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2024 China Automation Congress, CAC 2024

作者： Wu, Ji Wang, Kai Ma, Wenwen Zhang, Jingxin Wang, Shibo China Electric Power Research Institute Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9798350368604

Accurate ultra-short-term photovoltaic (PV) power forecasting is crucial for the real-time scheduling of grid systems. However, the inherent variability of solar energy makes this task extremely challenging. To enhance PV power forecasting, this paper introduces a novel hybrid model named PVTimesNet, designed to harness the strengths of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) for effective feature extraction. PVTimesNet consists of two parallel branches: the CNN1d branch, which captures correlations between multiple variables and adjacent time steps, and the LSTM branch, which learns the temporal dependencies within the PV power sequences. The features extracted by both branches are then concatenated and passed through a fully connected layer to generate multi-step PV power forecasts. Experimental results demonstrate that for forecast horizons of 1 to 4 hours, the proposed model significantly outperforms individual models and other CNN-LSTM hybrid structures. Additionally, it exhibits superior performance compared to related methods for longer forecast horizons. © 2024 IEEE.

关键词： Hybrid power

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Finite-Time Observer Based Integral Terminal SMC for Converter-Driven Motor systems with Mismatched Disturbances

Finite-Time Observer Based Integral Terminal SMC for Convert...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Zhongding Guo, Zeyu Wang, Zuo Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Shenzhen Research Institute Southeast University Shenzhen518063 China

ISBN: (纸本)9798350303759

Converter-driven motor systems play crucial roles in contemporary industrial applications owning to the advantages of smooth starting and stepless speed regulation. For such fourth-order systems with both immeasurable states and mismatched disturbances, a finite-time control solution is proposed to address the speed tracking problem in this paper. Firstly, a finite-time observer is constructed to precisely estimate the immeasurable states and mismatched disturbances. Then, based on integral terminal sliding mode control (ITSMC), a continuous finite-time controller is designed for the systems to achieve both rapid response and superior speed tracking performance. Furthermore, a rigorous global finite-time stability analysis for the closed-loop system is presented. Finally, simulation results validate the effectiveness of the proposed control method. As compared with existing methods, the composite ITSMC algorithm design demonstrates reliable finite-time speed tracking performance and exceptional capability in disturbance rejection. © 2023 IEEE.

关键词： Disturbance rejection

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General Simultaneous Localization and Mapping Scheme for mmWave Communication systems

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IEEE Internet of Things Journal 2024年第12期11卷 22521-22536页

作者： Du, Tao Yang, Jie Wen, Chao-Kai Xia, Shuqiang Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Key Laboratory Of Measurement And Control Of Complex Systems Of Engineering Ministry Of Education The Frontiers Science Center For Mobile Information Communication And Security Nanjing210096 China Institute Of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Zte Corporation The State Key Laboratory Of Mobile Network And Mobile Multimedia Technology Shenzhen518055 China Southeast University National Mobile Communications Research Laboratory The Frontiers Science Center For Mobile Information Communication And Security Nanjing210096 China

Utilizing high-resolution antenna arrays and wide bandwidth of the millimeter-wave (mmWave) spectrum in 5G new radio (NR) mmWave communication systems holds the potential for high-throughput data transmission while enabling user localization and environmental mapping. However, the majority of existing simultaneous localization and mapping (SLAM) algorithms rely on methods akin to the extended Kalman filter for generating initial map features. These methods prove ineffective when the measurement dimension is insufficient. Furthermore, there is a notable absence of research exploring mmWave prototype systems to evaluate and compare the performance and viability of various SLAM algorithms. To address these challenges, we propose an innovative probability hypothesis density (PHD) generation scheme for birth events and have developed a prototype system. Our approach, referred to as PHD-SLAM, exhibits remarkable effectiveness even in scenarios where the measurement dimension falls short of map features. This means it can function seamlessly with only delay or angle information available. Additionally, we have designed a 28GHz mmWave beam scanning prototype system that leverages the 5G NR frame for accomplishing SLAM algorithms. Following this, we conducted extensive simulations and experimental evaluations to gauge the performance of several leading-edge SLAM algorithms under diverse mmWave circumstances, encompassing PHD-based and belief propagation (BP) SLAM algorithms. Our analysis reveals that both PHD and BP SLAM can achieve agent localization precision within a decimeter and mapping precision within a meter, capitalizing on the angle parameters of mmWave signals. While BP SLAM showcases reduced computational demand, its estimation precision is marginally inferior to that of PHD-SLAM. © 2014 IEEE.

