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Diffusion-Induced Barrier Coverage of Multi-Layer Robotic Sensing Networks With Adaptive Scheduling Strategy

International Journal of Robust and Nonlinear control 2025年

作者： Fan, Pengyang Zhai, Chao Zhang, Hehong School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan China Engineering Research Center of Intelligent Technology for Geo-exploration Ministry of Education China University of Geosciences Wuhan China College of Computer and Data Science Fuzhou University Fuzhou China

The timely identification of external intruders is crucial to the protection of concerned regions or targets against malicious attacks. Multi-agent barrier coverage provides a powerful framework for the effective deployment of sensor networks to monitor baleful intruders. This paper aims to address the barrier coverage problem of robotic sensing networks by developing a multi-layer coverage formulation. Inspired by gas diffusion, a group of robotic sensors are scattered to expand the coverage territory from a gathering spot. With the assistance of a divide-and-conquer scheme, a distributed control algorithm is proposed to partition the defence barrier into multiple curve segments and integrate it with intruder monitoring. By adaptively scheduling robotic sensors among multi-layer networks, it contributes to maximizing the joint detection probability of sensing networks against external intruders. Moreover, theoretical analysis is conducted to acquire sufficient conditions for elevating the detection quality of a multi-layer sensing network. Finally, numerical simulations and robotic experiments are carried out to demonstrate the effectiveness of the proposed barrier coverage approach. © 2025 John Wiley & Sons Ltd.

关键词： Distributed parameter control systems

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Design of Variance-Constrained H∞ State Estimation Algorithm for Delayed Memristive Neural Networks with Attacks: An Adaptive Event-Triggered Approach

Design of Variance-Constrained H∞ State Estimation Algorith...

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IEEE International Conference on Cloud Computing and Intelligence systems (CCIS)

作者： Yan Gao Jun Hu Hui Yu Chaoqing Jia Department of Mathematics Harbin University of Science and Technology Harbin China School of Automation Harbin University of Science and Technology Harbin China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781665456579

This paper studies the algorithm design of variance-constrained $H_{\infty}$ state estimation problem for delayed memristive neural networks with adaptive event-triggered mechanism. The denial-of-service attacks are modeled by a series of random variables obeying the Bernoulli distribution with known probability. In addition, the adaptive event-triggered mechanism is introduced into the sensor-to-estimator to avoid unnecessary resource consumption. Our purpose is to construct a finite-horizon state estimation algorithm, and sufficient condition is obtained for the estimation error system satisfying the $H_{\infty}$ performance requirement and the error variance boundedness. Finally, a numerical example is used to illustrate the feasibility of the presented $H_{\infty}$ state estimation algorithm.

关键词： Sufficient conditions Adaptation models Wireless sensor networks Adaptive systems Neural networks Machine learning Denial-of-service attack

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Research on Temperature Error Calibration Method of Fluxgate Sensor 11th

Research on Temperature Error Calibration Method of Fluxga...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： Yang, Ruiping Ge, Jian Luo, Wang Hu, Xiangyun She, Jinhua Chugo, Daisuke Dong, Haobin 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 Faculty of Engineering Kwansai Gakuin University Osaka669-1330 Japan School of Geophysics and Geomatics China University of Geosciences Wuhan430074 China Hubei Subsurface Multi-scale Imaging Key Laboratory Wuhan430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo192-0982 Japan

ISBN: (纸本)9789819647521

The fluxgate sensor is the most widely used sensor in vector magnetic measurement. However, during long-term continuous observation, the fluxgate sensor will produce large measurement errors due to changes in ambient temperature. This paper proposes a temperature calibration method for the fluxgate sensor based on polynomial fitting to address the temperature error of fluxgate sensors. First, the effect of temperature on the performance indicators of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature calibration model of the fluxgate sensor was constructed. And compared it with other temperature calibration method, the result shows that the proposed temperature calibration method is relatively simple and can better achieve real-time calibration for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were implemented. The temperature drift was reduced from about 700 nT before calibration to about 1 nT, root mean square error RMSE=11.7189, indicating that the proposed method has a good temperature calibration effect on the data measured by the fluxgate sensor within the variable temperature background. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Curve Fitting Fluxgate Sensor Temperature Calibration

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Adaptive Fuzzy Tracking control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear systems

arXiv

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arXiv 2022年

作者： Mao, Bing Wu, Xiaoqun Liu, Hui Xu, Yuhua Lü, Jinhu The School of Mathematics and Statistics Wuhan University Hubei430072 China Hubei Key Laboratory of Computational Science Wuhan University Hubei430072 China Research Center of Complex Network Wuhan University Hubei430072 China The School of Automation Huazhong University of Science and Technology Hubei430074 China The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei430074 China The School of Statistics and Data Science Nanjing Audit University Jiangsu211815 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities. Firstly, to quantify the transient and steady performance constraints of the tracking error, a class of prescribed-time prescribed performance functions are designed, and a novel error transformation function is introduced to remove the initial value constraints and solve the singularity problem in existing works. Secondly, based on dynamic surface control methods, controllers with or without approximating structures are established to guarantee that the tracking error achieves prescribed transient performance and converges into a prescribed bounded set within prescribed time. In particular, the settling time and initial value of the prescribed performance function are completely independent of initial conditions of the tracking error and system parameters, which improves existing results. Moreover, with a novel Lyapunov-like energy function, not only the differential explosion problem frequently occurring in backstepping techniques is solved, but the drawback of the semi-global boundedness of tracking error induced by dynamic surface control can be overcome. The validity and effectiveness of the main results are verified by numerical simulations on practical examples. Copyright © 2022, The Authors. All rights reserved.

