Resolution...

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10th International Conference on systems and Informatics, ICSAI 2024

作者： Mu, Minghao Wang, Wei Lu, Xiaobo School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9798331510138

Joint face super-resolution and frontalization of non-frontal low-resolution faces is of significant importance for many face analysis applications. However, at the same time, it is a challenging task. In this paper, a novel Guided Transformative Generative Adversarial Network (GT-GAN) is proposed to address the challenge of generating frontal high-resolution face images from non-frontal low-resolution face images. Existing methods usually treat super-resolution and frontalization as two separate tasks, leading to unsatisfactory processing results. GTG AN innovatively achieves both goals within a unified frame-work. Specifically, we propose Profile Attention Guidance (PAG) to generate landmark heatmaps that provide a priori pose information to the generative network and direct its attention to critical facial regions. In addition, an Attention Fusion Transformation Module (AFTM) is proposed to significantly improve the hallucination performance of the network by utilizing the attention mechanism and STNs, combined with sequential feature enhancement strategies. The experimental results show that GT-GAN exhibits superior performance in the joint face super-resolution and frontalization task on the public face dataset Multi-PIE, which provides new ideas and references for this challenging topic. © 2024 IEEE.

关键词： Generative adversarial networks

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Active Perception for Grasp Detection via Neural Graspness Field 38

Active Perception for Grasp Detection via Neural Graspness F...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Ma, Haoxiang Shi, Modi Gao, Boyang Huang, Di State Key Laboratory of Complex and Critical Software Environment School of Computer Science and Engineering Beihang University Beijing China Geometry Robotics

This paper tackles the challenge of active perception for robotic grasp detection in cluttered environments. Incomplete 3D geometry information can negatively affect the performance of learning-based grasp detection methods, and scanning the scene from multiple views introduces significant time costs. To achieve reliable grasping performance with efficient camera movement, we propose an active grasp detection framework based on the Neural Graspness Field (NGF), which models the scene incrementally and facilitates next-best-view planning. Constructed in real-time as the camera moves, the NGF effectively models the grasp distribution in 3D space by rendering graspness predictions from each view. For next-best-view planning, we aim to reduce the uncertainty of the NGF through a graspness inconsistency-guided policy, selecting views based on discrepancies between NGF outputs and a pre-trained graspness network. Additionally, we present a neural graspness sampling method that decodes graspness values from the NGF to improve grasp pose detection results. Extensive experiments on the GraspNet-1Billion benchmark demonstrate significant performance improvements compared to previous works. Real-world experiments show that our method achieves a superior trade-off between grasping performance and time costs. Code is available at https://***/mahaoxiang822/ActiveNGF. © 2024 Neural information processing systems foundation. All rights reserved.

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Short-term Load Forecasting with Transformer Based on Fusion CNN-BiGRU

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第5期52卷 1344-1364页

作者： Xie, Tao Xiao, Zhongchuang He, Xiaojun Fan, Yinchun Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Jiaotong University's School of Electromechanical and Vehicle Engineering Chongqing400065 China SINOPEC Sales Co Ltd's Chongqing EJI branch Chongqing400065 China

To improve the accuracy of short-term power load forecasting, a short-term power load forecasting method based on Transformer with fused CNN-BiGRU is proposed. First, the best input data sequence is selected using the Partial Autocorrelation Function (PACF). Next, the importance scores and rankings of the feature data are obtained through the gradient boosting tree algorithm of CatBoost, and the optimal input features are selected. Then, the feature data and load data are combined. Finally, the combined data is used as input for the Transformer fused with CNN-BiGRU. In model training and forecasting, a hybrid forecasting strategy is employed, incorporating elements of multi-step forecasting into single-step forecasting. For the data of each moment, personalized and independent model training is performed, along with forecasting that include hybrid elements. The model replaces the original word embedding and position encoding components of Transformer. It uses CNN-BiGRU to extract high-dimensional feature representations of latent feature information and relative positional information from the input data. The proposed model demonstrates higher forecasting accuracy through validation on two different datasets and comparison with other forecasting models. Additionally, two ablation experiments are conducted. Through systematic ablation experiments, we demonstrate that modifications to the Transformer input layer significantly improve model performance in time series tasks. These results validate the rationality and effectiveness of the proposed approach. The ablation experiments on the method of PACF selecting the optimal input data sequence and CatBoost filtering the optimal input feature data, as well as the hybrid forecasting strategy, further verify the effectiveness and rationality of the data selection methods and forecasting strategies used in this study for short-term power load forecasting. Moreover, to eliminate the zigzagging jitter phenomenon in the foreca

