检索结果-内蒙古大学图书馆

作者： Weibin Chen Lei Hua Leixin Xu Benshun Zhang Mengmeng Li Tao Ma Yang-Yang Chen School of Automation Southeast University Robot Department Jiangsu Automation Research Institute School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

This paper deals with the coordinated welding problem of multi-robot systems by applying a deep reinforcement learning algorithm which is called multi-agent deep deterministic policy gradient(MADDPG).It is assumed that the states and actions of robots are continuous and each robot can only get local information of its neighbors.A novel reward composed of trajectory optimization,coordinated welding and collision avoidance is designed,which yield multi-robot systems to arrive the welding targets quickly,achieve the simultaneous welding for a weld line and avoid collision among *** results are provided to demonstrate the effectiveness of the proposed control algorithm.

关键词： Deep multi-agent reinforcement learning MADDPG Coordinated welding

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Adaptive Multi-dimensional Taylor Network control for Cement Calciner Outlet Temperature

Adaptive Multi-dimensional Taylor Network Control for Cement...

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IEEE Information Technology, Networking, Electronic and Automation control Conference

作者： Mao-Qi Zhang Hong-Sen Yan Lin-Wei Yu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143910

This paper focuses on the problem of adaptive multi-dimensional Taylor network control (adaptive MTN) for cement calciner outlet temperature. Firstly, we establish a mathematical model for the cement calciner system, and the model structure is designed as a multi-input single-output data-driven form with time-delay. Secondly, the cross-correlation method and the recursive least squares method are used to identify the model parameters. Thirdly, based on gradient descent method, MTN controller is designed to implement the online update of network weights. Finally, experiment results are given to illustrate the effectiveness of the proposed control strategy.

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Wind Speed Data Repairing Method Based on Bidirectional Prediction

Wind Speed Data Repairing Method Based on Bidirectional Pred...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Xincheng Shen Yi Qu Shaoxiong Huang Zhi Li Kaifeng Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China State Grid Anhui Electric Power Co. Ltd Hefei China

ISBN: (数字)9781728190044

ISBN: (纸本)9781728190051

In order to repair the lost data in distributed wind power system, this paper puts forward a wind speed data repairing model based on a new bidirectional prediction method. This model consists of two one-way prediction models. In each prediction model, the original wind speed data are decomposed into several intrinsic mode functions (IMFs) and a residue signal by ensemble empirical mode decomposition (EEMD) method. Then the Savitzky-Golay (SG) filter is used to redu.e noise for high-frequency IMFs. Next the long short-term memory (LSTM) model and autoregressive integrated moving average (ARIMA) model are combined to predict low-frequency IMFs and the noise redu.tion results respectively. At the end, all those forecast results are added and form a one-way result. By weighted average of two one -way results, the repairing result is calculated. The experimental results from multiple prediction cases show that this method can get more accurate results.

关键词： Wind speed Distributed databases Predictive models Maintenance engineering Wind power generation Data models Wind forecasting

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QMIX Algorithm for Coordinated Welding of Multiple Robots

QMIX Algorithm for Coordinated Welding of Multiple Robots

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作者： Liangchuang Liao Yang-Yang Chen School of Electronic Science & Engineering Southeast University Robot Department Jiangsu Automation Research Institute School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

This paper considers the coordinated welding control based on deep multi-agent reinforcement *** discrete-time states and actions with local observation for the welding robots and the characteristics of the weld lines(e.g.,the starting points of the weld lines and the ending points of the weld lines) are defined,which is suitable to use the monotonic value function factorisation for deep multi-agent reinforcement learning(QMIX) algorithm.A novel reward composed of trajectory optimization,collision avoidance and the task done is designed,which is proved by the simulation in the grid-world environment.