关键词： Belief propagation

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Apollonius Partitions Based Pursuit-evasion Game Strategies by Q-Learning Approach 41

Apollonius Partitions Based Pursuit-evasion Game Strategies ...

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第41届中国控制会议

作者： Qing Wang KaiQi Wu JianFeng Ye YongBao Wu Lei Xue SEU-Monash Joint Graduate school Southeast University The College of Software Engineering Southeast University School of Automation Southeast UniversityKey Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper studies a classical single pursuer and single evader pursuit-evasion *** pursuer attempts to capture the slower evader who aims to extend its lifetime during the *** simplify this question,requiring the evader to take fixation strategy which is choosing the farthest point in its current dominant region as aimpoint and moving at a constant *** the pursuer is faster than the ***,the speed ratio is a *** instaneous state space will be partitioned into pursuer's dominant zone and evader's dominant zone by the generalized Apollonius *** pursuit strategy is based on minimizing the area of the evader's dominant ***,we propose a supermodular game for this ***,the existence of the Nash equilibrium is *** results based on Q-learning are presented to solve the problem,which shows the effectiveness of this method.

关键词： Pursuit-evasion game Nash equilibrium Q-learning

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Multiple AMR Rigid Formation control With Collision Avoidance Based On MADDPG 41

Multiple AMR Rigid Formation Control With Collision Avoidanc...

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第41届中国控制会议

作者： Kaiqi Wu Qing Wang YongBao Wu Jian Liu Lei Xue The College of Software Engineering Southeast University SEU-Monash Joint Graduate school Southeast University School of Automation Southeast UniversityKey Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Recently,the formation control of multiple autonomous mobile robots(AMRs) have gained significant attention,and autonomous mobile robots(AMRs) have applied to all aspects of our ***-agent reinforcement learning is used to solve the autonomously sequential decision-making problem of agents in a common environment with competition or ***,we present a utility function and a reward function to achieve formation control with collision avoidance for a rigid AMRs system,and build a simulation environment to meet environmental requirements based on MPE.

关键词： Formation control Reinforcement learning Rigid system Collision avoidance

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A High-Quality View Synthesis system for Sparse Rgb-D Input

SSRN

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SSRN 2024年

作者： Zhao, Ziyu Lu, Xiaobo Zhou, Sifan Chiang, Patrick Y.I.N. School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Key Laboratory of ASIC and System Fudan University Shanghai China

Recent research in neural rendering has achieved impressive results in the field of novel view synthesis. However, high-quality view synthesis with sparse source views remains a challenging task, as the reduction in the number of source views significantly degrades the synthesis quality. In this paper, we present a novel view synthesis system that exploits RGB-D pairs from sparse source views. First, we introduce a two-branch feature extraction network to embed the geometric clues from depth maps into the texture information of color images and fuse it with regularized Fourier mapping features. Second, a distance-aware feature query method, which constructs multi-level feature maps, is proposed to ensure high-fidelity details across different distance views. Then, we apply an occupancy network to perform occupancy prediction on raw point cloud. By integrating occupancy probabilities and extracted features, we use a prediction network with geometric information as an additional input to obtain the color and density of sampled points, and synthesize the target view using neural volume rendering. To prevent model degradation and enhance efficiency, we design an efficient sampling strategy based on Gaussian Mixture Model. Moreover, we introduce pixel-wise local regularization and full-image global geometric regularization to optimize the model. Extensive experiments show that the proposed method can achieve the state-of-the-art results for novel view synthesis with sparse inputs, and the model performance has been significantly improved in different scenarios. © 2024, The Authors. All rights reserved.

关键词： Geometry

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Research on Rolling Bearing Fault Diagnosis Based on Classification Generative Adversarial Network

Research on Rolling Bearing Fault Diagnosis Based on Classif...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Kang, Ma Hongjun, Wang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science and Technology Cooperation Base for Intelligent Sensing and Control of High-End Equipment Beijing100192 China Beijing Key Laboratory of Measurement and Control of Electromechanical System Beijing100192 China

ISBN: (纸本)9783031494123

In practical engineering, it is usually difficult to label monitoring data, and the fault diagnosis accuracy is not high in strong noise environment. To solve the above problems, this paper uses Cwt-CatGAN method to process signals and diagnose faults. In this method, CWT is first used to convert one-dimensional original vibration signals into two-dimensional time–frequency graphs as the input of classification generation admixture network. Then, the CatGAN model is obtained through the adversarial training process, and fake samples similar to the extraction distribution of CWT are generated. Finally, the input samples are clustered into certain categories. In addition, the performance of the Cwt-CatGAN method is verified by using the classical rotating machine data set, the experiment is compared with the unsupervised fault diagnosis model CatAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout

