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Asian control Conference

作者： Jinyu Li Hongchao Wei Chengda Lu Wangnian Li Quanxin Li Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, the effect of the initial state of the drilling system on trajectory tracking is taken into account in the directional drilling trajectory tracking control. Firstly, the trajectory model describing the two-dimensional drilling trajectory of the rotary steering system (RSS) is introduced. To improve the accuracy of trajectory tracking control and reduce the trajectory deviation, a fixed-time sliding mode control (FTSMC) method is introduced. Then, the stability of the closed-loop system is analyzed, and the range of controller parameters satisfying the stability condition is obtained. Finally, the effectiveness of the designed control strategy is verified by simulation experiments.

关键词： Drilling Upper bound Trajectory tracking Steering systems Stability analysis Trajectory Closed loop systems

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High-precision Calibration of Camera and IMU on Manipulator for Bio-inspired Robotic System

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Journal of Bionic engineering 2022年第2期19卷 299-313页

作者： Yinlong Zhang Wei Liang Sichao Zhang Xudong Yuan Xiaofang Xia Jindong Tan Zhibo Pang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China The School of Computer Science and Technology Xidian UniversityXi’an 710071China Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKonxville 37996USA Department of Automation Technology ABB Corporate Research Sweden72178 VästerasSweden

Inspired by box jellyfish that has distributed and complementary perceptive system,we seek to equip manipulator with a camera and an Inertial Measurement Unit(IMU)to perceive ego motion and surrounding unstructured *** robot perception,a reliable and high-precision calibration between camera,IMU and manipulator is a critical *** paper introduces a novel calibration ***,we seek to correlate the spatial relationship between the sensing units and manipulator in a joint ***,the manipulator moving trajectory is elaborately designed in a spiral pattern that enables full excitations on yaw-pitch-roll rotations and x-y-z translations in a repeatable and consistent *** calibration has been evaluated on our collected visual inertial-manipulator *** systematic comparisons and analysis indicate the consistency,precision and effectiveness of our proposed calibration method.

关键词： Bio-inspired robotic system Monocular camera IMU Manipulator Calibration

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Analysis of the Effect of Marine Currents on an Aerial-Underwater Vehicle

Analysis of the Effect of Marine Currents on an Aerial-Under...

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2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： Kepler E. W. Rodrigues Stephanie L. Brião Pedro M. Pinheiro Mauro A. B. Cunha Cláudio L.D. Machado Paulo L. J. Drews Research Group on Automation and Control Electrical Engineering Sul-rio-grandense Federal Institute (IFSul) Pelotas RS Brazil Center for Computational Science Federal University of Rio Grande (FURG) Rio Grande RS Brazil

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

Aerial-underwater vehicles present control challenges in both aerial and underwater environments, so obtaining a model for simulating the current effects experienced by these vehicles in both environments provides an important theoretical basis for analyzing dynamic behavior in closed-loop control. This article proposes the analysis of an aerial-underwater hybrid vehicle while adding the effects of ocean currents as perturbations. In this regard, it is intended to explore two numerical simulation scenarios: in the first scenario, the vehicle performs a mission in an underwater environment without the presence of ocean currents, and in the second scenario, with the inclusion of the effect of ocean currents under the same mission conditions, to illustrate the difference in the vehicle's behavior.

关键词： Adaptation models Analytical models Sea surface Simulation Perturbation methods Mathematical models Trajectory Vehicle dynamics Robots Tuning

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Quantitative benefits of the digital product passport and data sharing in remanufacturing 56

Quantitative benefits of the digital product passport and da...