关键词： CatBoost hybrid forecasting strategy PACF Transformer fuses CNN-BiGRU

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RSD-Diff: Boundary-Frequency Feature-Enhanced Rail Surface Defect Inspection with Diffusion-Based Transformer Decoder 10

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

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10th International Conference on systems and Informatics, ICSAI 2024

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

ISBN: (纸本)9798331510138

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. © 2024 IEEE.

关键词： Decoding

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Criteria of tracking vortex surfaces in turbulent-like flows

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Advances in Aerodynamics 2022年第1期4卷 96-114页

作者： Zishuo Han Yue Yang State Key Laboratory for Turbulence and Complex Systems College of EngineeringPeking UniversityBeijing100871China

We propose criteria of tracking vortex surfaces in complex flows based on the vortex-surface field (VSF). The criteria characterize the accuracy and Lagrangian tracking performance of the numerical VSF solution, and determine the time period when the vortex surface tracking is satisfactory. Moreover, we develop a turbulent-like flow combining large-scale coherent structures in the Taylor-Green flow and small-scale turbulent structures in homogeneous isotropic turbulence (HIT). From tracking of vortex surfaces during the effective tracking period, we find that the imposed HIT disturbance significantly wrinkles vortex surfaces. Subsequently, the wrinkled vortex tube with large vorticity magnitude tends to be further twisted, contributing to energy cascade, while the wrinkling is mitigated in the region with small vorticity magnitude.

关键词： Vortex dynamics Turbulence Coherent structure Vortex-surface field

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Cooperative Output Regulation of Heterogeneous systems over Directed Graphs:A Dynamic Adaptive Event-Triggered Strategy

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Journal of systems Science & complexity 2023年第3期36卷 909-921页

作者： SU Hang CHENG Bin LI Zhongkui State Key Laboratory for Turbulence and Complex Systems and Department of Mechanics and Engineering Science College of EngineeringPeking UniversityBeijing 100871China Department of Control Science and Engineering College of Electronics and Information EngineeringTongji UniversityShanghai 201804China Frontiers Science Center for Intelligent Autonomous Systems Shanghai 201210China

This paper investigates the cooperative output regulation problem of heterogeneous linear multi-agent systems over directed graphs with the constraint of communication *** that there exists an exosystem whose state information is not available to all agents,the authors develop distributed adaptive event-triggered observers for the followers based on relative information between neighboring *** should be pointed out that,two kinds of time-varying gains are introduced to avoid relying on any global information associated with the network,and dynamic triggering conditions are designed to get rid of continuous *** the basis of the designed observers,the authors devise a local controller for each *** with the existing related works,the main contribution of the current paper is that the cooperative output regulation problem for general directed graphs is solved requiring neither global information nor continuous communications.

关键词： Adaptive control consensus cooperative output regulation directed graph event-triggered control multi-agent systems

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Transformer-Based Scenario Generation of Multi-segment Monotonic Increasing Bidding Curves of Power Plants

Transformer-Based Scenario Generation of Multi-segment Monot...

