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PI-funnel and inverse hysteresis compensation cascade control of smart soft dielectric elastomer actuator

control Theory and Technology 2024年第4期22卷 552-567页

作者： Peng Huang Yawu Wang Yue Zhang Jundong Wu Chun-Yi Su School of Automation China University of GeosciencesWuhan430074HubeiChina Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074HubeiChina Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan430074HubeiChina Gina Cody School of Engineering and Computer Science Concordia UniversityMontrealQCH3G 1M8Canada

Smart soft dielectric elastomer actuators(SSDEAs)possess wide applications in soft robotics due to their properties similar to natural muscles,including large deformation ratio,high energy density,and fast response ***,the complicated asymmetric and rate-dependent hysteresis property,creep property and quadratic input property of the SSDEA pose enormous challenges to its dynamic modeling and motion *** this paper,first,we construct the dynamic model of the SSDEA by connecting a square module,a one-sided Prandtl–Ishlinskii(OSPI)model and a linear system in series to describe the above *** key and innovative aspect of the dynamic modeling lies in cascading the square module in series with the OSPI model to construct the asymmetric hysteresis ***,a PI-funnel and inverse hysteresis compensation(PIFIHC)cascade control method of the SSDEA is proposed to actualize its tracking control *** performing the inversion operation on the asymmetric hysteresis model,the inverse hysteresis compensation controller(IHCC)is designed to compensate the asymmetric hysteresis property and quadratic input property of the *** addition,a PI-funnel controller is designed to cascade with the IHCC to construct the PIFIHC cascade controller to obtain a good tracking ***,the stability analysis of the PIFIHC cascade control system of the SSDEA is performed to theoretically prove that the tracking error can be controlled within the performance funnel and the steady-state error converges to ***,several practical tracking control experiments of the SSDEA are conducted,and RRMSEs are less than 2.30%for all *** experimental results indicate the effectiveness and feasibility of the proposed PIFIHC cascade control method of the SSDEA.

关键词： Smart soft dielectric elastomer actuator Dynamic modeling Inverse hysteresis compensation controller PI-funnel controller Cascade controller

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Single-Domain Generalization Combining Geometric Context Toward Instance Segmentation of Track Components

Single-Domain Generalization Combining Geometric Context Tow...

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International Joint Conference on Neural Networks (IJCNN)

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

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

A stable track components segmentation model should have consistent performance across a broad spectrum of railroad conditions, particularly in unfamiliar locations. Despite this, satisfying this requirement proves challenging when working with a limited track dataset, as there is a substantial domain shift between the given dataset and unobserved distributions. The goal of this paper is to improve the generalization ability of track component segmentation in situations where single-domain training data is available. Toward this end, a novel track component instance segmentation method combining the geometric context is proposed. First, we design an initial mask prediction head (IMPH) that utilizes predicted box output from the object detector to generate initial masks by merging the geometric priors of track components. Meanwhile, a multiscale feature fusion structure is introduced to encourage IMPH to better capture the geometric context. Then, a final mask refinement head (FMRH) is introduced to get higher quality masks with a geometry-gated aggregation strategy. On the basis of experiments on track datasets, it was determined that the heads can be incorporated with several types of detection frameworks and have demonstrated consistent generalization enhancements across multiple object detectors. Furthermore, our method substantially enhances segmentation performance on various unobserved domains.

关键词： Instance segmentation Geometry Accuracy Neural networks Merging Training data Detectors

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Smoky Vehicle Detection Based on Improved Vision Transformer 21

Smoky Vehicle Detection Based on Improved Vision Transformer

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Yuan, Li Tong, Shuzhen Lu, Xiaobo School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781450389853

The harmful exhaust emissions of fuel vehicles in theworld are damaging to human health and the environment, thus detecting smoky vehicles from real road environment is significant. At present, methods of smoky vehicle detection based on deep learning have the problem of high false-positive rate. To improve the performance, a two-stage video smoky vehicle detection algorithm based on the smoke classification in the core region from detected vehicle object boxes is proposed in this paper. Specifically, the vehicle object detection is realized by the algorithm based on YOLOv3. The smoke classification is realized by combining Vision Transformer and distillation, and the loss function is optimized in the training process. Experimental results on our smoky vehicle dataset have shown that the improved model achieves an F1 score over 0.4, precision over 0.4, recall nearly 0.1 improvement compared with the basic model, which can effectively reduce the false-positive rate during detection.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Smoke

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation 26

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Huang, Nanxin Xu, Chi School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models. © 2025 IEEE.

