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A novel multi-modal tactile sensor design using thermochromic material

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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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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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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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

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： 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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Phase-Based Quantification of Sports Performance Metrics Using a Smart IoT Sensor

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IEEE Internet of Things Journal 2023年第18期10卷 15900-15911页

作者： Chen, Meng Szu, Hui Fang Lin, Hsin Yen Liu, Yifan Chan, Ho-Yin Wang, Yufan Zhao, Yuliang Zhang, Guanglie Yao, Jeffrey Da-Jeng Li, Wen Jung National Tsing Hua University Physical Education Office Hsinchu City300 Taiwan Kla Corporation Tainan City744 Taiwan Michigan State University Department of Electronic and Computer Engineering East LansingMI48824 United States Shanghai Jiao Tong University School of Mechanical Engineering Department of Industrial Engineering and Management Shanghai200240 China Northeastern University at Qinhuangdao Hebei Key Lab. of Micro-Nano Prec. Optical Sensing and Msrmt. Technol. Sch. of Control Engineering Qinhuangdao066004 China City University of Hong Kong Department of Mechanical Engineering Kowloon Tong Hong Kong City University of Hong Kong Shenzhen Research Institute CASŰCityU Joint Laboratory for Robotics Shenzhen518057 China National Tsing Hua University Department of Power Mechanical Engineering Hsinchu City300 Taiwan

Sports performance is often judged based on the results of a series of motions rather than observing and analyzing the detailed sequential motions that lead to the results. Hence, subjective feedback from the coaches is often ineffective in improving player performance. In this work, we custom-built a smart Internet of Things wristband motion sensor to implement data-based sports performance evaluation. A phase-based feature selection method is also proposed to assess the athletes' sequential detailed motion for selected sport activities. To demonstrate the merits of this technology, we quantified the quality of the sequential motions of a specific type of volleyball serve by analyzing 183 samples of motion data obtained from a total of 18 players. The general skill levels (i.e., elite, subelite, and amateur) of the players were identified by machine learning algorithms, with accuracies of up to 95%. Moreover, we adopted biomechanical principles to extract 11 motion-related performance metrics from various phases of the players' serve motion. We identified the distributions of these metrics across different skill levels and found eight key metrics that were highly correlated to the skill level of a players. We suggest that these metric distributions can be used as a reference for providing feedback to the coaches and players, to improve a player's skill in the future. This phased-based analysis method can potentially be applied across many sports to increase the effectiveness of athletes' training. © 2014 IEEE.

关键词： Internet of things

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Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy industrial Park

arXiv

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arXiv 2022年

作者： Zhu, Dafeng Yang, Bo Liu, Yuxiang Wang, Zhaojian Ma, Kai Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling of multiple energy sources and the uncertainty of renewable energy and demand, centralized methods require large calculation and coordination overhead. Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. The energy management problem is formulated as a partially-observable Markov decision process, which is intractable by dynamic programming due to the lack of the prior knowledge of the underlying stochastic process. The objective is to minimize long-term energy costs while ensuring the demand of users. To solve this issue and improve the calculation speed, a novel multi-agent deep reinforcement learning algorithm is proposed, which contains the following key points: counterfactual baseline for facilitating contributing agents to learn better policies, soft actor-critic for improving robustness and exploring optimal solutions. A novel reward is designed by Lagrange multiplier method to ensure the capacity constraints of energy storage. In addition, considering that the increase in the number of agents leads to performance degradation due to large observation spaces, an attention mechanism is introduced to enhance the stability of policy and enable agents to focus on important energy-related information, which improves the exploration efficiency of soft actor-critic. Numerical results based on actual data verify the performance of the proposed algorithm with high scalability, indicating that the industrial park can minimize energy costs under different demands. © 2022, CC BY-NC-ND.

关键词： Multi agent systems

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Considering Vehicle-to-grid Distributed Eventtriggered Fixed-time Energy Management Strategy for Microgrids

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IEEE Transactions on Network Science and engineering 2025年

作者： Wu, Zhongqiang Zhang, Bo Yanshan University Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University School of Electrical Engineering China

For the energy management problem of microgrids with vehicle-to-grid (V2G) characteristics of electric vehicles (EVs), a distributed event-triggered fixed-time optimization algorithm is proposed which effectively reduces the operating costs of the microgrid and the dissatisfaction rate of EV users with charging. The fixed-time optimization being used, the global optimal power output of the power generation unit and optimal power consumption for EV users are achieved quickly within a fixed time. The upper limit of convergence time of algorithm can be accurately calculated as it is independent of the initial conditions of the system. It facilitates the adjustment of power and energy storage strategies timely based on actual conditions, providing more flexibility for the system. In order to reduce the communication cost among the power generation units, a distributed event-triggered algorithm is proposed meanwhile, in which the change error of power is used as an event trigger mechanism. Compared to centralized event-triggered, it has not central controller, thereby the internal information exchanges within the microgrid are effectively reduced and the communication costs are effectively lowered. Rigorous proofs of the convergence and no-Zeno behavior of the algorithm are provided. Finally, numerical simulations in the IEEE 6-Bus systems are provided to validate the effectiveness of the proposed algorithm. © 2013 IEEE.

关键词： Energy supply

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Reinforcement Learning Combined with Heuristic Search for Solving Discrete Space Path Planning Problems

Reinforcement Learning Combined with Heuristic Search for So...

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第33届中国控制与决策会议

作者： Xiuling Zhang Xuenan Kang Kailun Wei Jinxiang Li Kai Ma Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University

Reinforcement learning(RL) has been successfully applied to solve path planning problems,but learning is generally *** main reason is not making full use of information collected during interaction with the *** paper proposes a novel method to solve the discrete space path planning problem in an environment without prior knowledge with intensive obstacles based on RL and heuristic ***,we apply Dyna-Q algorithm of RL to explore the map and search for the target point and optimize its policy with upper confidence bound(UCB).Then,when the target point is found,we use heuristic search to plan the path from the starting point to the target point and narrow the path to a small ***,we combine Dyna-Q algorithm with the heuristic search recommended path for path *** evaluate our algorithm using maze navigation *** results verify that heuristic search accelerates Dyan-Q convergence.

关键词： Dyna-Q algorithm Heuristic search Path planning Discrete space

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