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56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023

作者： Szaller, Ádám Gallina, Viola Gal, Barna Gaal, Alexander Fries, Christian Institute for Computer Science and Control Kende str. 13-17 Budapest1111 Hungary EPIC InnoLabs Nonprofit Ltd. Kende str. 13-17 Budapest1111 Hungary Fraunhofer Austria Research GmbH Theresianumgasse 7 WienA-1040 Austria Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstr. 12 Stuttgart70569 Germany

As a part of the circular economy - which aims at closing the loop of materials - remanufacturing is getting more and more attention in recent years. With remanufacturing, companies are carrying out a recovery operation of used products with the aim of rebuilding a like-new condition. As various members of the supply chain are involved in this process, and product data needed for optimal operation is often missing, remanufacturing processes can become uncertain and hard to optimize. The goal of a new European Commission initiative, the digital product passport (DPP), is to facilitate information exchange between stakeholders, in order to reduce uncertainties. DPP, assigned to a product, could contain information about e.g., its origin, composition, repair and dismantling possibilities. In the paper, the essence of DPP is introduced, and a case study is presented to show the qualitative benefits of the increased amount of information about the product. A discrete event-based simulation model of three real production lines that are (re)manufacturing cylinder heads is presented. Experiments are also conducted, where quantitative benefits of using DPP are highlighted, such as decreasing unnecessary production time, increasing ratio of remanufactured parts and increasing output. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing Systems 2023.

关键词： data sharing, simulation digital product passport remanufacturing simulation

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Energy storage for black start services:A review

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International Journal of Minerals,Metallurgy and Materials 2022年第4期29卷 691-704页

作者： Yanqi Zhao Tongtong Zhang Li Sun Xiaowei Zhao Lige Tong Li Wang Jianning Ding Yulong Ding School of Mechanical Engineering Jiangsu UniversityZhenjiang 212013China Jiangsu Collaborative Innovation Centre of Photovoltaic Science and Engineering Changzhou UniversityChangzhou 213164China Birmingham Centre for Energy Storage&School of Chemical Engineering University of BirminghamBirmingham B152TTUnited Kingdom School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen 518055China Intelligent Control&Smart Energy(ICSE)Research Group&School of Engineering University of WarwickCoventry CV47ALUnited Kingdom School of Energy and Environmental Engineering University of Science and Technology BeijingBeijing 100083China

With the increasing deployment of renewable energy-based power generation plants,the power system is becoming increasingly vulnerable due to the intermittent nature of renewable energy,and a blackout can be the worst *** current auxiliary generators must be upgraded to energy sources with substantially high power and storage capacity,a short response time,good profitability,and minimal environmental *** in the power restoration of renewable energy generators should also be *** different energy storage methods can store and release electrical/thermal/mechanical energy and provide flexibility and stability to the power ***,a review of the use of energy storage methods for black start services is provided,for which little has been discussed in the ***,the challenges that impede a stable,environmentally friendly,and cost-effective energy storage-based black start are *** energy storagebased black start service may lack supply ***,the typical energy storage-based black start service,including explanations on its steps and configurations,is *** start services with different energy storage technologies,including electrochemical,thermal,and electromechanical resources,are *** suggest that hybridization of energy storage technologies should be developed,which mitigates the disadvantages of individual energy storage methods,considering the deployment of energy storage-based black start services.

关键词： black start renewable energy electrochemical energy storage thermal energy storage electromechanical energy storage hybrid energy storage

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Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1612-1626页

作者： Yixin Wang Shuang Qiu Dan Li Changde Du Bao-Liang Lu Huiguang He the Research Center for Brain-inspired Intelligence National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of ScienceBeijing 100190 the University of Chinese Academy of Sciences Beijing 100049 the Beijing Institute of Control and Electronic Technology Beijing 100038China the School of Mathematics and Information Sciences Yantai UniversityYantai 264003China the Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of ScienceBeijingChina the Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in *** attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration *** solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal *** method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common *** adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of *** experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed *** work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.

关键词： Cycle-consistency domain adaptation electroencephalograph(EEG) multi modality variational autoencoder

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Robust control of Feeding Speed in Coal Seam Drilling Process Considering Borehole Friction

Robust Control of Feeding Speed in Coal Seam Drilling Proces...

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Chinese automation Congress (CAC)

作者： Xiao Liu Luefeng Chen Chengda Lu Haipeng Fan Min Wu Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Istanbul Turkey

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

During the coal seam drilling process, the drill string is subject to compressive deformation, compounded by unpredictable variations in formation hardness and borehole wall friction, leading to challenges in maintaining a stable feeding speed. This paper presents a novel approach by introducing uncertain parameters to describe the effects of formation hardness and borehole wall friction. Drill string axial movement model is modeled as a polyhedral system based on a lumped parameter representation. To meet industrial performance requirements, we design a robust $H_{\infty}$ controller to achieve consistent feeding speed control. Our simulation results demonstrate the controller's effectiveness in ensuring system stability despite fluctuations in formation hardness and drill string friction.

