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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Wu, Penghao Tao, Hongfeng Tian, Engang Yin, Tangwen Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350361674

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods. © 2024 IEEE.

关键词： Variational mode decomposition

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Leaching process control and simulation based on Model-free adaptive control algorithm

Leaching process control and simulation based on Model-free ...

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第34届中国过程控制会议

作者： Han Zhang Yiru Zhao Dakuo He School of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

The most important index in the leaching process is the leaching rate,which determines the economic benefit of hydrometallurgy to a great *** the leaching rate as the index,this paper first simulates the mechanism model to verify its feasibility and provides the simulated actual production data for the subsequent model-free ***,the data-driven Compact Format Model-free adaptive control(CF-MFAC) and improved Compact Format Model-free adaptive control(iCFMFAC) schemes are ***,the optimal control simulation of the leaching rate was carried out,and the performance of CF-MFAC and iCF-MFAC methods were compared and *** simulation results show that CF-MFAC and iCF-MFAC can achieve optimal control of the leaching rate,and the control performance of the iCF-MFAC method is better.

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Robust PD-type iterative learning control design for uncertain batch processes subject to nonrepetitive disturbances 41

Robust PD-type iterative learning control design for uncerta...

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第41届中国控制会议

作者： Robert Maniarski Wojciech Paszke Shoulin Hao Hongfeng Tao Institute of Automation Electronic and Electrical EngineeringUniversity of Zielona Góra Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper develops PD-type iterative learning control schemes for a class of uncertain batch processes subject to nonrepetitive *** means of two-dimensional/repetitive setting,the sufficient conditions for batch-to-batch error convergence and H disturbance attenuation are formulated and ***,the procedure for computing the desired control law matrices is formulated in terms of solvability of linear matrix *** proposed control law is able to fulfil the imposed design specifications,i.e.,they are suitable for the batch processes with time-varying uncertainties as well as non-repetitive *** illustrative example is used to validate the proposed control scheme and demonstrates a possible applicability of the developed results.

关键词： iterative learning control repetitive processes batch processes robust control nonrepetitive disturbances

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Data augmentation for fault prediction of aircraft engine with generative adversarial networks

Data augmentation for fault prediction of aircraft engine wi...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical processes, SAFEprocess 2021

作者： Lang, Pengxue Peng, Kaixiang Cui, Jiapeng Yang, Jie Guo, Yingxin University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Process of Ministry of Education School of Automation and Electrical Engineering Beijing China

ISBN: (纸本)9781665401159

Fault prediction is to predict the remaining useful life(RUL) of the equipment by constructing models using historical data. However, the run-to-failure data is difficult to obtain, and it is impossible to build an accurate prediction model. To address this problem, an innovative forecasting method based on data augmentation technology is proposed in this paper. First, a generative adversarial network (GAN) is used to study the distribution of the original dataset, and generate a new train set. Then, the original dataset is fused with the new one to train a convolutional neural network and long short-term memory network (CNN-LSTM) prediction model. Finally, the experiment is conducted on the original C-MAPSS and fusion dataset, which results show that the proposed feature extraction method can effectively predict the RUL compared with the existed methods. © 2021 IEEE.

关键词： Generative adversarial networks

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RIS-Aided Localization Method Based on DNN-SSA Fusion in NLOS Scenarios

RIS-Aided Localization Method Based on DNN-SSA Fusion in NLO...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Yujia Luo Qingsong Hu Yuanxun Cheng Shuya Yuan Yuansheng Zhang School of Information and Control Engineering China University of Mining and Technology Xuzhou China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

With the advent of the 6G era, precise positioning and reliable, energy-efficient data transmission have become critical for the Internet of Things. However, high-frequency signals like millimeter-wave and terahertz are easily obstructed, making traditional LOS-based methods ineffective in complex environments. To overcome this, NLOS localization methods and Reconfigurable Intelligent Surfaces offer promising solutions by dynamically modifying the wireless environment. This paper introduces a Direct Position Determination method that integrates Deep Neural Networks (DNN) with the Sparrow Search Algorithm (SSA) to tackle the challenges of high computational complexity and low localization accuracy in RIS-aided systems. An NLOS scenario with RIS is established, and a far-field channel model is derived. The method uses DNN to model the nonlinear relationship between the likelihood function and user position, and then employs SSA to optimize the DNN-based fitness function, efficiently solving the objective function. Experimental results show that the DNN-SSA method consistently achieves high localization accuracy across different scenarios, even at low SNR levels, demonstrating its effectiveness and potential for 6G high-precision localization.

关键词： Location awareness 6G mobile communication Wireless communication Accuracy Simulation Search methods Artificial neural networks Reconfigurable intelligent surfaces Nonlinear optics Signal to noise ratio

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Highly sensitive salinity sensor based on Mach-Zehnder interferometer with double-C fiber

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Fundamental Research 2022年第2期2卷 296-302页

作者： Ya-nan Zhang Like Li Jincheng Zhao Yong Zhao College of Information Science and Engineering Northeastern UniversityShenyang 110819China State Key Laboratory of Synthetical Automation for Process Industries Shenyang 110819China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao 066004China

This paper proposes a highly sensitive,compact,and low-cost optical fiber salinity sensor based on the Mach-Zehnder *** sensor is constructed using a single mode fiber(SMF)-no-core fiber-double-C fiber(DCF)-NCF-SMF structure,with the DCF prepared by etching the dual side-hole fiber with HF *** DCF’s large-size exposed microfluidic channels solve the previous microstructured optical fiber’s challenging liquid filling and replacement *** simulations and experiments demonstrate that the sensor is suitable for high-sensitivity salinity *** sensor exhibits a high salinity sensitivity of-2.26 nm/‰ in the salinity range of 10‰-50‰,as demonstrated by the experimental ***,the sensor exhibits some fascinating characteristics,including high repeatability,hysteresis,reversibility,and stability.