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International Conference of Electrical, Electronic and Networked Energy systems, EENES 2024

作者： Xie, Haochuan Lv, Jianhu Wang, Ying Yuan, Kun College of Software Southeast University Suzhou China China Electric Power Research Institute Beijing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9789819618637

With the relaxation of electricity market regulation, power plants are permitted to engage in bidding. The bidding outcomes in the competitive market differ significantly from those in the previously regulated market. These differences in bidding outcomes will further impact power flow and system security. To provide a reference for practical electricity market operations, make market simulation more realistic, it is crucial to analyze and establish a model to simulate the bidding behavior of power generators. This paper applies Transformer networks to predict multi-segment bidding behaviors of power plants, we have improved the classic Transformer network by adopting a one encoder-two decoder format, considering six key factors influencing bidding behavior: unit ID, fuel type, capacity, location, system load, and load ratio. Additionally, monotonicity of the multi-segment bidding curves of power plants is taken into account, and monotonicity loss functions are introduced to achieve this goal. We apply this method to predict bidding behaviors in power plants in the Australian electricity market, validating the accuracy of the model. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： bidding curves generation monotonicity constraint power market Transformer-based

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Hazard-Aware Weighted Advantage Combination for UAV Target Tracking and Obstacle Avoidance

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IEEE/CAA Journal of Automatica Sinica 2024年第6期12卷 1260-1271页

作者： Xu, Lele Liu, Jian Chang, Xiaoguang Liu, Xuping Sun, Changyin The School of Artificial Intelligence Anhui University Hefei230601 China The School of Automation the Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

In recent years, the rapid evolution of unmanned aerial vehicles (UAVs) has brought about transformative changes across various industries. However, addressing fundamental challenges in UAV technology, particularly target tracking and obstacle avoidance, remains crucial for wildlife protection, military industry security, etc. Many existing methods based on reinforcement learning to solve UAV multi-tasks need to be redesigned and retrained, and cannot be quickly and effectively extended to other scenarios. To this end, we propose a novel solution based on a hazard-aware weighted advantage combination for UAV target tracking and obstacle avoidance. First, we independently trained the UAV target tracking and obstacle avoidance using the Dueling Double Deep Q-Network reinforcement learning algorithm. Subsequently, in a multitasking scenario, we introduce the two pre-trained networks. Meanwhile, we design a weight determined by the present risk level encountered by the UAV. This weight is utilized to perform a weighted summation of the advantage values from both networks, eliminating the need for retraining to obtain the final action. We validate our approach through extensive simulation experiments in the robotics simulator known as CoppeliaSim. The results demonstrate that our method outperforms current state-of-the-art techniques, achieving superior performance in both tracking accuracy and avoidance of collisions. © 2013 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A novel pathway for the preparation of Mg metal from magnesia

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Journal of Magnesium and Alloys 2022年第10期10卷 2847-2856页

作者： Zhimin Zhang Xuchen Lu Yan Yan State Key Laboratory of Multi-phase Complex systems Institute of Process EngineeringChinese Academy of ScienceHaidian DistrictBeijing 100190PR

High-purity anhydrous magnesium chloride was prepared from magnesia and ammonium *** chlorination process was analyzed and then the critical stages affecting the purity of anhydrous magnesium chloride were *** effect of sample dimension on the above critical stages was investigated *** purity guarantee mechanism of anhydrous magnesium chloride was *** that,magnesium metal was obtained via electrolyzing the anhydrous magnesium chloride-containing molten *** new process for the continuous production of magnesium metal from magnesia was proposed and *** incomplete chlorination reaction and the hydrolysis of anhydrous magnesium chloride are the two critical stages affecting the purity of the anhydrous magnesium *** dimension of the sample can influence reaction process and reaction mechanism,and thus the problems of incomplete chlorination reaction and hydrolysis can be solved *** magnesia content in anhydrous magnesium chloride was below 0.1 wt.%when the ratio of height to diameter of the sample was over *** content of impurities in the magnesium metal obtained met the specifications of the product *** current efficiency was(94.7±1.8)%and the electricity consumption was(9107±97)kW h/t.

关键词： Magnesium Electrolysis Anhydrous magnesium chloride Hydrolysis

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