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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Tiago Davi Curi Busarello Department of Control Automation and Computing Engineering Federal University of Santa Catarina Blumenau Brazil

In sensorless motor drive applications, the rotor and the speed of the rotor are usually estimated through state observer algorithms. A key part of such algorithm is the computation of the derivative of the rotor position, resulting in the estimated rotor speed. Computing derivatives brings challenges due to their inherent noise amplification characteristic. This paper presents enhanced discrete-time differentiators for state observers of sensorless permanent magnetic synchronous motor drives. A class of discrete-time differentiators based on finite impulse response filters and one based on Kalman filter are proposed. Details about how to obtain the coefficients of the differentiators are presented. To verify the performance of the proposed differentiators, all of them were executed in F28379D LaunchPad microcontroller. Results show that the proposed discrete-time differentiators compute the derivative of the rotor position signal in a satisfactory manner, even in the presence of noise.

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Asynchronous State Estimator Design for Markov Switched Systems with Incomplete Probabilities and Redundant Channels 42

Asynchronous State Estimator Design for Markov Switched Syst...

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42nd Chinese control Conference, CCC 2023

作者： Chen, Haiyang Gao, Fangzheng Mao, Xueyi School of Control Science and Engineering Tiangong University Tianjin300387 China School of Automation Nanjing Institute of Technology Nanjing211167 China Nanjing Communications Institute of Technology Publicity Department Nanjing211188 China

ISBN: (纸本)9789887581543

This paper is concerned about the H∞ asynchronous state estimator design for Markov switched systems (MSSs) with incomplete transition probabilities and redundant channels. A hidden Markov chain (HMC) is introduced to characterize the asynchronous degree between the state estimator mode and the system mode. Firstly an asynchronous full-order state estimator is devised for the MSSs. Then, by Lyapunov functional and stochastic analysis, new criteria are derived such that the augmented system consisting of the target system and state estimator is H∞ stochastically finite-time boundedness. After that, with matrix decoupling technique, a design algorithm is obtained in form of linear matrix inequalities (LMIs). Finally, the practical one-link robotic manipulator is utilized to validate the developed results. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Linear matrix inequalities

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Dynamic Performance Prediction in Batch-Based Assembly System with Bernoulli Machines and Changeovers

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Complex System Modeling and Simulation 2022年第3期2卷 224-237页

作者： Zunjun Wang Zhiyang Jia Xiuxuan Tian Jingchuan Chen Bei Pan State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Industrial and Manufacturing Systems Engineering University of Hong KongHong Kong 999077China School of Engineering Tokyo University of TechnologyTokyo 192-0982Japan

Worldwide competition and diverse demand of customers pose great challenges to manufacturing enterprises. How to organize production to achieve high productivity and low cost becomes their primary task. In the mean time, the rapid pace of technology innovation has contributed to the development of new types of flexible automation. Hence, increasing manufacturing enterprises convert to multi-product and small-batch production, a manufacturing strategy that brings increased output, reduced costs, and quick response to the market. A distinctive feature of small-batch production is that the system operates mainly in the transient states. Transient states may have a significant impact on manufacturing systems. It is therefore necessary to estimate the dynamic performance of systems. As the assembly system is a typical class of production systems, in this paper, we focus on the problem of dynamic performance prediction of the assembly systems that produce small batches of different types of products. And the system is assumed to be characterized with Bernoulli reliability machines, finite buffers, and changeovers. A mathematical model based on Markovian analysis is first derived and then, the analytical formulas for performance evaluation of three-machine assembly systems are given. Moreover, a novel approach based on decomposition and aggregation is proposed to predict dynamic performance of large-scale assembly systems that consist of multiple component lines and additional processing machines located downstream of the assemble machine. The proposed approach is validated to be highly accurate and computationally efficient when compared to Monte Carlo simulation.

关键词： flexible assembly system dynamic performance Bernoulli machines finite buffers changeovers

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A Reinforcement Learning Mission Supervisor with Memory for Human-multi-robot Coordination Systems 17th

A Reinforcement Learning Mission Supervisor with Memory for ...

