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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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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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Event-Triggered Fault-tolerant Model Predictive control of Nonlinear Multi-agent System with Time Delay and Parameter Uncertainty

Event-Triggered Fault-tolerant Model Predictive Control of N...

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第40届中国控制会议

作者： Linglong Wang Jianxiong Li Xinjian Liu Yiming Fang Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University The Key Lab of Industrial Computer Control Engineering of Yanshan University

This paper proposes a compound control method combining with robust model predictive control(RMPC) method and the event-triggered mechanism for a class of nonlinear multi-agent system(NMAS) with time delay, uncertainties, actuator faults and input constraints. Specifically, at each sampling time, the control action is calculated by solving an online constrained optimization problem,using Lyapunov-Krasovskii functions to design state feedback control laws to ensure that the consensus error of the NMAS tends to zero and the event-triggered mechanism can save communication resources. Finally, the effectiveness of the proposed event-triggered fault-tolerant RMPC method is verified by an example.

关键词： robust model predictive control fault-tolerant control non-linear multi-agent system event-triggered mechanism

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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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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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Condition Monitoring and Fault Diagnosis of Coal Mill Based on Mechanism Modeling Simulation and D-S Evidence Fusion

Condition Monitoring and Fault Diagnosis of Coal Mill Based ...

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第34届中国控制与决策会议

作者： Tian Qiu Yuchen Yang Yue Guo Xiaofeng Chen Key Laboratory of Industrial Process Measurement and Control New Technology and System North China Electric Power University School of Control and Computer Engineering North China Electric Power University North China Electric Power Research Institute Co. Ltd

The coal mill is the key equipment of powdering system in coal-fired power plants,whose working state has an important influence on the combustion stability of boiler as well as the operation ***,owing to the lack of real fault data,it is difficult and not available enough to support and achieve the research of data-driven fault diagnosis ***-type medium-speed coal mill was selected as the research object in this ***,the existing mechanism model had been further improved to make it more effective and reasonable,and we used it to respectively simulate the typical operating data of the normal condition and three fault *** paper transformed the problem of fault diagnosis into the measurement of similarity between the testing and the typical fault samples,taking the numerical distance and changing trends into account,and adopted the concept of similarity function and transformed the results into the expression and calculation of probability level,which was calculated as the diagnostic evidence of individual parameters under the same time scale.D-S evidence theory was employed to fuse the individual evidence between multiple parameters evidence,getting the fusion of multiparametric evidence as to the final diagnosis ***,the experiments verified the high accuracy of the proposed diagnostic method,and the similarity function has high rationality and feasibility.

关键词： Coal Mill System Process Fault Dynamic Mathematical Model Similarity Function D-S Evidence Fusion Theory Fault Diagnosis

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Adaptive Muti-Scale Tf-Net for High-Resolution Time-Frequency Representations

SSRN

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SSRN 2023年

作者： Chen, Tao Chen, Qiming Zheng, Qian Li, Zhishan Zhang, Ziyi Xie, Lei Su, Hongye State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China DAMO Academy Alibaba Group Hangzhou China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Michigan 500 S. State Street Ann ArborMI48109 United States

In this paper, a novel adaptive multi-scale time-frequency network (AMTFN) is proposed to provide high-resolution time-frequency representations for nonstationary signals. AMTFN is an end-to-end deep network, which firstly adaptively learns the comprehensive basis functions to produce time-frequency (TF) feature maps through multi-scale 1D convolutional kernels. Then, the channel attention mechanism is embedded into AMTFN to rescale the TF feature maps selectively. Thus, the subsequent residual encoder-decoder block’s energy concentration performance is greatly improved with these rescaled TF feature maps. Besides, this paper designs a new training strategy to elegantly enable the model to pay more attention to the intersections of instantaneous frequencies trajectories. In the end, a series of simulations as well as real-world cases is studied to demonstrated the effectiveness and advantages of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Submodularity-based False Data Injection Attack Scheme in Multi-agent Dynamical Systems

arXiv

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arXiv 2022年

作者： Luo, Xiaoyu Zhao, Chengcheng Fang, Chongrong He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China The Department of Electrical and Computer Engineering University of Victoria BC Canada

Consensus in multi-agent dynamical systems is prone to be sabotaged by the adversary, which has attracted much attention due to its key role in broad applications. In this paper, we study a new false data injection (FDI) attack design problem, where the adversary with limited capability aims to select a subset of agents and manipulate their local multi-dimensional states to maximize the consensus convergence error. We first formulate the FDI attack design problem as a combinatorial optimization problem and prove it is NP-hard. Then, based on the submodularity optimization theory, we show the convergence error is a submodular function of the set of the compromised agents, which satisfies the property of diminishing marginal returns. In other words, the benefit of adding an extra agent to the compromised set decreases as that set becomes larger. With this property, we exploit the greedy scheme to find the optimal compromised agent set that can produce the maximum convergence error when adding one extra agent to that set each time. Thus, the FDI attack set selection algorithms are developed to obtain the near-optimal subset of the compromised agents. Furthermore, we derive the analytical suboptimality bounds and the worst-case running time under the proposed algorithms. Extensive simulation results are conducted to show the effectiveness of the proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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Heliostat Relative Efficiency Metric Inspiration for Central Receiver System

SSRN

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SSRN 2023年

作者： He, Caitou Chen, Huiling Zhang, Weizhong Zhao, Yuhong Feng, Jieqing College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325006 China State Key Laboratory of CAD&CG Zhejiang University Hangzhou310058 China Institute of Industrial Intelligence and Systems Engineering College of Control Science and Engineering Zhejiang University Hangzhou310027 China School of Data Science Fudan University Shanghai200433 China

The optimization of heliostat field layout is a topic of considerable interest to both industry and academia, as variations in solar radiation contribution and flux peak at different positions on the site can have significant impacts. Prior research has predominantly focused on optimizing from the perspective of collected solar radiation. Building on the analytical radiative flux distribution model, this study presents a novel relative efficiency metric for heliostat layout optimization, with a focus on peak expression. In this expression, mirror reflectivity, atmospheric attenuation, and receiver cosine effect can all be readily derived from the relative position, whereas the radiation diffusion factor, an important element whose relationship with slant range and slope error is unknown, requires further exploration. To this end, this study undertakes theoretical derivation, experimental simulation, and numerical approximation to determine the relationship between the diffusion factor and slant range for a specific heliostat. Notably, the results of this study demonstrate that the relative efficiency metricis highly consistent with the heliostat layout of PS10, highlighting its potential for practical application. Additionally, the study confirms several heliostat field design logics and principles for central receiver system. © 2023, The Authors. All rights reserved.

关键词： Solar radiation

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