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第40届中国控制会议

作者： Xinjian Liu Jianxiong Li Zhi Li Yiming Fang Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment The Key Lab of Industrial Computer Control Engineering of Hebei Province

A novel super-twisting control method is proposed for a class of third-order systems with matched Lipschitz disturbance in this paper. The proposed method ensures finite-time convergence to the fourth-order sliding set, by using a homogeneous, continuously differentiable and strict Lyapunov function. Finally, numerical simulation results are shown to illustrate the effectiveness of the proposed method.

关键词： Third-order sliding mode Super-twisting algorithm Finite time control

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A novel multi-modal tactile sensor design using thermochromic material

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Science China(Information Sciences) 2019年第11期62卷 189-191页

作者： Fuchun SUN Bin FANG Hongxiang XUE Huaping LIU Haiming HUANG Department of Computer Science and Technology Tsinghua University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University College of Information Engineering Shenzhen University

Tactile sensors are important for ensuring robotic perception [1], and their performance directly affects a robot’s capability for performing dexterous operations. These sensors mimic the tactile sensing capability of human hands. This is because the skin of human hands is involved in considerably meticulous works and can provide multi-

关键词： A novel multi-modal tactile sensor design using thermochromic material

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Detection System of Remainder Particle for Space-borne Electronic Equipment Based on PIND

Detection System of Remainder Particle for Space-borne Elect...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Zhishuai Jiang Chengzhong Yu Dongmei Yan Yuliang Zhao School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China Key Lab of Industrial Computer Control Engineering of Hebei Province School of Electrical Engineering Yanshan University Qinhuangdao China

Space-borne electronic equipment is an indispensable part of the space system, but the possible remainder particle in the sealed space-borne electronic equipment causes irreparable losses to the space system. How to detect the remainder particle for space-borne electronic equipment has always been a key problem affecting the stability of the spaceborne electronic equipment and the safety of space. Particle impact noise detection (PIND) is usually used to detect remainder particle. However, how to use PIND to design a convenient and fast system to detect the remainder particle for sealed space-borne electronic equipment has always been the focus of research. In the system, artificial vibration is used by us to provide vibration conditions for the tested parts, and the noise information is collected by acoustic emission sensor. Then the data acquisition card is used to collect the information to the host computer. In order to detect remainder particle, individual pulse is extracted to process the acquired noise information firstly. Next, analyze time domain features of single pulse to judge the quality and material of remainder particle. Simultaneously, distinguish component signal or signal of remainder particle by short-time Fourier transform. Through many experiments, the accuracy of the system can reach 100% by this method.

关键词： Vibrations Electronic equipment Fourier transforms Data acquisition Feature extraction Stability analysis Safety

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Actuator Fault Estimation Based On Adaptive Iterative Learning Algorithm For Nonlinear Periodic Systems

Actuator Fault Estimation Based On Adaptive Iterative Learni...

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第40届中国控制会议

作者： Ya-qi Liu Yi-ming Fang Le Liu Xing-da Li Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University

A actuator fault estimation algorithm based on adaptive iterative learning is proposed for a class of nonlinear periodic systems with actuator fault and ***,the proposed method designs a state observer and a fault estimator based on adaptive iterative *** Lyapunov stability theory and the optimization theory are employed to prove the convergence of the *** the LMI toolbox is utilized to obtain the results of the observer gain matrix and fault estimator parameter *** method combines the iterative learning strategy with the adaptive algorithm,and utilizes not only the state error signals of the last iteration learning,but also the output error signals of this *** makes the estimated fault approximate to the real fault gradually and realizes the accurate estimation of the time-varying fault and the intermittent ***,the validity and accuracy of the proposed method are verified by the simulation of a single-link robotic arm mold.

关键词： Iterative learning scheme Adaptive algorithm Fault estimation Actuator fault Linear matrix inequality

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NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations

arXiv

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arXiv 2022年

作者： Ran, Yunlong Zeng, Jing He, Shibo Chen, Jiming Li, Lincheng Chen, Yingfeng Lee, Gimhee Ye, Qi The College of Control Science and Engineering Zhejiang University Hangzhou China The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University The Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China Fuxi AI Lab NetEase Inc. Hangzhou China The Department of Computer Science National University of Singapore Singapore

Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. However, applying them to autonomous 3D reconstruction, where a robot is required to explore a scene and plan a view path for the reconstruction, has not been studied. In this paper, we explore for the first time the possibility of using implicit neural representations for autonomous 3D scene reconstruction by addressing two key challenges: 1) seeking a criterion to measure the quality of the candidate viewpoints for the view planning based on the new representations, and 2) learning the criterion from data that can generalize to different scenes instead of a hand-crafting one. To solve the challenges, firstly, a proxy of Peak Signal-to-Noise Ratio (PSNR) is proposed to quantify a viewpoint quality;secondly, the proxy is optimized jointly with the parameters of an implicit neural network for the scene. With the proposed view quality criterion from neural networks (termed as Neural Uncertainty), we can then apply implicit representations to autonomous 3D reconstruction. Our method demonstrates significant improvements on various metrics for the rendered image quality and the geometry quality of the reconstructed 3D models when compared with variants using TSDF or reconstruction without view planning. Project webpage https://***/NeurAR/. Copyright © 2022, The Authors. All rights reserved.

