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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Shiyuan Pan Zhiguang Hua Qi Yang Dongdong Zhao Wentao Jiang Yuanlin Wang Junpeng Ji Manfeng Dou School of Automation Northwestern Polytechnical University Xi'an China Jianghuai Advance Technology Center Hefei China School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an China School of Electrical Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

In the data-driven prediction methods, the echo state network (ESN) model could realize the prediction of proton exchange membrane fuel cells of degradation. Aiming at the problem of low prediction accuracy, a decoupled ESN (DESN) with the lateral inhibition based on reservoir predictive (DESN-RP) mechanism is proposed in this paper. By improving the structure of ESN and inhibiting the influence of other neurons and sub-reservoirs on the activated neurons, the preliminary decoupling of DESN is realized. The reservoir predictive (RP) mechanism accelerates the network learning of useful information and improves the prediction by strengthening the competition of activated neurons and inhibiting other neurons. It could further weaken the coupling of neurons and reduce the redundant information of the internal state. In general, DESN-RP could enhance feature representation, increase sparsity, reduce the fitting risk, and reinforce the generalization ability of the network. It was proved that DESN-RP improved the accuracy of long-term prediction of the degradation of PEMFC under steady-state, quasi-dynamic, and dynamic conditions.

关键词： Degradation Couplings Training Accuracy Neurons Fuel cells Voltage

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An overview on the designs of distributed observers in LTI multi-agent systems

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science China(Technological sciences) 2021年第11期64卷 2337-2346页

作者： PEI YingQing GU HaiBo LIU KeXin LU JinHu Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China

With the rapid development of information technologies and cloud computing, sensor networks play an increasingly important role in our society. Over the past few decades, distributed observer theory has attracted unprecedented attention due to its wide potential applications in different areas. Meanwhile, various approaches and algorithms have been proposed and investigated. The design of distributed observers is one of the frontier topics of system and control research, which has the significant theoretical values and broad application prospects. This paper attempts to review the representative models and the corresponding approaches for distributed observer design in linear time-invariant(LTI) systems. Firstly, the research backgrounds and main advances of distributed observer designs are briefly reviewed. Then, recent results of distributed observer designs for discrete-time and continuous-time LTI multi-agent systems(MASs) are introduced in detail, respectively. Finally, the prospects and the future work directions of the design of distributed observers are put forward. The main purpose of this paper is to promote the emerging topic on the designs of distributed observers, with focuses on the interdisciplinary interest from technological sciences.

关键词： multi-agent system linear time-invarient system distributed observer consensus

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Online Identification of Parameters for PMSM Based on a Modified Artificial Bee Colony Algorithm

Online Identification of Parameters for PMSM Based on a Modi...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Xing Ren Yuanchao Cao Qing Guo Guicheng Wu Qiang Long Mengfan Ran School of Automation Engineering University of Electronic Science and Technology of China Chengdu China School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China Sichuan Aerospace Fenghuo Servo Control Technology Corporation Chengdu China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

The variable step size least mean square (LMS) technology is integrated into the artificial bee colony (ABC) algorithm, which significantly improves the exploitation of ABC and accelerates the convergence rate. In addition, a new probability model of onlooker bees is designed to prevent the solutions from easily falling into the local optimum. The modified ABC is called LMSABC, and an online parameter identification method based on LMSABC for permanent magnet synchronous motor (PMSM) is proposed. The diversity of LMSABC first decreases and then increases, thus ensuring that the algorithm converges quickly in the early stage and maintains strong exploration in the later stage. The simulation comparison results with other similar algorithms indicate that the proposed method has the highest parameter identification accuracy and the fastest convergence rate.

关键词： Parameter estimation Accuracy Mechatronics Artificial bee colony algorithm Permanent magnet motors Robots Optimization Convergence

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Multi-Feature Likelihood Ratio Detection Algorithm Based on Random Point Patterns

Multi-Feature Likelihood Ratio Detection Algorithm Based on ...