关键词： Optical fiber sensor Salinity sensor Mach-Zehnder interferometer Microfluidic channel Double-C fiber

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The Robot Fusion Mapping Method Applied in Indoor Poultry Farming Environment 2

The Robot Fusion Mapping Method Applied in Indoor Poultry Fa...

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2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Zhang, Jianming Lei, Jiasheng Zhao, Yuting Yang, Kaixiong Liu, Luoran Peng, Xin He, Kaixun College of Information Science and Engineering East China University of Science and Technology Shanghai China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350360363

Temperature and humidity fluctuations in indoor poultry settings significantly impact poultry survival, influencing farm efficiency. Employing robots for inspections equipped with specialized tools can enhance inspection efficiency by replacing manual labor. Yet, indoor environments pose challenges like odors, cleanliness issues, and inspection complexities. To address these, we propose a novel method-Visual-Assisted G2 Laser Robot Mapping (VAGL)-tailored for indoor poultry farming. VAGL integrates laser and visual mapping to cover structured and unstructured areas effectively. Laser mapping handles structured zones, overcoming limitations of single-line LiDAR by converting depth images into laser data for detecting obstacles at varying levels. For unstructured areas, like corners with railings, we utilize the visual ORB- SLAM mapping method. Employing Bayesian fusion enhances mapping accuracy and completeness by integrating laser and visual mapping data. Simulation tests using MATLAB's image correction tools reveal significantly improved mapping accuracy and efficiency compared to using only laser or visual mapping methods. This study presents a promising technical solution for large-scale indoor poultry farming, potentially boosting environmental monitoring and management precision and efficiency in farming practices. © 2023 IEEE.

关键词： Mapping

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Modeling and Implicit Inversion of Butterfly Hysteresis in Dielectric Elastomer Actuated System

Modeling and Implicit Inversion of Butterfly Hysteresis in D...

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

作者： Hongzhi Xu Zihan Xu Xiuyu Zhang Zhi Li State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Automation Engineering Northeast Electric Power University Jilin China

Butterfly hysteresis nonlinearity is a unique type of hysteresis characteristic that is distinct from the traditional single-loop hysteresis characteristic, demonstrating non-monotonic and multi-peak features between input and output. In this paper, we analyze the causes of butterfly hysteresis non-linearity in dielectric elastomer actuated systems and propose a butterfly hysteresis model based on the classical relay operator. To compensate for the butterfly non-monotonic hysteresis nonlinearity in the dielectric elastomer actuated systems, an novel implicit inverse compensation algorithm is proposed. Experimental results demonstrate the effectiveness of the established model and the inverse compensation algorithm.

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process Operational State Assessment Based on Stacked Enhanced Denoising Autoencoder

Process Operational State Assessment Based on Stacked Enhanc...

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Chinese Control and Decision Conference, CCDC

作者： Binsheng Feng Yan Liu Fuli Wang School of Automation Southeast University Nanjing China School of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Integrated Automation of Process Industry Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper presents a process operational state assessment method based on a deep neural network— specifically, the Stacked Enhanced Denoising Autoencoder (SEDAE)—tailored for complex industrial production data characterized by strong coupling, nonlinearity, and significant noise interference. The approach initially employs SEDAE to extract deep features closely associated with the operational states from process data. Subsequently, the extracted features are processed using a SoftMax classifier to accomplish the operational state assessment. Finally, the proposed method is validated through simulation using actual production data from the cyanide leaching process. The results demonstrate the superiority of the SEDAE model compared to others, maintaining a leading position under varying noise ratios. Even at a noise ratio of 60%, the SEDAE model sustains an accuracy level of around 90%.

关键词： Couplings Training Noise Noise reduction Production Interference Network architecture

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Fault-Tolerant Prescribed Performance Control of Unknown MIMO Nonlinear Systems

Fault-Tolerant Prescribed Performance Control of Unknown MIM...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Wei Ding Jin-Xi Zhang Faculty of Electrical Engineering and Automation Changshu Institute of Technology Changshu China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

This paper investigates the fault-tolerant control problem for the multi-input multi-output nonlinear systems with unknown dynamics and process faults. The process faults together with the unknown dynamics lead to dynamic uncertainties in the plant, such that the commonly used approximation techniques fail to be applied straightforward. To overcome this challenge, a novel fault-tolerant prescribed performance control scheme is developed in this paper. Except for the known control direction of the plant, no prior knowledge of the system model and the process faults is involved in the control law. Even so, there is no need to invoke the techniques for function approximation, parameter identification, disturbance estimation, fault detection, fault estimation, fault isolation, etc. The proposed controller guarantees reference tracking with the predefined transient and steady-state performance. The simulation results manifest the effectiveness and the advantage of the controller.

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