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Chinese automation Congress (CAC)

作者： Gang Xiang Wenjing Chen Yu Peng Yuanjin Wang Chen Qu School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The on-orbit operation and all-weather duty of spacecraft lead to the rapid increase of test data, which brings severe challenges to fault diagnosis. In recent years, deep learning has shown excellent performance in feature extraction and pattern recognition. More and more attentions have been paid to the application of deep learning in spacecraft fault diagnosis. However, the success of deep learning largely relies on sufficient labeled data. Due to the high reliability of spacecraft, the test data usually contains lots of normal sample points, while the faulty sample points are extremely scarce. Firstly, this paper designs a fault diagnosis model based on 1-D convolutional neural network, which directly processes the 1-D raw data and extracts features; then, for the first time, the transfer learning technology is introduced into the field of spacecraft fault diagnosis, and a domain adaptive deep transfer model is proposed. MMD is used to reduce the discrepancy of data probability distribution between the source and target domain. The results of the experiments show that the proposed model could accurately diagnose and identify the faults of spacecraft in different application scenarios.

关键词： Fault diagnosis Space vehicles Feature extraction Convolution Deep learning Data models Neural networks

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Complex Modified Projective Synchronization for Fractional-order Chaotic Complex Systems

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International Journal of automation and computing 2018年第5期15卷 603-615页

作者： Cui-Mei Jiang Shu-Tang Liu Fang-Fang Zhang College of Control Science and Engineering Shandong University School of Electrical Engineering and Automation Qilu University of Technology

The aim of this paper is to study complex modified projective synchronization（CMPS） between fractional-order chaotic nonlinear systems with incommensurate orders. Based on the stability theory of incommensurate fractional-order systems and active control method, control laws are derived to achieve CMPS in three situations including fractional-order complex Lorenz system driving fractional-order complex Chen system, fractional-order real Rssler system driving fractional-order complex Chen system, and fractionalorder complex Lorenz system driving fractional-order real Lü system. Numerical simulations confirm the validity and feasibility of the analytical method.

关键词： Fractional-order system chaotic complex system incommensurate order complex modified projective synchronization（CMPS） active control.

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Time Delay Estimation with Fixed-time SMC Scheme for Nonlinear Dynamics

Time Delay Estimation with Fixed-time SMC Scheme for Nonline...

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International Conference on control, automation and Diagnosis (ICCAD)

作者： Saim Ahmed Ahmad Taher Azar Arezki Fekik Haoping Wang Chakib Ben Njima College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Department of Electrical Engineering University Akli Mohand Oulhadj-Bouria Rue Drissi Yahia Bouira Algeria CNRS LS2N UMR 6004 Nantes Université École Centrale Nantes Nantes France School of Automation Nanjing University of Science and Technology China University of Sousse Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Time delay estimation using fixed-time terminal sliding mode control (TDEFxSMC) for unknown nonlinear systems with uncertainty and external disturbances is presented in this study. Firstly, the notion of fixed-time terminal sliding mode control (FxSMC) is outlined and thoroughly examined. Using the FxSMC scheme, nonsingular fixed-time control, non-chatter control inputs, and high performance tracking positions have been achieved. To estimate the unknown dynamics, proposed method incorporates a TDE scheme. The Lyapunov theorem is applied to find whether the proposed system can achieve its stability in a fixed-time. The effectiveness of the suggested method is evaluated and demonstrated by presenting the results of the proposed method implemented to the nonlinear robot dynamics.

关键词： Uncertainty automation Delay effects Estimation 2-DOF Stability analysis Nonlinear dynamical systems

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Epileptic Seizure Prediction from the Scalp EEG Signals by using Random Forest Algorithm

Epileptic Seizure Prediction from the Scalp EEG Signals by u...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Ziyu Hu Chunxiao Han Fengjuan Guo Qing Qin Shanshan Li Yingmei Qin Tianjin Key Laboratory of Information Sensing & Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education China

ISBN: (数字)9780738105451

ISBN: (纸本)9781665422994

Epilepsy is a common chronic neurological disease of the brain and becomes the second critical neurological disease following the cerebrovascular disease. It has great significance of studying epileptic electroencephalogram (EEG) signals to reduce the frequency of seizure or even prevent epilepsy, thereby decrease the incidence of disability and death induced by epilepsy. Therefore, we proposed an epileptic EEG signals prediction algorithm by using the random forest (RF). The scalp EEG signals used here is weaker than the intracranial EEG signals but easier to collect. The prediction process had four steps: epileptic scalp EEG signals preprocessing by the discrete wavelet transform (DWT), features extraction by several linear measures in the time domain, comparison of the general model and the patient-specific model, and prediction optimization by RF with different features and sub-bands combination. It is found that patient-specific models had higher accuracy, and the coefficient of variation (CV) had a great influence on the seizure prediction. The influence of different sub-bands on the seizure prediction was also investigated. Finally, CV with all sub-bands were optimally selected, and the average accuracy of the seizure prediction was up to 99.8102%. The effectiveness of the proposed prediction algorithm was verified by numerical calculation.