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Chinese automation Congress (CAC)

作者： Weifeng Liu Yuhang Ren Zhi Wang Zhangming Peng School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an China School of Automation Hangzhou Dianzi University Hangzhou China School of Mechanical Engineering Hangzhou Dianzi University Hangzhou China

This paper focuses on the random point pattern detection problems. Based on the characteristics of random point patterns, a multi-feature likelihood ratio random point pattern detection algorithm is proposed. This algorithm first utilizes Monte Carlo experiments to estimate the likelihood ratio and detection threshold of the distribution parameters of random point patterns. Then, it discriminates the input data to achieve the detection of random point patterns.

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Feature Distribution Distillation-Based Few Shot Class Incremental Learning 5

Feature Distribution Distillation-Based Few Shot Class Incre...

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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Zhu, Juntao Yao, Guangle Zhou, Wenlong Zhang, Guiyu Ping, Wang Zhang, Wei Chengdu610059 China Artificial Intelligence Key Laboratory of Sichuan Province Yibin643000 China Sichuan University of Science & Engineering School of Automation & Information Engineering Yibin64300 China Chengdu Technological University Communication Engineering School Chengdu611730 China University of Electronic Science and Technology of China School of Information and Communication Engineering Sichuan Chengdu611731 China Science and Technology on Electronic Information Control Laboratory Sichuan Chengdu610036 China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

This paper focuses on the challenging but practical problem: convolutional neural networks (CNN) based few-shot class-incremental learning (FSCIL), which requires learning new knowledge incrementally from a few examples without forgetting the previously learned knowledge. To balance the memory of the old knowledge and the adaptation to the new knowledge, we propose a feature distribution distillation-based incremental learning framework. First, we freeze the shallow parameters of the network to alleviate the massive forgetting of the learned knowledge. Then, we propose a feature distribution-based knowledge distillation loss to transfer the previously knowledge. Finally, we also generate the classification weights initialization for each new class to improve the adaptation of the network. Extensive experiments show that our proposed method can effectively alleviate the forgetting of the previously knowledge and improve the adaptability to the new knowledge. Our method outperforms other state-of-the-art few-shot class incremental learning methods on MiniImageNet dataset and gets a competitive result on CIFAR-100 dataset. © 2022 IEEE.

关键词： Distillation

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考虑装配误差影响的齿轮副啮合特性优化（英文）

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Journal of Central South University 2025年第4期32卷 1400-1430页

作者：赵晓剑马辉马泽宇刘家奇曹鹏吴玉萍丁相符赵天宇 School of Mechanical Engineering and Automation Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion Systems of Ministry of Education Northeastern University AECC Hunan Aviation Powerplant Research Institute School of Science Northeastern University

齿轮装配误差会导致系统振动和噪声增大，影响系统的稳定性。这种影响可以通过齿轮微观修形得到补偿。首先，提出了一种考虑微观修形和耦合装配误差的改进三维轮齿加载接触分析(3D-LTCA)方法，并利用MASTA软件以及三个案例验证了该方法... 详细信息

齿轮装配误差会导致系统振动和噪声增大，影响系统的稳定性。这种影响可以通过齿轮微观修形得到补偿。首先，提出了一种考虑微观修形和耦合装配误差的改进三维轮齿加载接触分析(3D-LTCA)方法，并利用MASTA软件以及三个案例验证了该方法的准确性；其次，基于BP神经网络建立修形参数与啮合力学参数之间映射的代理模型(SM)，验证其有效性性，并将建立的代理模型与常用的Kriging、支持向量回归(SVR)、多项式回归响应面法(RSM)等代理模型进行了对比，从而进一步说明了本文代理模型的优越性；最后，引入SM建立以啮合刚度波动最小和啮合表面载荷分布更均匀为目标的优化模型。结果表明，即使考虑网络训练时间，通过代理模型优化齿轮副的效率仍远高于LTCA方法。优化后，带耦合装配误差的齿轮副的啮合刚度波动减小了34.10%，啮合面左右区域的平均接触应力差减小了62.84%。

关键词：斜齿轮啮合特性轮齿修形装配误差神经网络多目标优化

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Predefined Output Containment for Heterogeneous Euler-Lagrange Systems with Multiple Uncertain Nonidentical Leaders

Predefined Output Containment for Heterogeneous Euler-Lagran...