关键词： Deep multi-agent reinforcement learning Welding robots QMIX

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An enhanced algorithm for cell-level anomaly segmentation in photovoltaic solar panels using electroluminescence imaging

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Energy 2025年 331卷

作者： Ruiyao Duan Yongjian Wang Xisong Chen Shihua Li School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 PR China Institute for Automatic Control and Complex System University of Duisburg–Essen Duisburg 47057 Germany

Electroluminescence imaging is a crucial diagnostic tool for assessing the quality and performance of photovoltaic (PV) modules. However, current research often focuses on defect detection in PV modules, neglecting the need for detailed segmentation at the individual cell level. To address the limitations of existing methods in recognizing complex defects and suppressing background noise, this paper proposes a novel semantic segmentation algorithm (CAAK-Net), capable of identifying anomalies at the cell level of photovoltaic panels. To enhance network performance, CAAK-Net uses K-Net as a baseline and incorporates the Convolutional Block Attention Module (CBAM), Attention Refinement Module (ARM), and Atrous Spatial Pyramid Pooling (ASPP) modules. Experimental results comparing CAAK-Net with mainstream segmentation networks demonstrate its superior segmentation performance, particularly in recognizing defect edges and small-area attached defects. Additionally, we establish a quantitative correlation model between detection errors and PV system efficiency losses, pioneering the translation of pixel-level segmentation accuracy into measurable operational costs, thereby providing an economic assessment framework for industrial PV inspection. Furthermore, the network exhibits a certain degree of robustness in noisy environments, showcasing its segmentation advantages in diverse defect scenarios.

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Distributed PV Identification Based on High-Precision Bus Data Analysis

Distributed PV Identification Based on High-Precision Bus Da...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Shen, Xincheng Huang, Shaoxiong Li, Zhi Zhang, Kaifeng Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Center State Grid Anhui Electric Power Co. Ltd. Department of Dispatch Hefei China

ISBN: (纸本)9781728168531

With the rapid development of distributed photovoltaic (PV), it is necessary to study its low-cost output identification technology. In this paper, a low-cost PV output identification method is proposed by using feature extraction. This paper analyzes the high-precision bus data, and uses harmonic analysis, wavelet analysis and Ensemble Empirical Mode Decomposition (EEMD) to extract the operating features of PV output. Then this paper screens these extracted features with the correlation between features and PV output, the stability of the features at different times and the difference of features in different signals. The appropriate features are selected for PV output identification, and its identification accuracy is calculated. The experimental results show that with the method of the Ensemble Empirical Mode Decomposition, an appropriate operating feature can be extracted. This feature can identify the distributed PV output in small bus bar when the PV is working stably. © 2020 IEEE.

关键词： Empirical mode decomposition

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TransTracking for UAV: An Autonomous Real-time Target Tracking System for UAV via Transformer Tracking

TransTracking for UAV: An Autonomous Real-time Target Tracki...

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Intelligent Technology and Embedded systems (ICITES), International Conference on

作者： Xiaolou Sun Fei Xie Qi Wang Yuncong Yao Hao Wang Wei Wang Wankou Yang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China School of Automation Nanjing University of Information Science and Technology Nanjing China

Most of the existing Siamese-type trackers usually adopt pre-defined anchor boxes or anchor-free schemes to accurately estimate the bounding box of targets. Unfortunately, they suffer from complicated hand-designed components and tedious post-processings. It is not easy to adjust parameters for unique scenes in real applications. So, we propose a new scheme by formulating visual tracking as a direct set prediction problem to alleviate this issue. The main component is a transformer attached to the Siamese-type feature extraction networks. Thus, our new framework can be summarized as Siamese Network with Transformers (SiamTFR). With a fixed small set of learned object queries, we force the final set of predictions via bipartite matching, significantly redu.ing hyper-parameters associated with the candidate boxes. Due to the unique predictions of this framework, we significantly ease the heavy burden of hyper-parameters search of post-processings in visual tracking. Extensive experiments on visual tracking benchmarks, including GOT-10K, demonstrate that SiamTFR achieves competitive performance and runs at 50 FPS. Specifically, SiamTFR outperforms leading anchor-based tracker SiamRPN++ in the GOT-10K benchmark, confirming its effectiveness and efficiency. Furthermore, SiamTFR is deployed on the embedded device in which the algorithm can be run at 30FPS or 54FPS with TensorRT meeting the real-time requirements. In addition, we design the complete tracking system demo that can work in the real road to narrow the gap between the academic models and industrial deployments.