关键词： Electroencephalography Scalp Epilepsy Radio frequency Feature extraction Brain modeling Predictive models

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Optimizing the teaching efficiency of english classrooms in colleges and universities based on deep learning

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AIP Conference Proceedings 2024年第1期2937卷

作者： Y. Dileep Kumar M. Praveen Kumar A. Mahesh Reddy G. Vamsi Krishna G. V. Sunil Kumar Department of Electronics & Instrumentation Engineering SreeVidyanikethan Engineering College A. Rangampeta Tirupati-517102 India. Department of Control & Automation School of Electrical Engineering VIT University Katpadi Vellore 632014 Tamilnadu India. Department of Electronics &Communication Engineering Sri Venkatesa Perumal College of Engineering & Technology Puttur-517583 India. Department of Electronics & Instrumentation Engineering Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Chikkadapally Hyderabad500020 India. Project Manager Involute Automation (P) Ltd Balanagar Hyderabad-500037 Telangana India

An accurate assessment of the quality of English classroom instruction is essential for moving English education reform forward while also raising English education standards in Chinese colleges. A comparison model for evaluating the quality of English classroom teaching is built using SVM-LM (Support vector machine-Learning Machine) model together with Particle Swarm Optimization (PSO). The SVM-LM approach reduces the need for experiment samples while enhancing model fit because fewer hidden layer neurons are required for accuracy. According to results from experiments, utilizing the SVM- LM algorithm to assess English classroom instruction is a realistic option. Consequently, the study created English classroom teaching effectiveness index system has been successful and is intended to increase the standard of English language classroom instruction as well as how it is administered. The study results proved that the proposed algorithm works better than PSO.

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Modeling and Fault Diagnosis of Multi-phase Winding Inter-turn Short Circuit for Five-phase PMSM based on Improved Particle Swarm Optimization

Modeling and Fault Diagnosis of Multi-phase Winding Inter-tu...

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International Conference on Power Electronics and Motion control (IPEMC)

作者： Jianwei Yang Youming Wang Weiyi Zhang Zhiyong Dai Zhen Zhang Kang Xiangli School of Automation Xi’an University of Posts and Telecommunications Xi’an China School of Mechanical and Electrical Engineering Xidian University Xi’an China School of Electronic and Control Engineering Chang’an University Xi’an China

ISBN: (数字)9781728153018

ISBN: (纸本)9781728153025

Multi-phase PMSMs are widely used in fault-tolerant control applications. As an electrical fault with a high probability, inter-turn short circuit (ITSC) fault seriously affects the performance of five-phase PMSM. However, due to high coupling of fault parameters, most of the fault models and diagnosis methods in fault-tolerant control focus on one-phase winding ITSC fault of three-phase PMSM. In this paper, a novel model and a fault diagnosis method are proposed for multi-phase winding ITSC (MPWITSC) of five-phase PMSM. First, by analyzing the related physical parameters of the motor, a mathematic model of MPWITSC fault is established. Then, a parameter estimation method based on improved particle swarm optimization (IPSO), which solves the convergence premature of traditional PSO (TPSO) and reformulates the fault diagnosis as the extreme seeking for fault parameters, is proposed to detect the fault level. Simulation results provide preliminary verification of the proposed model and fault diagnosis method.

关键词： Fault diagnosis Simulation Windings Circuit faults Mathematical model Integrated circuit modeling Particle swarm optimization

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Cloud-Fog automation: The New Paradigm towards Autonomous Industrial Cyber-Physical Systems

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IEEE Journal on Selected Areas in Communications 2025年

作者： Jin, Jiong Pang, Zhibo Kua, Jonathan Zhu, Quanyan Johansson, Karl H. Marchenko, Nikolaj Cavalcanti, Dave Swinburne University of Technology School of Engineering HawthornVIC3122 Australia KTH Royal Institute of Technology Department of Intelligent Systems Stockholm10044 Sweden ABB Corporate Research Sweden Department of Automation Technology Västerås72226 Sweden Deakin University School of Information Technology GeelongVIC3220 Australia New York University Tandon School of Engineering Department of Electrical and Computer Engineering BrooklynNY11201 United States KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures Stockholm10044 Sweden Robert Bosch GmbH Corporate Research Renningen71272 Germany Intel Corporation Santa ClaraCA95054 United States

Autonomous Industrial Cyber-Physical Systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term "3C co-design") is required. In this paper, we position Cloud-Fog automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security. © 1983-2012 IEEE.

关键词： computer control

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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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Manufacturing Service Network of Digital Twin Systems Under Cloud Computing Environment

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Procedia CIRP 2022年 109卷 460-465页

作者： Feng Xiang Ping Zhou Ying Zuo Fei Tao Dashun Zhang Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan 430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan 430081 China Precision Manufacturing Institute Wuhan University of Science and Technology Wuhan 430081 China Research Institute for Frontier Science Beihang University Beijing 100191 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China No.55 Research Institute China North Industries Group Corporation Changchun 130012 China

With the promotion and application of the digital twin system in the industrial Internet platform, and around the "intelligent, integrated, and service-oriented" manufacturing characteristics, a digital twin system manufacturing service network with full-process, full-object, and full-factor coverage of virtual and real resources has gradually formed. In a typical cloud computing environment, this paper defines the manufacturing service network of the digital twin system based on the virtual and real mapping, simulation calculation, feedback control and other characteristic requirements of the digital twin system, and constructs the network model from the network nodes and network connections. Based on this, the optimization steps for resource allocation of manufacturing service network based on the digital twin system, and the key technologies for the construction of the manufacturing service network of the digital twin system are analyzed.

关键词： Digital Twin Cloud Environment Service Network Resource Allocation

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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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