关键词： Fuzzy control

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Data-Driven Interval Robust Optimization Method of Vpp Bidding Strategy in Spot Market Under Multiple Uncertainties

SSRN

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SSRN 2024年

作者： Ma, Ying Li, Zhen Liu, Ruyi Liu, Bin Yu, Samson S. Liao, Xiaozhong Shi, Peng School of Automation Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing100081 China School of Mathematics and Statistics University of Sydney NSW2006 Australia Beijing102249 China School of Engineering Deakin University VIC3216 Australia School of Electrical and Electronic Engineering Univerty of Adelaide SA5005 Australia Research and Innovation Center Obuda University Budapest1034 Hungary

Virtual power plant (VPP) participating in the spot market can improve the flexibility of new-type power systems with the increasing penetration of renewable energy. However, multiple uncertainties on the market side, load side and generation side affect the bidding plans and operation efficiency of a VPP. In this paper, a two-stage interval robust optimization model is established to optimize the bidding plans of a VPP including gas turbine, energy storage, photovoltaic (PV) and electric vehicles (EVs). Interval numbers generated by the data-driven model are used to describe the uncertainty and correlation of electricity price in the spot market. Uncertainty sets are employed to describe the uncertainties in the number of EVs and PV power generation. The objective of the model is to jointly optimize the cost of VPP participating in the spot market considering arbitrage opportunity. The objective function containing interval numbers is transformed into a solvable form based on a ranking method considering pessimistic decisions. To solve this model, an improved column-and-constraint generation (C&CG) algorithm is developed based on the combination method of genetic algorithm (GA) and solver Gurobi. The results show that the interval numbers of electricity price generated by the data-driven model proposed in this paper can reduce the cost fluctuation of VPP by 3.2%. The two-stage interval robust model proposed in this paper can reduce the cost of VPP by 1.4% compared with the conventional single-stage robust method, and lessen the cost by 35.2% compared with the robust method that does not consider arbitrage opportunities in day-ahead and real-time markets. The improved C&CG algorithm presents superior performance in convergence and accuracy. Compared to the stochastic optimization method that generates n scenarios, the computational time of the model with the interval optimization method introduced in this paper can be reduced to 1∕n. Results from case studies illust

关键词： Digital storage

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Data-Driven Interval Robust Optimization Method of Vpp Bidding Strategy in Spot Market Under Multiple Uncertainties

SSRN

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SSRN 2024年

Virtual power plant (VPP) participating in the spot market can improve the flexibility of new-type power systems with the increasing penetration of renewable energy. However, multiple uncertainties on the market side, load side and generation side affect the bidding plans and operation efficiency of a VPP. In this paper, a two-stage interval robust optimization model is established to optimize the bidding plans of a VPP including gas turbine, energy storage, photovoltaic (PV) and electric vehicles (EVs). Interval numbers generated by the data-driven model are used to describe the uncertainty and correlation of electricity price in the spot market. Uncertainty sets are employed to describe the uncertainties in the number of EVs and PV power generation. The objective of the model is to jointly optimize the cost of VPP participating in the spot market considering arbitrage opportunity. The objective function containing interval numbers is transformed into a solvable form based on a ranking method considering pessimistic decisions. To solve this model, an improved column-and-constraint generation (C&CG) algorithm is developed based on the combination method of genetic algorithm (GA) and solver Gurobi. The results show that the interval numbers of electricity price generated by the data-driven model proposed in this paper can reduce the cost fluctuation of VPP by 3.2%. The two-stage interval robust model proposed in this paper can reduce the cost by 1.4% compared with the single-stage robust method, and lessen the cost by 35.2% compared with the robust method that does not consider arbitrage opportunities in day-ahead and real-time markets. The improved C&CG algorithm presents superior performance in convergence and accuracy. Compared to the stochastic optimization method that generates n scenarios, the computational time of the model with the interval optimization method introduced in this paper can be reduced to 1∕n. Results from case studies illustrate the effectivene

关键词： Digital storage

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How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins, and Parallel Intelligence

arXiv

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arXiv 2023年

作者： Hu, Xuemin Li, Shen Huang, Tingyu Tang, Bo Huai, Rouxing Chen, Long The School of Artificial Intelligence Hubei University Hubei Wuhan430062 China The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Beijing Huairou Academy of Parallel Sensing Beijing101499 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100864 China The Waytous Inc. Beijing100083 China