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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Mo, Zhibin Zhang, Zhenyi Chen, Yutao Huang, Jie School of Electrical Engineering and Automation Fuzhou University Fuzhou350108 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350108 China

ISBN: (纸本)9789811663192

In this paper, a novel reinforcement learning mission supervisor (RLMS) with memory is proposed for human-multi-robot coordination systems (HMRCS). The existing HMRCS are known to suffer from long decision waiting time and large mission error caused by repeated human intervention, restricting the autonomy of multi-robot systems. The proposed supervisor elaborately integrates deep-Q-network (DQN) and long-short-term memory (LSTM) knowledge base within the null-space based behavioral control (NSBC) framework, so as to achieve optimal adjustment strategy of the behavioral priority in the presence of mission conflicts, and to reduce the frequency of human intervention. In particular, the proposed RLMS with memory first memorize human intervention history when robot systems are not confident in decision making when encountering emergencies, and then reload the history information when encountering the same situation that have been tackled by human previously. Simulation demonstrates the effectiveness of proposed RLMS with memory. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Detecting Defects of Wooden Boards by Improved YOLOv4-tiny Algorithm 17th

Detecting Defects of Wooden Boards by Improved YOLOv4-tiny A...

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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Qiu, Yang Ai, Zhijie Lin, Ye Xu, Zhezhuang Liu, Xinxiang College of Electrical Engineering and Automation Fuzhou University Fuzhou350000 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9789811663192

Wooden boards are widely used as raw materials for furniture. Detecting defects on the wooden is significant to improve the quality of products. In this paper, we propose an improved YOLOv4-tiny target detection algorithm with feature extraction and utilization tailored for wood defect recognition. The proposed algorithm replace the residual network of the original algorithm with the Res2Net network to enhance the feature extraction ability, and increase the different scales to improve the prediction accuracy. A testbed is deployed to collect the images of wooden boards, and the performance of the proposed algorithm is evaluated on the testbed. Compared with the original YOLOv4-tiny algorithm, the mean average precision for detecting the three types of defects deadknot, insectpest and crack was 80.06% which was an increase of 5%. Compared with other methods of wood defect detection, the proposed algorithm is more accurate and faster. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Testbeds

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Tensorized Timeline Alignment for Neural Dynamical Systems in Irregularly Sampled Time Series Prediction

Tensorized Timeline Alignment for Neural Dynamical Systems i...

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第42届中国控制会议

作者： Linxiao Qin Tao Sun Shuo Zhang Xi-Ming Sun School of Control Science and Engineering Dalian University of Technology Department of Automation Tsinghua University

For many real-world scenarios,time series prediction needs to be performed on irregularly sampled *** ordinary differential equations have successfully introduced neural dynamical system(NDS) methods into such ***,these models suffer from a large computational cost,and cannot effectively utilize the temporal information in mini-batches,due to mismatched timestamps of *** this work,a unified architecture for NDS is developed,with two novel implementations on discrete input sequences(named TDINDS) and continuous sequences(named TCINDS).The reuse of network modules effectively reduces the scale of parameters and time consumption,while maintaining *** addition,a tensorized timeline alignment method is proposed,which preserves the temporal information of each training sample via reversible mappings,improving prediction performance and reducing time complexity by at least one order of *** are conducted on the Goo gleStock dataset,where we evaluate model accuracy and computational cost by predicting irregular future *** model robustness is tested using incomplete *** results demonstrate the superior performance of TDINDS and TCINDS,with mean square error reduced by 44% and 50% *** evaluation shows that tensorized timeline alignment is also a key factor to enhance irregular time series prediction ability.

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Data-driven Fault Diagnosis Method Based on Fusion of Multi-direction and Multi-source Signals 5

Data-driven Fault Diagnosis Method Based on Fusion of Multi-...

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5th International Conference on Intelligent Autonomous Systems, ICoIAS 2022

作者： Yang, Yang Wang, Junda Li, Ning Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

As a pivotal mechanical component of turbine drive-chain, gearbox suffers from various accidents and fault diagnosis could remarkably enhance its reliability and maintain running safety and lifetime. Vibration-based analysis on single-channel is mainly adopted in previous diagnostic, which cannot catch full vibration inner information in gearbox sensors and bring model performance down. In this paper, a novel method based on the fusion of multi-direction and multi-source signals and the convolutional neural network (MDMS-CNN) is presented. This method can obtain multi-dimensional feature maps from MDMS and preserve the time correlation among different signal sequences. First, features from MDMS are extracted via a corrosion conversion method that converts signals into two-dimensional (2-D) gray images. Then, homologous images are spliced into same channels, while heterogeneous images are superimposed to different channels. Finally, fusion features are input into CNN for fault classification. In experiments, the method is validated on a gearbox diagnosis case. Results have shown that the proposed method achieves 99.86% recognition accuracy and faster convergence speed compared to others. © 2022 IEEE.

