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Industrial Cyber-Physical systems (ICPS)

作者： Lingxia Mu Wenzhe Sun Youmin Zhang Nan Feng Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Mechanical Industrial & Aerospace Engineering Concordia University Montreal Canada

This paper proposes a novel method named recursive transformed component dissimilarity analysis (RTCDA) combining dissimilarity analysis algorithm and traditional sliding window technique for detecting incipient gradual faults. Firstly, orthogonal transformed components (TCs) corresponding to a new set of data in the sliding window are obtained using a recursive algorithm based on rank-one modification. Then, to quantitatively estimate the distribution difference of TCs, the dissimilarity index between TCs of the new dataset and that of referenced dataset is calculated. The distribution of TCs changes more dramatically than that of original data after a small quantitative bias in the original data. Compared with original data, TCs are more sensitive to tiny quantitative variation of dataset. Finally, case studies on a numerical example and a practical industrial fed-batch penicillin fermentation process are carried out to evaluate the performance of RTCDA method for incipient gradual fault detection.

关键词： Fault detection Cyber-physical systems Indexes Data mining

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Event Trigger Mechanism-Based Robust control for Cyber-Physical systems under DoS Attack

Event Trigger Mechanism-Based Robust Control for Cyber-Physi...

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Chinese Automation Congress (CAC)

作者： Peng Tian Huiling Xu Jiajia Zhao Lewei Dong School of Mathematics and Statistics Nanjing University of Science and Technology Nanjing P-R-China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing P.R.China

In this paper, the event trigger mechanism-based robust control problem is investigated for cyber-physical systems under denial of service attacks and disturbances. The effects of DoS attacks are regarded as a type of data packet loss. An event trigger mechanism is introduced into the control scheme design to effectively compensate the packet loss data. The robust attenuation technology is composited into control scheme to reduce the impact of disterbances. Then, by modeling the cyber-physical systems under denial of service(DoS) attack as a class of bounded sequential switching system, and constructing the corresponding Lyapunov function, the stability of the system is verified and analyzed. Finally, simulation experiments are given to verify the correctness of the above theoretical results.

关键词： Robust control Automation Switching systems Packet loss Switches Cyber-physical systems control systems

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Robust Output Synchronization of Discrete-Time Linear-Time-Invariant Multi-Agent systems Using Phase Tool

Robust Output Synchronization of Discrete-Time Linear-Time-I...

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European control Conference (ECC)

作者： Xin Mao Dan Wang Wei Chen Li Qiu School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Southern University of Science and Technology Shenzhen China Hong Kong University of Science and Technology Hong Kong S.A.R. China

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper, the output synchronization in large-scale discrete-time networks is examined by utilizing the novel phase tool, where the agent dynamics are allowed to be significantly heterogeneous. The synchronization synthesis problem is formulated and thoroughly investigated, with the goal of characterizing the allowable heterogeneity among the agents to ensure synchronization under a uniform controller. The solvability condition is provided in terms of the phases of the residue matrices of the agents at the persistent modes. When the condition is satisfied, a design procedure is given, producing a low-gain synchronizing controller. Numerical examples are given to illustrate the results.

关键词： Sufficient conditions Europe Heterogeneous networks Synchronization Multi-agent systems

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Stabilization with Prescribed Instant via Lyapunov Method

arXiv

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arXiv 2023年

作者： Kuang, Jiyuan Gao, Yabin Sun, Yizhuo Wang, Jiahui Liu, Aohua Zhao, Yue Liu, Jianxing The Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China The College of Intelligent System Science and Engineering Harbin Engineering University Harbin150001 China

This letter investigates the prescribed-instant stabilization problem for high-order integrator systems. In anothor word, the settling time under the presented controller is independent of the initial conditions and equals the prescribed time instant. The controller is designed with the concept of backstepping. A strict proof based on the Lyapunov method is presented to clamp the settling time to the prescribed time instant from both the left and right sides. This proof serves as an example to present a general framework to verify the designed stabilization property. It should be emphasized that the prescribed-time stability (PSTS) [1] can only prescribe the upper bound of the settling time and is different from this work. The detailed argumentation will be presented after a brief review of the existing important research. © 2023, CC BY.

关键词： Lyapunov methods

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Power system Events Classification technology Based on Deep-Learning

Power System Events Classification Technology Based on Deep-...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Xin Lei Hongwei Ma Bin Liu Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing China College of Information Science and Engineering China University of Petroleum (Beijing) Beijing China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

As the complexity of the power system continues to increase, the frequency of the power system anomalies is on the rise. These anomalies have significant and widespread impacts on the stability of the power grid. Therefore, the rapid and accurate classification of these anomalies is crucial in preventing their further propagation and mitigating potential economic losses. This study presents an algorithm based on Phasor Measurement Unit (PMU) data for monitoring the state of power systems and identifying the types of anomalies. First, a dataset for anomaly event classification is created based on PMU data, which is used to train and validate the anomaly event classification model. Subsequently, a robust anomaly event classification model is constructed, consisting of a residual module with one-dimensional Convolutional Neural Networks (CNN) and a cascaded fully connected neural network classifier. This algorithm has undergone rigorous testing in the IEEE New England 39 bus test system, demonstrating exceptional event recognition accuracy.

