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IEEE International Conference of Intelligent Robotic and control engineering (IRCE)

作者： Xuefeng Sun Qingxin Meng Jundong Wu Mingliang Zhu School of Automation China University of Geosciences Wuhan Hubei China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan Hubei China

As the basic construction of flexible mechanical systems, the flexible beam and its control problem have attracted widespread attention in recent years. This paper takes a flexible beam with pneumatic soft actuators (FBPSA) as the research object and studies its phenomenological modeling and end-point trajectory tracking control strategy. Firstly, we develop an experimental platform for the FBPSA and perform tests on it, gathering its input and output data. Using these collected data, we analyzed the motion characteristics of the FBPSA. Subsequently, a phenomenological model is established to describe the motion characteristics of the system, and the parameters of this model are identified through a large amount of the collected experimental data. Then, a combined feedforward-feedback control strategy is proposed to achieve the end-point trajectory tracking control of the system. Finally, three sets of experiments are carried out to verify the accuracy of the established model and the effectiveness and superiority of the control strategy.

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A Novel Rate of Penetration Fusion Modeling Method based on Improved Dung Beetle Optimization Algorithm and Support Vector Regression 26

A Novel Rate of Penetration Fusion Modeling Method based on ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Zhou, Yang Zhang, Zihan Chen, Shipeng Wu, Min Chen, Xin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Future Technology China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798331521950

The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. However, ROP modeling in deep geological drilling is complicated by nonlinearity, diverse working conditions, and high-dimensional variations. To overcome these challenges, a fusion modeling approach for ROP is proposed. First, the fuzzy C-means clustering method is applied to classify drilling data into different working con-ditions. Based on this classification, support vector regression is employed to develop ROP sub-models, effectively addressing nonlinearity. To further enhance model accuracy, an improved dung beetle optimization algorithm (IDBO) is designed to deter-mine optimal model parameters and integrate the sub-models, thereby resolving issues related to multiple working conditions and high-dimensional variations. The IDBO incorporates four key enhancements, average weight, chaos disturbance, modified local search, and re-updating of the best solution, to strength-en its global search capability. Comparative results using the IEEE CEC2017 benchmark test functions demonstrate that the proposed algorithm outperforms others in 12 test functions, highlighting its strong global optimization ability. Additionally, results from real-world drilling data validate the effectiveness of the proposed modeling approach in practical applications. © 2025 IEEE.

关键词： Clustering algorithms

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Speech Emotion Recognition Based on Syllable-Level Feature Extraction

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SSRN 2023年

作者： Rehman, Abdul Liu, Zhen-Tao Wu, Min Cao, Wei-Hua Jiang, Cheng-Shan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Speech emotion recognition systems have high computational requirements for deep learning models and low generalizability mainly because of the poor reliability of emotional measurements across multiple corpora. To solve these problems, we present a speech emotion recognition system based on a reductionist approach of decomposing and analyzing syllable-level ***-spectrogram of an audio stream is decomposed into syllable-level components, which are then analyzed to extract statistical features. The proposed method uses formant attention, noise-gate filtering, and rolling normalization contexts to decrease contextual differences and increase focus the attention on the structure of a formant. A set of syllable-level formant features is extracted and fed into a single hidden layer neural network that makes predictions for each syllable as opposed to the conventional approach of using a sophisticated deep learner to make sentence-wide *** syllable level predictions help to lower the aggregated error in utterance level cross-corpus predictions. The experiments on IEMOCAP (IE), MSP-Improv (MI), and RAVDESS (RA) databases show that the method archives better than the state-of-the-art cross-corpus unweighted accuracy of 47.6% for IE to MI and 56.2% for MI to IE. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Blockchain-based consensus study on distributed control systems 1

Blockchain-based consensus study on distributed control syst...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Zhu, Jing Deng, Hongchang Li, Xiang Yuan, Yong Wang, Feiyue College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Mathematics Renmin University of China Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

In this paper, two blockchain-based consensus mechanisms are proposed for distributed control systems, those are the proof-of-work-based (PoW-based) and proof-of-stake-based (PoS-based) consensus mechanisms. The basic idea and process of these blockchain-based consensus mechanisms are introduced, giving rise to the dynamic, impartial and efficient consensus compared with the consensus protocol achieved by traditional control theories. © 2021 IEEE.

