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arXiv 2021年

作者： Zhang, Chunyang Gao, Qing Deng, Yue Qiu, Jianbin School of Automation Science and Electrical Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China School of Astraunotics Beihang University Beijing China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

The fundamental problem of stabilizing a general non-affine continuous-time nonlinear system is investigated via piecewise affine linear models (PALMs) in this paper. A novel integral sliding-mode parallel control (ISMPC) approach is developed, where an uncertain piecewise affine system (PWA) is constructed to model a non-affine continuous-time nonlinear system equivalently on a compact region containing the origin. A piecewise sliding-mode parallel controller is designed to globally stabilize the PALM and, consequently, to semi-globally stabilize the original nonlinear system. The proposed scheme enjoys three favorable features: i) some restrictions on the system input channel are eliminated, thus the developed method is more relaxed compared with the published approaches;ii) it is convenient to be used to deal with both matched and unmatched uncertainties of the system;and (iii) the proposed piecewise parallel controller generates smooth control signals even around the boundaries between different subspaces, which makes the developed control strategy more implementable and reliable. Moreover, we provide discussions about the universality analysis of the developed control strategy for two kinds of typical nonlinear systems. Simulation results from two numerical examples further demonstrate the performance of the developed control approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Nonlinear systems

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Short-Term Photovoltaic Power Prediction Modeling Based on AdaBoost Algorithm and Elman 10

Short-Term Photovoltaic Power Prediction Modeling Based on A...

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10th International Conference on Power and Energy systems, ICPES 2020

作者： Sun, Wenxuan Zhang, Tao Tao, Ran Wang, Anna Northeastern University College of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665404945

In recent years, with the rapid expansion of the installed capacity of renewable energy systems, the availability, stability and quality of smart grids have become increasingly important[1]. The application of renewable energy production forecasting has also been rapidly developed, especially in the field of solar photovoltaic (PV)[2][3]. In the example of solar PV output prediction, machine learning and hybrid technologies have been implemented for many applications. In this paper, a high-precision PV system output power prediction model based on improved AdaBoost and Elman is proposed. Multiple model using integrated AdaBoost algorithm fusion with the bat algorithm for the parameters of optimized combination of weak Elman neural network predictor to become a higher prediction precision, the method of strong predictor of the model can according to the weather information, such as temperature, solar radiation and the history of the output of the PV system data, the probability of photovoltaic power generation for 12 hours and deterministic prediction. The prediction accuracy of the model is determined by Root Mean Squared Error (RMSE). Experimental results show that the prediction accuracy of this algorithm is better than that of other benchmark models, and the algorithm can effectively predict the volatility and irregularity of complex time series. © 2020 IEEE.

关键词： Adaptive boosting

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Research on PMSM Vector control System Based on Fuzzy PI Parameter Self-Tuning 10

Research on PMSM Vector Control System Based on Fuzzy PI Par...

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10th International Conference on Power and Energy systems, ICPES 2020

作者： Ding, Jianhua Zhang, Tao Tao, Ran Wang, Anna Northeastern University College of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665404945

Based on the analysis of the permanent magnet synchronous motor (PMSM) mathematical model, a PMSM rotor field-oriented double closed-loop vector control system is constructed. Aiming at the defects of slow speed response, large overshoot, severe torque ripple, and poor anti-disturbance ability in the traditional PI controller used in the system speed loop, a fuzzy PI parameter self-tuning controller is proposed. The controller can set the two parameters of proportional and integral online according to fuzzy control rules. The system simulation model is built based on Matlab/Simulink. The simulation results show that the fuzzy PI parameter self-tuning controller has the advantages of fast dynamic response, high steady-state precision and strong anti-disturbance capability. © 2020 IEEE.

关键词： Permanent magnets

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Design simulation and structure optimization of plunger assembly closing 2

Design simulation and structure optimization of plunger asse...

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2021 2nd International Conference on Mechatronics Technology and Intelligent Manufacturing, ICMTIM 2021

作者： Qiao, Weizhi Wang, Zhenyu Gao, Xiang Liu, Xiaochao Qiu, Zhongyi Hou, Yanze Chen, Chong Ningbo Institute of Technology Beihang University Zhejiang Ningbo315800 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Zhejiang Hangzhou310027 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Research Institute for Frontier Science Beihang University Beijing100191 China Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing100094 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China

Hydraulic motors and pumps are the core components in hydraulic systems. The plunger assembly is a key component for the realization of the function of the axial piston pump and motor. The coupling between the slipper and the plunger body is completed through the closing process. With the application of computational science and finite element technology, DEFORM-3D metal forming process simulation is used to establish the corresponding closing device model of the plunger assembly. Through simulation, we analyze the change law of metal flow during plastic deformation process, and carry out structural optimization design based on the simulation results. We found that the closing is completed under the appropriate cone angle and distance line of the shoe, the quality is relatively uniform, and there is no closing defects such as metal flow overflow and cracks. The pull-off force and the clearance value after rolling can meet the design requirements. This method not only can effectively shorten the product verification cycle and reduce the cost, but also can reduce the defects of the finished product and improve the yield rate, providing a new way for the design of the closing structure and closing model of the plunger assembly. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Structural optimization

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The design of special energy conversion device of integrated self-powered brake system 2

The design of special energy conversion device of integrated...