关键词： computer vision

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Sensor Fault Detection in nonlinear system using Adaptive Weighted Fusion Algorithm

Sensor Fault Detection in nonlinear system using Adaptive We...

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第40届中国控制会议

作者： Fan Qin Shu-ya Yan Yi-ming Fang Jian-xiong Li Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University

In view of the problem that the state estimation contains fault values when detecting sensor faults containing noise based on the Kalman filter algorithm,a sensor fault detection method based on adaptive weighted fusion is proposed in this *** adaptive weighted fusion algorithm gives different weights to several groups of measurement output data to generate the measurement output without ***,the composite embedded cubature Kalman filter algorithm is used to get the state estimation without faults,which will make the mean or variance of the generated residuals change,so as to realize fault ***,the single-link direct drive manipulator system driven by permanent magnet brush DC motor is simulated and *** results show that,compared with the CECKF algorithm alone,the proposed method can eliminate the faults contained in the Kalman filter state estimation,and detect all the output sensor faults more effectively.

关键词： Sensor fault detection Adaptive weighted fusion State estimation Kalman filter

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Super-twisting Finite Time control for Third Order Nonlinear Systems

Super-twisting Finite Time Control for Third Order Nonlinear...

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Chinese control Conference (CCC)

作者： Xinjian Liu Jianxiong Li Zhi Li Yiming Fang Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Qinhuangdao China The Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China

关键词： Uncertain systems Numerical simulation Nonlinear systems Sliding mode control Convergence Lyapunov methods

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Deep Learning based Defect Detection Algorithm for Solar Panels

Deep Learning based Defect Detection Algorithm for Solar Pan...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Jiaqi Li Hongxu Li Yifan Wu Hailiang Zhou Luigi Manfredi Peng Li Hong Zhang Department of Key Lab of Industrial Computer Control Engineering Hebei Province Yanshan University Qinhuangdao China Department of Computing Science University of Alberta Edmonton AB Canada Hebei Baoding Jiasheng Photovoltaic Technology Co. Ltd. Department of Institute for Medical Science and Technology (IMSaT) University of Dundee United Kingdom Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

Defect detection of solar panels plays an essential role in guaranteeing product quality within automated production lines. However, traditional manual inspection of solar panel defects suffers from low efficiency. This paper proposes an enhanced YOLOv5 algorithm (EL-YOLOv5) fused with the CBAM hybrid attention module to ensure product quality. The algorithm focuses on detecting five common types of defects that frequently appear on photovoltaic production lines, namely hidden cracks, scratches, broken grids, black spots, and short circuits. This study utilizes publicly available solar panel datasets, as well as datasets collected from actual photovoltaic production lines. These datasets are annotated accordingly and used to train the proposed algorithm. The experimental results demonstrate that the proposed algorithm achieves good performance on both the public and actual solar panel defect datasets. Particularly in actual datasets, where defect features are often less apparent and defects are smaller in size, the proposed algorithm can still detect even minor black spots.

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ICA and PIND based Remainder Particle Detection Technique for Space-borne Equipment

ICA and PIND based Remainder Particle Detection Technique fo...

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IEEE International Conference on Robotics and Biomimetics

作者： Zhishuai Jiang Zengshuai Qiu Yuanda Tian Junyu Shi Chengzhong Yu Hongzhi Gao Yuliang Zhao School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China Key Lab of Industrial Computer Control Engineering of Hebei Province School of Electrical Engineering Yanshan University Qinhuangdao China DFH Satellite Co. Ltd. Beijing China

ISBN: (纸本)9781665405362

Space-borne equipment is a major component of the satellite system, which plays a very important role in the process of satellite launch and reception. Therefore, the satellite system has high requirements for the reliability and safety of space-borne equipment. The remainder particle are metal and non-metal particles inside the space-borne equipment that affect the normal operation of the space-borne equipment. When the space-borne equipment is in a weightless state, the remainder particle will produce irregular movement accordingly cause huge safety hazards to the space-borne equipment. Particle impact noise detection (PIND) by way of vibrating the space-borne equipment cause remainder particle in the space-borne equipment to collide with the inner wall of the space-borne equipment. Then, the acoustic emission sensor is used to receive the sound generated by the vibration of the remainder particles. We can analyze the received sound signal to determine whether the space-borne equipment contains remainder particle. However, how to avoid the interference of environmental noise has always been a thorny issue in the process of using PIND to detect remainder particle. In the process of detecting noise, there may be environmental noise that will inevitably affect the detection of remainder particle. In order to filter out the environmental noise, we recorded mixed audio of the remainder particle. Then the ICA algorithm is used to separate the remainder particle signal. Through many experiments, we learn that the recognition rate of this judgment method reaches 100%.

关键词： Vibrations Satellites Working environment noise Metals Transforms Interference Robot sensing systems

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Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

arXiv

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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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