关键词： Industrial robots

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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A Delay-free control Method Based On Function Approximation And Broadcast For Robotic Surface And Multiactuator systems

arXiv

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arXiv 2024年

作者： Zhao, Yuchen School of Automation Southeast University Jiangsu Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Jiangsu Nanjing210096 China

Robotic surface consisting of many actuators can change shape to perform tasks, such as facilitating human-machine interactions and transporting objects. Increasing the number of actuators can enhance the robot's capacity, but controlling them requires communication bandwidth to increase equally in order to avoid time delays. We propose a novel control method that has constant time delays no matter how many actuators are in the robot. Having a distributed nature, the method first approximates target shapes, then broadcasts the approximation coefficients to the actuators, and relies on themselves to compute the inputs. We build a robotic pin array and measure the time delay as a function of the number of actuators to confirm the system size-independent scaling behavior. The shape-changing ability is achieved based on function approximation algorithms, i.e. discrete cosine transform or matching pursuit. We perform experiments to approximate target shapes and make quantitative comparison with those obtained from standard sequential control method. A good agreement between the experiments and theoretical predictions is achieved, and our method is more efficient in the sense that it requires less control messages to generate shapes with the same accuracy. Our method is also capable of dynamic tasks such as object manipulation. © 2024, CC BY.

关键词： Time delay

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Framework and key Technologies for High-speed Railway Comprehensive Transport Hub 3

Framework and Key Technologies for High-speed Railway Compre...

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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Xiong, Gang Zhang, Wei Liu, Xiaoming Yuan, Zhiming Zhu, Fenghua Zhang, Zundong Chinese Academy of Sciences State Key Laboratory for Multimodal Artificial Intelligence Systems Beijing China North China University of Technology School of Electrical and Control Engineering Beijing China China Academy of Railway Science Communication Signal Research Institute Beijing China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9798350318470

The surge in urban population density in China has underscored the vital role of high-speed rail and subway systems in urban transit. Comprehensive transportation hubs, notably high-speed rail passenger terminals, have emerged as linchpins for seamless multimodal transit both within and beyond urban centers. China's railway sector vigorously champions the innovative concept of "station-city integration"and has articulated a forward-looking "16-word policy"for modern railway stations: "Seamless integration, environmental friendliness, economic viability, and intelligent accessibility". The judicious allocation of resources and the augmentation of coordination and control capabilities within comprehensive transportation hubs are imperative for the harmonious evolution of urban transit. The implementation of intelligent control systems for transportation hubs has emerged as a pivotal area of research. This study delves into the framework and critical technologies for high-speed railway comprehensive transport hubs, encompassing various components and their intricate interplay, including transport modalities, infrastructure, travelers, environmental factors, and information systems, thus forming a prototypical cyber-physical-social system. Addressing the prevalent and pressing imperative for the advancement of "station-city integration"high-speed railway comprehensive transport hubs, this research endeavor aims to surmount bottleneck challenges and refine the oversight of passenger flows at high-speed rail terminals. The findings hold noteworthy theoretical import and portend promising practical applications. © 2023 IEEE.

关键词： Integration

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Road Underground Target Detection in Ground Penetrating Radar Images Based on YOLO Models

Road Underground Target Detection in Ground Penetrating Rada...