关键词： Drilling Robust control Friction Simulation Geology Velocity control Stability criteria Process control Coal Output feedback

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Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive control

Trajectory Tracking for Directional Drilling in Underground ...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Jiajun Wu Chengda Lu Hao Li Hongchao Wei Min Wu School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Automation China University of Geosciences Wuhan China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China China Coal Research Institute Beijing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the $H_{\infty}$ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment.

关键词： Drilling Uncertainty Trajectory tracking Stability criteria Predictive models Taylor series Coal mining Trajectory Thermal stability Predictive control

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Comparison Performance on SOTA Deep Learning Models for Coffee Beans Grading Inspection

Comparison Performance on SOTA Deep Learning Models for Coff...

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Asian control Conference

作者： Achmad Norman Firdaus Amalia Rakhmawati Vebi Nadhira Suprijanto Endang Juliastuti Gema Nuran Utomo Ayu Gareta Risangtuni Master Program on Instrumentation and Control Faculty of Industrial Technology Bandung Institute of Technology Bandung Indonesia Graduate Program on Engineering Physics Faculty of Industrial Technology Bandung Institute of Technology Bandung Indonesia Instrumentation Control and Automation Research Group Faculty of Industrial Technology Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Several coffee producers rely on manual inspection for quality control, which is prone to inconsistencies and time consumption. Recent studies have proposed Comparison performance on State-of-The-Art (SOTA). Deep learning models were investigated as part of the proposed intelligent computer vision for faster and more reliable coffee bean grading. Performance Deep learning models, i.e., MobileNetV3Small, ResNet50, EfficientNetV2L, and DenseNet201, were evaluated to grade the quality of coffee beans that followed a grading procedure based on Indonesian National Standard (SNI) 01-2907-2008. A specific tray was designed to position coffee beans evenly without overlap for sample preparation, which consist of about 500–600 coffee beans. The process of capturing images is conducted systematically, focusing on each type of coffee bean defect individually. For comparison of SOTA models, five classes of coffee bean defects, With 1053 beans per class, the total dataset comprises 5265 beans. This dataset was divided into 10% for testing and 90% for training. Within the training data, 12% was reserved for validation. The result from this paper shows that MobileNetV3Small achieves the highest accuracy (99.43%) with minimal memory and parameters. Its efficient design allows for robust feature extraction, crucial for distinguishing subtle defect features. While larger models like ResNet50 and EfficientNetV2L also perform well, they exhibit higher memory and parameters.

关键词： Deep learning Training Accuracy Computational modeling Training data Inspection Feature extraction

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Length Prediction Model for Heavy Plate after Cooling Bed Based on BP Neural Network 6

Length Prediction Model for Heavy Plate after Cooling Bed Ba...

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6th International Conference on ThermoMechanical Processing, TMP 2022

作者： Zhang, Weiye Wang, Yuxia Cao, Jianzhao Huang, Shaowen Dong, Zishuo School of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China Research Institute of Shandong Iron & Steel Group Co. Ltd. Jinan271104 China The State Key Laboratory of Rolling and Automation Northeastern University Shenyang110819 China

ISBN: (纸本)9787502492441

Length variation of heavy plate after cooling bed is hard to calculate accurately because of large size, uneven temperature and material property. The thermal expansion coefficient is not consistent even the same steel grade, since different steel rolling process and different thickness of the specification. In order to solve this problem, BP neural network is used to build a model to predict the length. At the same time, ReLU function is used to replace Sigmoid function to solve the problem of gradient disappearance. In addition, BP neural network, CART and the results calculated by thermal expansion coefficient are compared with the actual values. The comparison shows that the correlation coefficient R2of the BP neural network model is much higher than the calculated results of CART and thermal expansion coefficient, and the BP neural network model has a higher approximate value with the actual data, which has very strong practicability. © TMP 2022 - Proceedings. All rights reserved.

关键词： Forecasting

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