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2021 2nd International Conference on Mechatronics Technology and Intelligent Manufacturing, ICMTIM 2021

作者： Qiao, Weizhi Li, Ding-Bo Gao, Xiang Liu, Xiaochao Zhang, Hao Li, Renjie Wang, Xiyu Hou, Yanze Chen, Chong Ningbo Institute of Technology Beihang University Ningbo315800 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Research Institute for Frontier Science Beihang University Beijing100191 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing100094 China

In recent years, a novel electro-hydraulic integrated self-powered brake system for aircraft has been proposed. A modular "self-powered device" is installed near the wheel to recover the rotational kinetic energy when the wheel lands and convert it into hydraulic energy for braking. The energy conversion device is the compact high-power-weight ratio energy harvesting mechanism, which needs to be designed in the extremely narrow internal space of the wheel. At the same time to ensure high reliability, high strength, high power weight ratio, difficult, and wheel brake disc internal temperature reaches 600 ℃ above, this to the design of the can take institutions are challenges. In this paper, the high strength compact high power-weight ratio energy conversion device as the research goal, through new principles, new structure, new materials, new technology, new methods and other means of the energy taking mechanism design and development, and finally achieve the strength, stiffness, reliability, life of the energy conversion device to meet the requirements of the system. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Wheels

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Application Research of Ensemble Learning Frameworks

Application Research of Ensemble Learning Frameworks

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Chinese automation Congress (CAC)

作者： Kunkun Wang Xianda Liu Jianming Zhao Hongwei Gao Zhen Zhang Key Laboratory of Networked Control Systems Shenyang Ligong University College of Automation and Electrical Engineering Shenyang China Key Laboratory of Networked Control Systems Shenyang Insitute of Automation Chinese Academy of Sciences College of Automation and Electrical Engineering Shenyang Ligong University Shenyang China Information & Telecommunication Branch State Grid Jiangsu Electric Power Co. Ltd. Nanjing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In the field of artificial intelligence, machine learning is a hot research direction. With the improvement of computing power, machine learning has been better developed in the era of big data. A single machine learning algorithm has some shortcomings. Multiple simple base learning methods are combined to form an ensemble learning through a certain strategy, and meanwhile the performance of the ensemble learning will be greatly improved. The ensemble learning framework includes three integration modes: boosting, bagging, and stacking. This article introduces the development process of ensemble learning and studies the principles of the three ensemble methods, summarizes the existing ensemble learning algorithms, summarizes the characteristics and applications of the ensemble algorithms, and finally discusses the improvement and future development of the algorithms.

关键词： Training Classification algorithms Bagging Boosting Machine learning algorithms Stacking Prediction algorithms

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Distributed Formation of Autonomous Underwater Vehicles with Unreliable Switching Topologies and Transmission Delays

Distributed Formation of Autonomous Underwater Vehicles with...

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第三十九届中国控制会议

作者： Chao Ma Wei Wu Yidao Ji Hang Fu School of Mechanical Engineering University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

This paper investigates the formation problem of autonomous underwater vehicles with unreliable switching communication topologies and time-varying transmission delays. More precisely, the switching topologies contains achievable and unachievable sub-topologies, which can better describe the practical underwater communication environment. By performing model transformation and constructing appropriate multiple Lyapunov-function method, sufficient conditions are established based on admissible edge-dependent average dwell time, such that the desired formation configuration can be achieved with transmission delays. Finally, an illustrative example is given at last to verify the effectiveness of the main results.

关键词： Distributed formation Autonomous underwater vehicles Switching topologies Admissible edge-dependent switching

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Recursive Weighted Null-Space Fitting Method for Identification of Multivariate systems ⁎

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IFAC-PapersOnLine 2021年第7期54卷 345-350页

作者： Mengyuan Fang Miguel Galrinho Håkan Hjalmarsson Zhejiang Engineering Laboratory of Industrial Internet Communication Technology Zhejiang Sci-Tech University Hangzhou China. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China OHB Sweden AB Kista Sweden Centre for Advanced Bio Production and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Recursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.

关键词： Recursive system identification multivariate systems weighted null-space fitting

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Target identity recognition method based on trusted information fusion

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International Journal of Performability engineering 2019年第4期15卷 1235-1246页

作者： Wang, Lu Wen, Chenglin Wu, Lan Department of Electrical Automation Shanghai Maritime University Shanghai201306 China Department of Image and Network Investigation Railway Police College Zhengzhou450053 China Institute of Systems Science and Control Engineering Hangzhou Dianzi University Hangzhou310018 China College of Electrical Engineering Henan University of Technology Zhengzhou450001 China

Safe and reliable target identity recognition is the important foundation of information security. In the complex environment of multi-source target information, in view of the potential impact of many uncertain factors on target identity recognition and the performance requirements of information security in the process of recognition, a trusted target identity recognition method is proposed in this paper. The BP neural network based on momentum factor is used to study and build an ensemble classification model, and based on this model, the trusted target identity recognition model is constructed. According to the relevant information characterized by the model, it can improve the recognition reliability of the target to a certain extent, thus providing more security and credibility for the recognition of the identified target. Finally, the effectiveness and feasibility of the proposed algorithm is verified by simulation experiments under an uncertain set environment. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Information fusion

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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Computer systems Science & engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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