关键词： Multi agent systems

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Novel resonant pressure sensor based on piezoresistive detection and symmetrical in-plane mode vibration

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Microsystems & Nanoengineering 2020年第1期6卷 165-175页

作者： Xiangguang Han Qi Mao Libo Zhao Xuejiao Li Li Wang Ping Yang Dejiang Lu Yonglu Wang Xin Yan Songli Wang Nan Zhu Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesXi’an Jiaotong University710049 Xi’anChina School of Mechanical Engineering Xi’an Jiaotong University710049 Xi’anChina Xi’an Flight Automatic and Control Institute 710049 Xi’anChina

In this paper,a novel resonant pressure sensor is developed based on electrostatic excitation and piezoresistive *** measured pressure applied to the diaphragm will cause the resonant frequency shift of the *** working mode stress–frequency theory of a double-ended tuning fork with an enhanced coupling beam is proposed,which is compatible with the simulation and experiment.A unique piezoresistive detection method based on small axially deformed beams with a resonant status is proposed,and other adjacent mode outputs are easily *** to the structure design,high-vacuum wafer-level packaging with different doping in the anodic bonding interface is fabricated to ensure the high quality of the *** pressure sensor chip is fabricated by dry/wet etching,high-temperature silicon bonding,ion implantation,and wafer-level anodic *** results show that the fabricated sensor has a measuring sensitivity of~19 Hz/kPa and a nonlinearity of 0.02%full scale in the pressure range of 0–200 kPa at a full temperature range of−40 to 80°*** sensor also shows a good quality factor>25,000,which demonstrates the good vacuum ***,the feasibility of the design is a commendable solution for high-accuracy pressure measurements.

关键词： resonant mode anodic

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Symplectic Stiffness Method for the Buckling Analysis of Hierarchical and Chiral Cellular Honeycomb Structures

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SSRN 2023年

作者： Jin, Mingzhu Hou, Xiuhui Zhao, Wenhao Deng, Zichen School of Aeronautics Northwestern Polytechnical University Xi’an710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an710072 China

Structure-function-material integration design based on the buckling instability of flexible cellular structures has attracted much attention. According to the reversible and repeatable characteristics of buckling deformation, phonon bandgap switches, the autonomous drive of soft robots, programmable logic controllers, and reusable spacecraft landing buffer devices can be designed. Based on the Hamiltonian variational principle and symplectic eigen expansion, a theoretical analysis model of the symplectic stiffness method is established, and analytical expressions for the stiffness of several typical buckling modes of elastically supported beams are obtained. The analytical closed-form expressions for the macroscopic buckling strength of honeycombs are obtained according to the bending moment equilibrium equation. The buckling modes of regular hexagonal, hierarchical hexagonal, and tri-chiral honeycombs are obtained by numerical simulation and experimental verification, and some novel higher-order modes are observed. Uniaxial compression experiments reveal that these higher-order buckling modes can be triggered by adjusting the geometric parameters of the unit cells. The high agreement between experimental, numerical, and existing literature results for the failure surfaces of cellular structures verifies the effectiveness of the proposed symplectic stiffness method. In a word, the buckling modes can be designed to meet different functional requirements by changing the initial configuration of flexible cellular structures. This provides a new idea and theoretical guidance for the development of metamaterials and structures with tailored properties. © 2023, The Authors. All rights reserved.

关键词： Stiffness

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A Novel Ceramic Surface Based Constant Force control Method for Flatness Inspection

SSRN

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SSRN 2023年

作者： Chen, Xin Gao, Xu Zhou, Yang Qin, Lang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

A constant force control method of the robot based on a force sensor is proposed for the difficult point of high difficulty and low accuracy of ceramic surface flatness inspection. This paper proposed a method that combines a constant impedance controller for normal contact force with a posture compliance controller. The method decomposes force control and position control orthogonally to simplify the complex three-dimensional surface model of ceramics. The method solves the problem that the end-effector is difficult to be perpendicular to the contact surface due to the change of ceramic surface shape. Moreover, the proposed method can also detect whether the ceramic surface is flat or not by the change of the force sensor's indication and the change of the actual end position of the robot arm. Finally, the defect detection experiments are conducted on a plane and on a complex surface, respectively. The experiments demonstrate the adaptability and fast convergence of the method proposed in this paper, which meets the needs of high precision and fast response of industrial processes. © 2023, The Authors. All rights reserved.