Safety and cost are two important concerns for the development of autonomous driving technologies. From the academic research to commercial applications of autonomous driving vehicles, sufficient simulation and real world testing are required. In general, a large scale of testing in simulation environment is conducted and then the learned driving knowledge is transferred to the real world, so how to adapt driving knowledge learned in simulation to reality becomes a critical issue. However, the virtual simulation world differs from the real world in many aspects such as lighting, textures, vehicle dynamics, and agents' behaviors, etc., which makes it difficult to bridge the gap between the virtual and real worlds. This gap is commonly referred to as the reality gap (RG). In recent years, researchers have explored various approaches to address the reality gap issue, which can be broadly classified into three categories: transferring knowledge from simulation to reality (sim2real), learning in digital twins (DTs), and learning by parallel intelligence (PI) technologies. In this paper, we consider the solutions through the sim2real, DTs, and PI technologies, and review important applications and innovations in the field of autonomous driving. Meanwhile, we show the state-of-the-arts from the views of algorithms, models, and simulators, and elaborate the development process from sim2real to DTs and PI. The presentation also illustrates the far-reaching effects and challenges in the development of sim2real, DTs, and PI in autonomous driving. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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The systematic error and the anomaly in the sensitivity and the probability of post-selection raised from the plural weak value

arXiv

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arXiv 2021年

作者： Huang, Jing-Hui Duan, Xue-Ying Wang, Guang-Jun Hu, Xiang-Yun Institute of Geophysics and Geomatics China University of Geosciences Wuhan430074 China 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 China State Key Laboratory of Geological Processes and Mineral Resources China University of Geosciences Hubei Wuhan430074 China

Recently, weak value Aw derived in the pre- and post-selected weak measurement has been shown to be powerful in measuring minute physical effects. In principle, the decrease in the post-selection probability will increase the sensitivity. Besides, the sensitivity which characterizes the pointer position shift is proportional to the real part of Aw, and the sensitivity which characterizes the pointer shift in momentum space is proportional to the imaginary part of Aw. However, we find that the relationship between the post-selection probability and the sensitivity is true when Aw is a purely real number or a purely imaginary number. The plural Aw will lead to the abnormal behavior where the decrease in the post-selection probability reduce the sensitivity at certain pre- and post-selection. In addition, considering the device imperfections and the environmental instability in the WVA protocol, this anomaly raised from plural Aw will reduce the sensitivity and generate a systematic error of the measurement compared with the original scheme. Finally, three feasible methods are proposed to reduce these negative effects when the weak measurement inevitably changes into measurement with a plural weak value. © 2021, CC BY.

关键词： Probability

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Model Predictive control Strategy Based on Improved Trajectory Extension Model for Deviation Correction in Vertical Drilling Process ⁎

引用

IFAC-PapersOnLine 2020年第2期53卷 11213-11218页

作者： Dian Zhang Min Wu Luefeng Chen Chengda Lu Weihua Cao Feng Wang School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and IntelligentAutomation for Complex Systems Wuhan 430074 China

Vertical drilling system is widely used in deep geological exploration. As only inclination angle is considered in conventional vertical drilling systems, which decreases the quality of drilling trajectory, especially in geological drilling. In this paper, a model predictive control strategy based on improved trajectory extension model is provided, and it aims to reduce the position deviation and inclination angle of the drilling trajectory in vertical drilling process. An improved trajectory extension model is established by considering both attitude dynamic and space movement of bottom hole assembly under ground in vertical drilling process; and then, in order to deal with control constraints directly, a model predictive controller is provided based on the improved trajectory extension model. Simulation results of deviation correction are presented for validating the proposed strategy.

关键词： Deep geological exploration vertical drilling deviation correction improved trajectory extension model model predictive control

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Comparison of the Smith-Waterman and Needleman-Wunsch algorithms for online similarity analysis of industrial alarm floods

Comparison of the Smith-Waterman and Needleman-Wunsch algori...

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IEEE Canada Electric Power Conference, EPEC

作者： Md Rezwan Parvez Wenkai Hu Tongwen Chen University of Alberta Edmonton Alberta Canada School of Automation and Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan China

ISBN: (数字)9781728164892

ISBN: (纸本)9781728164908

Alarm floods are considered to be the major obstacles that prevent the smooth process operations of large-scale industrial facilities. During an alarm flood situation, industrial operators often get confused by too many alarms and thus have difficulties in observing and handling critical alarms. In recent years, sequence alignment based similarity analysis has emerged as an effective way to handle alarm floods. Alarm floods caused by the same fault are very likely to consist of the same group of alarms in a certain sequential order. Conducting realtime sequence alignment of industrial alarm floods can help operators to quickly recall the root cause and make prompt corrective actions. This paper proposes the online similarity analysis of alarm floods based on the Smith-Waterman and Needleman-Wunsch algorithms, and compares their differences and application conditions. Case studies are provided to illustrate the proposed online similarity analysis methods and the differences of the two sequence alignment algorithms.

关键词： Heuristic algorithms Indexes Prediction algorithms Power systems Pattern matching Frequency measurement Conferences

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