关键词： Accuracy Power system stability Propagation losses Phasor measurement units Data models Stability analysis Classification algorithms

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A Subspace-Based Non-Dominated Subset Selection Method

A Subspace-Based Non-Dominated Subset Selection Method

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Tan, Qingshan Li, Changhe Zeng, Sanyou Yang, Shengxiang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9798350314588

Environmental selection is an important process in multi-objective evolutionary algorithms (MOEAs). As the evolution progresses, the number of non-dominated solutions increases. This paper is focused on selecting a subset from excess non-dominated solutions for evolutionary or the final output. However, traditional selection methods in classical MOEAs encounter difficulties when dealing with candidate solutions that possess irregular topologies. Although the distance-based subset selection methods are not sensitive to the topologies of the candidate points, they have significant room for reducing computational complexity. In order to address the above issues, a subspace selection method is proposed in this paper. It partitions the objective space into multiple subspaces that have comparable volumes and shapes. The maximal minimum distance of each solution is considered to ensure that the sparsest solution is always chosen first. To save computational costs, only the solutions in the neighboring subspaces are taken into account. The experimental results demonstrate that the proposed subspace selection method outperforms classical selection methods in solving problems with various shapes of the Pareto front. © 2023 IEEE.

关键词： Multiobjective optimization

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Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model 42

Prediction Method of Truck Travel Time in Open Pit Mines Bas...

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42nd Chinese control Conference, CCC 2023

作者： Ao, Mengting Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9789887581543

Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver's behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Long short-term memory

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Fast Decentralized Multi-Agent Collision Avoidance Based on Safe-Reachable Sets

Fast Decentralized Multi-Agent Collision Avoidance Based on ...

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IEEE Conference on Decision and control

作者： Zikai Ouyang Junwei Liu Haibo Lu Wei Zhang Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

This paper presents a decentralized multi-agent collision avoidance method for systems with single integrator dynamics and identical maximum speeds. The key to our approach lies in the concept of safe-reachable sets, which define the set of positions that each agent can reach while avoiding collisions with its neighbors for any admissible controllers. With this concept, we develop a distributed controller by solving an online convex program, which is shown to guarantee collision-free trajectories. Furthermore, under a no temporary deadlock condition, we establish that each agent converges to its target position. Our approach is also efficient in terms of makespan, representing the total time needed for convergence. Simulation results demonstrate the effectiveness of our approach in terms of safety, convergence, and efficiency.

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Compound Fault Diagnosis of Planetary Gearbox Using Capsule Network with Multi-Scale Convolutional Neural Network

Compound Fault Diagnosis of Planetary Gearbox Using Capsule ...

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2024 Prognostics and system Health Management Conference, PHM 2024

作者： Li, Guoyan Zhang, Jingbin Lu, Guoliang School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu61173 China Taiyuan University of Technology Key Laboratory of Advance Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China School of Mechanical Engineering Key Laboratory of High-efficiency and Clean Mechanical Manufacture of MOE Shandong University National Demonstration Center for Experimental Mechanical Engineering Education Jinan250061 China

ISBN: (纸本)9798350360585

Capsule network is an innovative deep learning-based model that uses neuron vectors as inputs and outputs of the network. It can achieve independent output of multiple classification labels, and solve the problem of decoupling recognition of compound fault components in planetary gearboxes. However, the single-scale feature extraction cannot fully explore multi-scale feature information in compound fault signals. This study proposes a capsule network combined with multi-scale convolutional feature fusion to diagnose the compound fault of planetary gearbox. The multi-scale features are extracted from STFT images of vibration signals using convolutional layers with different kernel sizes;secondly, a capsule network is designed to classify and identify the fault components by using the dynamic routing algorithm. The experiments are conducted to assess the performance of the proposed method, and the results indicates that it can effectively decoupling identify the compound faults with strong anti-interference and stability. © 2024 IEEE.

关键词： Dynamic routing algorithms

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Identification of Panax Notoginseng Origin Using Terahertz Precision Spectroscopy and Neural Network Algorithm

SSRN

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SSRN 2024年

作者： Gu, Hongyu Wang, Shengfeng Hu, Songyan Wu, Xu Li, Qiuye Zhang, Rongrong Zhang, Juan Zhang, Wenbin Peng, Yan University of Shanghai for Science and Technology Terahertz Technology Innovation Research Institute Shanghai Key Lab of Modern Optical System Shanghai Institute of Intelligent Science and Technology University of Shanghai for Science and Technology Shanghai200093 China Wenshan Institute for Food and Drug Control Yunnan663099 China Wenshan Sanqi Institute of Science and Technology China

Panax notoginseng (P. notoginseng), a Chinese herb containing various saponins, benefits immune system in medicines development, which from Wenshan (authentic cultivation) is often counterfeited by others for large demand and limited supply. Here, we proposed a method for identifying P. notoginseng origin combining terahertz (THz) precision spectroscopy and neural network. Based on the comparative analysis of four qualitative identification methods, we chose high-performance liquid chromatography (HPLC) and THz spectroscopy to detect 252 samples from five origins. After classifications using Convolutional Neural Networks (CNNs) model, we found that the performance of THz spectra was superior to that of HPLC. The underlying mechanism is that there are clear nonlinear relations among the THz spectra and the origins due to the wide spectra and multi-parameter characteristics, which makes the accuracy of five-classification origin identification up to 97.62%. This study realizes the rapid, non-destructive and accurate identification of P. notoginseng origin, providing a practical reference for herbal medicine. © 2024, The Authors. All rights reserved.

关键词： High performance liquid chromatography

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