关键词： Force control

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A Novel Visual Perception Framework for Unmanned Aerial Vehicles: Challenges and Approaches

A Novel Visual Perception Framework for Unmanned Aerial Vehi...

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2021 China Automation Congress, CAC 2021

作者： Li, Xuan Duan, Haibin Mo, Hong Wang, Fei-Yue Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen518055 China The State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical Engineering Beihang University Beijing100083 China The School of Electrical and Information Engineering Changsha University of Science and Technology Changsha410114 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781665426473

Change detection (CD) is an important preprocessing process in visual perception. Due to the difficulties of collection and labeling images in water scnens, the design and validation of the change detection algorithm is more difficult than expected. In this paper, we present a novel visual perception framework based on parallel vision for unmanned aerial vehicle (UAV). The visual perception framework consists of artificial scenes, computational experiments and parallel execution. In artificial scenes, we construct a water scene and automatically generated pixel-level image annotation. Next, computational experiments are carried out on different domain dataset. The experiment results show that: 1) mixed images effectively improve the performance of change detection model;2) domain shift problems still greatly affect the algorithm performance. © 2021 IEEE

关键词： Change detection

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Dynamic Sensing Modeling of Dielectric Elastomer Sensors Based on GRU Neural Network

Dynamic Sensing Modeling of Dielectric Elastomer Sensors Bas...

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IEEE International Conference of Intelligent Robotic and control engineering (IRCE)

作者： Mingyi Zhang Peng Huang Yawu Wang School of Automation China University of Geosciences Wuhan Hubei China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan Hubei China

Dielectric elastomer sensor (DES) is a flexible sensor that can perform free bending deformation, thus it has broad application prospects in the fields of medical electronics, wearable devices, soft robots, etc. Previous studies have mostly focused on exploring the static sensing characteristics of the DES. Considering that the DES may be used in dynamic conditions, it is very meaningful to study its dynamic sensing characteristics to broaden its application ranges. In this paper, a dynamic sensing model of the DES is established based on the gate recurrent unit (GRU) neural network. Firstly, the structure of the DES and the construction of the experimental system are introduced. In addition, the dynamic sensing characteristics of the DES are analyzed by conducting several sets of experiments, which shows that the DES has significant rate-dependent hysteresis nonlinearities, multivalued mapping and memory characteristics. After that, the dynamic sensing model of the DES is built based on the GRU neural network to describe the above dynamic sensing characteristics. Next, the dynamic displacement and force sensing models of the DES are trained according to the dynamic displacement-capacitance and dynamic force-capacitance experimental data, respectively. Finally, several experiments are performed to verify the effectiveness and generalization ability of the established dynamic sensing model.

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Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

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Chinese Journal of Aeronautics 2020年第2期33卷 427-438页

作者： Yongbo LI Xiaoqiang DU Fangyi WAN Xianzhi WANG Huangchao YU MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical UniversityXi’an 710072China School of Mechanical Engineering Northwestern Polytechnical UniversityXi’an 710072China Institute of Unmanned System National University of Defense TechnologyChangsha 410073China

Rotating machinery is widely applied in industrial *** diagnosis of rotating machinery is vital in manufacturing system,which can prevent catastrophic failure and reduce financial ***,Deep Learning(DL)-based fault diagnosis method becomes a hot *** Neural Network(CNN)is an effective DL method to extract the features of raw data *** paper develops a fault diagnosis method using CNN for InfRared Thermal(IRT)***,IRT technique is utilized to capture the IRT images of rotating ***,the CNN is applied to extract fault features from the IRT *** the end,the obtained features are fed into the Softmax Regression(SR)classifier for fault pattern *** effectiveness of the proposed method is validated using two different experimental *** show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.

关键词： Convolutional neural network Feature extraction Infrared thermography(IRT) Intelligent fault diagnosis Rotating machinery

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