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2023 China Automation Congress, CAC 2023

作者： Li, Ting Xue, Wei Yang, Xinya Wu, Dongchang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350303759

Ground penetrating radar (GPR) is extensively employed for subsurface road target detection, offering benefits such as convenience, nondestructive testing, rapid data acquisition, and superior resolution. Despite these advantages, interpreting GPR data often depends on the expertise of professionals, resulting in low detection efficiency and low accuracy. To address these challenges, this study introduces an intelligent detection technique for GPR images, utilizing an enhanced YOLOv5 framework. First, considering the problems of the small amount of GPR image datasets and the unclear characteristics caused by the complex underground media, a Dense-C3 module is built by utilizing the structure of DenseNet to enhance the network's capability for extracting features. Subsequently, a channel and spatial hybrid attention module is introduced into the backbone for feature refinement and improving the efficiency. Finally, the multi-class focal loss function is devised to enhance the precision in cases of imbalanced sample classes. Experimental results show that the proposed model surpasses the original YOLOv5 model and various contemporary advanced models. © 2023 IEEE.

关键词： Efficiency

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E-Nose-Based Cross-Domain Recognition of Combustion-Supporting Agents via M-BFFNet 42

E-Nose-Based Cross-Domain Recognition of Combustion-Supporti...

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42nd Chinese control Conference, CCC 2023

作者： Wang, De-Xiu Meng, Qing-Hao Han, Rui-Xue Li, Hong-Yue Hou, Hui-Rang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China Tianjin Navigation Instruments Research Institute Tianjin300131 China

ISBN: (纸本)9789887581543

Identification of the type of combustion-supporting agents (CSAs) by an electronic nose (e-nose) is severely limited due to the absence of untested gas concentration in the e-nose training set. In order to solve this problem, this study designs an e-nose for classifying four CSAs (Diesel, Gasoline, Ethanol and Thinner), and builds cross-concentrations data set. This study uses Gramian Angular Field method that converts one-dimensional time series data to two-dimensional data, and proposes an algorithm named the Multi-Branch Feature Fusion Network (M-BFFNet). This algorithm passes the one-dimensional and two-dimensional data through two different networks and obtains two types of features. First, the algorithm can strength the one-dimensional raw data, integrate the advantages of different networks, and fuse the characteristics of different networks. Second, it can learn the characteristics of volatile gas with specific concentrations to identify the unknown concentrations. Last, a feature fusion module is designed to improve the algorithm ability of learning odor fingerprint. In order to verify the superiority of the algorithm, this study tests the recognition performance of the e-nose based systems and compares the proposed M-BFFNet with traditional algorithms. The results show that the proposed M-BFFNet improves the identification accuracy compared to other algorithms. According to the analysis of this work, the M-BFFNet can be used to identify CSAs with unknown concentrations. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Electronic nose

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Gesture Recognition Using FMCW Radar Based on Deep Neural Network

Gesture Recognition Using FMCW Radar Based on Deep Neural Ne...

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2023 China Automation Congress, CAC 2023

作者： Wang, Rui Xue, Wei Peng, Jiao Yang, Xinya School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350303759

In recent years, radar based gesture recognition technology has attracted more and more attention. Among different types of radars, frequency modulated continuous wave (FMCW) radar has the greatest application potential due to its high range and velocity resolution and low cost. However, insufficient gesture information extraction and interference signals affect the performance of FMCW radar gesture recognition. To address the problems, a gesture recognition method based on multi-dimensional features and deep neural network is proposed in this paper. First, the range-time map (RTM) and velocity-time map (VTM) of the gesture are constructed from the range-Doppler map (RDM) with two-dimensional fast Fourier transform (2D-FFT). Then, the angle-time map (ATM) is constructed with the multiple signal classification (MUSIC) algorithm. Subsequently, RTM, VTM and ATM are normalized and adaptively filtered to suppress interference signals, and the three maps are fused to construct the range-velocity-angle-time map (RV ATM) of the gesture. Finally, an improved VGG16 network with self-attention mechanism module is used for feature extraction and recognition. The experimental results show that the proposed method achieves an average accuracy of 98.3% for six gestures, and the improved VGG16 network outperforms other traditional convolutional neural networks. © 2023 IEEE.

关键词： Deep neural networks

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