关键词： Performance evaluation Visualization Target tracking Embedded systems Roads Force Benchmark testing

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Finite-Time Continuous Terminal Sliding-Mode Speed control of Micro Gas Turbine System with Mismatched Disturbances

Finite-Time Continuous Terminal Sliding-Mode Speed Control o...

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Chinese Automation Congress (CAC)

作者： Yanjun Sun Long Xu Ning Chen Shihua Li School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China China Electric Power Research Institute Nanjing China

ISBN: (纸本)9781665426480

The micro gas turbine system acts as a key role in distributed renewable generation systems due to its flexibility, versatility, and low emissions. This paper focuses on the micro gas turbine speed control problem, which primarily determines the fuel flow for the turbine under variable load conditions. Due to nonlinear factors in MGTS, such as the coupling between systems parameters, and external disturbances such as electric load variation, it is a challenge to realize accurate speed control. In this paper, a continuous terminal sliding-mode control law based on a finite-time disturbance observer is designed for micro gas turbine control system. In this method, the integral of high-frequency switching function is introduced to control law. It guarantees the continuity of control signal and alleviates the chattering problem. Besides, the linear sliding mode surface is replaced by a full-order terminal sliding mode surface, and it obtains finite-time convergence and smaller speed tracking error. Furthermore, disturbance estimates are used in control law to eliminate the adverse effect of disturbances on control performance, which improves disturbance rejection ability. Finally, simulation comparison with conventional sliding-mode control validates the advantages of the composite controller.

关键词： Renewable energy sources Simulation Velocity control Switches Load management Disturbance observers Fuels

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An Improved YOLOv5 Detection Algorithm with Pruning and OpenVINO Quantization

An Improved YOLOv5 Detection Algorithm with Pruning and Open...

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Chinese Automation Congress (CAC)

作者： Cheng Chen Yahui Gan Zao Han Huan Gao Ang Li Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

In order to address the problem of current object detection models being too large to be deployed on robot controllers, this paper proposes improvements to YOLOv5 for real-time detection. The YOLOv5s model is pruned at the Batch Normalization (BN) layer, and further optimized using the OpenVINO tool for quantization. The results demonstrate that our improvements effectively improve the model's inference speed while maintaining its accuracy. Compared to the original YOLOv5 model, the pruned model achieves the same accuracy without degradation, with a 36.6% redu.tion in parameter count and a 35% redu.tion in weight storage file size. Furthermore, after optimization with OpenVINO, the final model achieves a FPS of 56.

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Composite Single-Loop Model Predictive control Design for PMSM Servo System Speed Regulation Based on Disturbance Observer

Composite Single-Loop Model Predictive Control Design for PM...

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第32届中国控制与决策会议

作者： Mengdi Zhou Xiangyang Liu Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

In order to optimize the control performance of the permanent magnet synchronous motor（PMSM） in the presence of strong disturbances and model uncertainty,a composite single-loop model predictive control（MPC） based on disturbance observer（DOB） is studied in this ***,a DOB is employed to estimate the lumped disturbance including the uncertain parameters and the external disturbance in real ***,benefitting from model-based design,estimations are introduced into the prediction model of the PMSM,then a composite single-loop MPC is designed based on this ***,the optimal control input is obtained,by introducing Lagrange vector and solving the quadratic programming problem with *** results show that the proposed composite control method has a faster transient response and a better disturbance rejection *** addition,compared with the generally cascade structure,only the predictive horizon and the observer poles are needed to be adjusted,so it provides a more practical optimization algorithm for the engineering application.

关键词： Composite Single-Loop Model Predictive control Disturbance Observer Quadratic Programming Problem PMSM Servo System

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