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International Conference on control and automation (ICCA)

作者： Qing Wang Xiwang Dong Zhi Feng Jiayi Wang Zhexin Shi School of Automation Science and Electri-cal Engineering Science and Technology on Aircraft Control Labora-tory Beihang University Beijing P.R. China School of Economics and Management Tsinghua University Beijing P.R. China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper discusses the robust predefined output containment (RPOC) control problem for heterogeneous Euler-Lagrange systems having multiple uncertain nonidentical lead-ers. In order to solve this problem, a new kind of distributed observer-based robust predefined output containment control framework is presented. Firstly, for obtaining the information of nonidentical leaders' dynamics including uncertain parameters in leaders' system matrices and states, two kinds of adaptive observes are constructed in a fully distributed form without any knowledge of the system matrices of nonidentical leaders, exactly. Secondly, on the basis of adaptive learning technique, a new RPOC controller is then developed by using the presented observers. Furthermore, with help of Lyapunov stability theory, the RPOC criteria for the considered system under multiple uncertain nonidentical leaders are derived from the constructed controller. At last, a simulation example is provided to demonstrate the effectiveness of the proposed RPOC controller.

关键词： Adaptive learning Adaptive systems Protocols automation Stability criteria Observers Lead

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Multi-Phase Degradation Modeling Based on Uncertain Random Process for Remaining Useful Life Prediction Under Triple Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1129-1143页

作者： Xuerui Cao Kaixiang Peng Ruihua Jiao Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Metallurgical Industry Safety and Risk Prevention and Control Ministry of Emergency University of Science and Technology Beijing Beijing China AVIC Xi'an Aviation Brake Technology Co. Ltd. Xi'an China

Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure, degradations of some equipment are characterized by multi-phase and jumps. Meanwhile, equipment is subject to inherent fluctuations, limited data and imperfect measurements resulting in aleatory, epistemic and measurement uncertainties of the degradation process. This paper proposes a degradation model and remaining useful life (RUL) prediction method under triple uncertainties for a category of complex equipment with multi-phase degradation and jumps. First, a multi-phase degradation model with random jumps and measurement errors is constructed based on uncertain random processes. Afterward, the analytic expression of RUL prediction considering the heterogeneity is derived by modeling the uncertainty of degradation states at change points under the concept of first hitting time. A stochastic uncertain approach is utilized for the proposed multi-phase degradation model to identify model parameters based on historical data. Furthermore, the implied degradation features are adaptively updated in online stage using similarity-based weighted stochastic uncertain maximum likelihood estimation and Kalman filtering. Finally, the effectiveness of the method is verified by simulation example and practical case.

关键词： Degradation Adaptation models Measurement errors Uncertainty Filtering Measurement uncertainty Stochastic processes Predictive models Random processes Kalman filters

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Position Sensorless control of PMSM Based on EKF Current Noise Compensation and MRAS

Position Sensorless Control of PMSM Based on EKF Current Noi...

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IEEE International Conference on Mechatronics and automation

作者： Yuanchao Cao Xing Ren Qing Guo Guicheng Wu Qiang Long Mengfan Ran School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Aerospace Fenghuo Servo Control Technology Corporation Chengdu China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

A position sensorless PMSM control method based on model reference adaptation system (MRAS) and current noise compensation is proposed in this paper. Firstly, a rotor speed and position estimation method based on MRAS and proportional-integrated (PI) adaptive control is designed. Then, in order to solve the problem that PMSM control performance is easily affected by current loop noise, a compensation approach based on extended Kalman filter (EKF) is developed. The sensorless control method based on MRAS and EKF current noise compensation proposed in this paper can accurately estimate the motor speed and position, and significantly eliminate the negative effect of current loop noise. The effectiveness of the proposed method is verified through simulations under different speeds and external loads.

关键词： Adaptation models Noise Velocity control Rotors Estimation Sensorless control Synchronous motors

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A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System 8

A Power Split Strategy for a Vehicular Hybrid Energy Storage...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Jiao Zhang, Lijun Yang, Longzhi Wang, Bo School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China Northumbria University Faculty of Engineering and Environment Department of Computer Science and Digital Technologies Newcastle upon TyneNE1 8ST United Kingdom

ISBN: (纸本)9798350300178

This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time. © 2023 IEEE.

关键词： Supercapacitor

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