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Chinese Journal of Aeronautics 2022年第10期35卷 301-312页

作者： Jiqiang ZHANG Xiangwei KONG Xueyi LI Zhiyong HU Liu CHENG Mingzhu YU School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of EducationNortheastern UniversityShenyang 110819China Liaoning Province Key Laboratory of Multidisciplinary Design Optimization of Complex Equipment Northeastern UniversityShenyang 110819China Angang Steel Company Limited Anshan 114021China

Bearing pitting,one of the common faults in mechanical systems,is a research hotspot in both academia and *** fault diagnosis methods for bearings are based on manual experience with low diagnostic *** study proposes a novel bearing fault diagnosis method based on deep separable convolution and spatial dropout *** separable convolution extracts features from the raw bearing vibration signals,during which a 3×1 convolutional kernel with a one-step size selects effective features by adjusting its *** similarity pruning process of the channel convolution and point convolution can reduce the number of parameters and calculation quantities by evaluating the size of the weights and removing the feature maps of smaller *** spatial dropout regularization method focuses on bearing signal fault features,improving the independence between the bearing signal features and enhancing the robustness of the model.A batch normalization algorithm is added to the convolutional layer for gradient explosion control and network stability *** validate the effectiveness of the proposed method,we collect raw vibration signals from bearings in eight different health *** experimental results show that the proposed method can effectively distinguish different pitting faults in the bearings with a better accuracy than that of other typical deep learning methods.

关键词： Batch normalization Convolutional neural network Fault diagnosis Similarity pruning Spatial dropout

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Constraint-Following Based Adaptive Robust control for Underactuated Mechanical systems

Constraint-Following Based Adaptive Robust Control for Under...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Cui Wei Ye-Hwa Chen Yanling Wei Institute of Intelligent Manufacturing Nanjing Tech University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA USA Key Laboratory of Measurement and Control of Complex System of Engineering School of Automation Southeast University Nanjing China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This paper introduces an adaptive robust control approach tailored for underactuated mechanical systems encountering matched and mismatched uncertainty, employing a constraint-following methodology. The control strategy unfolds in two phases: initially, a nominal control scheme is devised neglecting uncertainty and deviations in initial conditions from constraints. Subsequently, uncertainty is categorized into matched and mismatched components, ensuring that mismatched uncertainties remain unobservable. Leveraging the structural characteristics of the uncertainty bound, a novel segmented adaptive law is proposed and seamlessly integrated into the adaptive robust control framework. By employing the Lyapunov minimax approach, the method ensures uniform boundedness and uniform ultimate boundedness simultaneously, thereby ensuring approximate adherence to constraints for underactuated mechanical systems facing both matched and mismatched uncertainties alongside initial condition deviations.

关键词： Robust control Uncertainty Adaptive systems Costs Automation Nonlinear dynamical systems Mechanical systems Closed loop systems Robots

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Data-Driven Multi-step Nonlinear Model Predictive control for Industrial Heavy Load Hydraulic Robot

arXiv

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arXiv 2024年

作者： Ma, Dexian Zhou, Bo Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education Nanjing210096 China

Automating complex industrial robots requires precise nonlinear control and efficient energy management. This paper introduces a data-driven nonlinear model predictive control (NMPC) framework to optimize control under multiple objectives. To enhance the prediction accuracy of the dynamic model, we design a single-shot multi-step prediction (SSMP) model based on long short-term memory (LSTM) and multilayer perceptrons (MLP), which can directly obtain the predictive horizon without iterative repetition and reduce computational pressure. Moreover, we combine offline and online models to address disturbances stemming from environmental interactions, similar to the superposition of the robot’s free and forced responses. The online model learns the system’s variations from the prediction mismatches of the offline model and updates its weights in real time. The proposed hybrid predictive model simplifies the relationship between inputs and outputs into matrix multiplication, which can quickly obtain the derivative. Therefore, the solution for the control signal sequence employs a gradient descent method with an adaptive learning rate, allowing the NMPC cost function to be formulated as a convex function incorporating critical states. The learning rate is dynamically adjusted based on state errors to counteract the inherent prediction inaccuracies of neural networks. The controller outputs the average value of the control signal sequence instead of the first value. Simulations and experiments on a 22-ton hydraulic excavator have validated the effectiveness of our method, showing that the proposed NMPC approach can be widely applied to industrial systems, including nonlinear control and energy management. Copyright © 2024, The Authors. All rights reserved.

关键词： Excavators

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