关键词： Failure analysis

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Disturbance Rejection for Pedaling Rehabilitation Robot Based on Integration of Equivalent-Input-Disturbance and Repetitive control Methods 32

Disturbance Rejection for Pedaling Rehabilitation Robot Base...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Zhou, Yujian She, Jinhua Wang, Feng Iwasaki, Makoto School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Tokyo University of Technology School of Engineering Tokyo Hachioji192-0982 Japan Nagoya Institute of Technology Department of Electrical and Mechanical Engineering Nagoya466-8555 Japan

ISBN: (纸本)9798350399714

This study integrated an improved equivalent-input-disturbance (EID) and a repetitive control methods to ensure reference tracking and enhance disturbance-rejection performance for a pedaling rehabilitation robot. A repetitive controller ensures steady-state tracking of a periodic reference input. An EID estimator with a state observer estimates and compensates for the effect of disturbances generated by patients with lower-limb impairment. The stability condition of the closed-loop system is analyzed based on the Lyapunov stability theory. Simulation results show the effectiveness of this method. © 2023 IEEE.

关键词： Disturbance rejection

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Power Grid Surveillance and control Based on Wireless Sensor Network Technologies: Review and Future Directions 5

Power Grid Surveillance and Control Based on Wireless Sensor...

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5th International Scientific Conference on Advanced engineering Technologies, ISC-AET 2020

作者： Abdulwahid, Ali Hadi Department of Automation and Control Engineering Techniques Southern Technical University Basra Iraq

In this paper, the concept of the smart grid and the emerging challenges of wireless communication technology for use on the smart grid (SG)is reviewed. One modern-day technology of great significance is wireless sensor networks (WSNs), which has the potential to ensure the smooth operation of smart grid management and increase its energy efficiency, reliability and cost-effectiveness. The advanced electric power grid infrastructure known as smart grid takes advantage of automated control and cutting-edge communication strategies to achieve greater efficiency, reliability and safety, whilst seamlessly combining renewable and alternative sources of energy. Unlike conventional communication technologies, WSNs are more beneficial to modern electric power systems because they are affordable and operate on a cooperative basis. For this reason, WSNs are considered to have good potential for improving the way electric power is produced, distributed and used. Therefore, they are a key element of the smart grid. Nevertheless, the implementation of WSNs in smart grid settings may be inadequately reliable due to the complexity and unfavourable aspects of electric power system environments. The purpose of the current work is to review WSN uses in electric power systems and the opportunities and difficulties offered by this technology, as well as to delineate possible facets of smart grid applications and wireless technologies that future research could explore. Therefore, this work is significant. © Published under licence by IOP Publishing Ltd.

关键词： Smart power grids

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Validation of Moment Monitoring Results in Rolling Mill Stand Spindles

Validation of Moment Monitoring Results in Rolling Mill Stan...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Gasiyarova, Olga A. Loginov, Boris M. Radionov, Andrey A. Gasiyarov, Vadim R. Khramshin, Vadim R. Llc Npp Uchtekh-Profi Department of Material Science Chelyabinsk Russia Magnitogorsk Iron and Steel Works Magnitogorsk Russia Moscow Polytechnic University Department of Automation and Control Moscow Russia Power Engineering and Automated Systems Institute Nosov Magnitogorsk State Technical University Magnitogorsk Russia

ISBN: (纸本)9798350395044

The authors highlight the need for the development and industrial deployment of online monitoring systems for the elastic moment on the rolling mill stand spindles. An example of a 5000 plate mill was used to demonstrate that these systems are made due to the need for a reliable moment assessment in dynamic modes. This problem is relevant for the development of control systems limiting the maximum (amplitude) values of moments when the workpieces enter the stand. The shock loading causes a multifold increase of the dynamic moment over the steady rolling moment. This results in the fatigue breakdown of spindle connections., as well as mill failures and emergency stops. The authors provide information on a telemetric elastic moment monitoring system installed on a 5000 mill. The data were validated in the industrial context. To achieve this, the authors propose a procedure where experimental research is carried out in two stages. During the first stage, the electromagnetic moment of the motor is compared to the spindle moment in the quasi-steady rolling mode when the elastic properties of the spindle are not manifested. During the second stage, the elastic moments of the upper and lower roll motors are assessed during 9 passes of reverse rolling. Some conclusions are made about the proportion of roll motor loads, spindle moment amplitudes, and the relevance of aligning rolling moments through the improvement of electric drive speed control algorithms. © 2024 IEEE.

关键词： Ductile fracture

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