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第三十八届中国控制会议

作者： Guangjun Wang Chengyong Ma Jianjin Jiang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Recently, compressed sensing(CS) has been widely used in ground penetrating radar(GPR) imaging due to its low sampling time, high quality performance and high quality imaging results in underground imaging. However, for densely distributed underground multi-target, the error of GPR imaging based on CS is large. So, this paper proposes a filter matrix,which can effectively reduce the energy of B-scan hyperbolic arms, increase the hyperbolic apex energy, and reduce the error of GPR imaging based on CS in densely distributed multi-target environment. The algorithm also takes into account the distance between the antenna and the ground. At first, a method of the normalized is used to calculate the echo data. Then the filter matrix designed in this paper is used. Experimental results present that the filter matrix can effectively reduce the imaging error in multi-target environment.

关键词： Compressed sensing(CS) underground imaging ground penetrating radar(GPR) multi-target

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Optimal Trajectory Planning Based on Improved Adaptive Genetic Algorithm for a 6 DOF Manipulator

Optimal Trajectory Planning Based on Improved Adaptive Genet...

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第三十八届中国控制会议

作者： Haoyang Zhang Yan Yuan Xin Chen School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper is concerned with the optimal trajectory planning for a 6 degree-of-freedom(DOF) manipulator, where the energy minimization is considered under the constraint of power. Firstly, based on the establishment of kinematics model, the influence of main factors on power is analyzed by using Jacobian matrix and Lagrange equation. Then the energy consumption is calculated as the objective function by the power consumption of each joint, and in order to obtain the desired trajectory for the arm, each joint locus is approximated by a 3-5-3 spline interpolation curve. To this end, in order to obtain the most reliable solutions for trajectory planning, the improved adaptive genetic algorithm(IAGA) is used to optimize the energy under the constraint of power, speed and acceleration, and the crossover rate and mutation rate are effectively adjusted with ***, we conduct the simulation by using the dynamics analysis software-ADAMS. The results demonstrate the effectiveness of minimizing the optimized energy in the case of constrained power mainly.

关键词： Energy optimization trajectory planning improved adaptive genetic algorithm

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Modeling and Optimization of the Activated Sludge Process

Modeling and Optimization of the Activated Sludge Process

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第三十八届中国控制会议

作者： Shun Huang Lijun Zhang Hui Guo Peng Chen Wei Xia Cong Hu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In the process of urban sewage treating, reducing the energy consumption and improving the quality of the effluent are significantly meaningful. According to the activated sludge method, the key factors affecting the energy consumption and water quality of wastewater treatment are determined. In order to minimize the energy consumption of the activated sludge process and maximize the quality of the effluent, four different objective functions are modeled [i.e., the airflow rate, the carbonaceous biochemical oxygen demand(CBOD) of the effluent, the total phosphorus(TP) of the effluent, and the ammonia nitrogen of the effluent(NH4-N)]. These models are developed using a back propagation(BP) neural network based on industrial data, and dissolved oxygen(DO) is the controlled variable. A multi-objective model was evaluated by six evaluation indicators. Based on the analysis of the model and the mechanism of activated sludge process, the multi-objective particle swarm optimization(MOPSO)algorithm was used to optimize the energy consumption and water quality of the activated sludge process. The experimental results show that eventually reduce aeration energy consumption by 17%.

关键词： Activated sludge Wastewater treatment Energy consumption Back propagation neural network Multi-objective particle swarm optimization

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Dynamic event-triggered communication based fault detection for networked singularly perturbed systems

Dynamic event-triggered communication based fault detection ...

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第三十八届中国控制会议

作者： Tizhuang Han Xiongbo Wan Jianqi An School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper investigates the problem of fault detection(FD) for networked singularly perturbed systems under the dynamic event-triggered communication. To save the network resources, a dynamic event-triggered communication mechanism(DETCM) is proposed, which contains some existing triggering mechanisms as special cases. Our aim is to design a fault detection filter(FDF) under such a DETCM, which ensures that the resulting filtering error dynamics of FD is asymptotically stable and satisfies an H∞ performance requirement. By constructing a Lyapunov function dependent on both the singular perturbation parameter(SPP) and flexible variable of the DETCM, a sufficient condition is obtained in terms of linear matrix inequalities(LMIs), which ensures the existence of the desired FDF. When these LMIs have feasible solutions, the parameters of the FDF are explicitly given and the admissible upper and lower bounds of the SPP are evaluated. A numerical example is provided to demonstrate the effectiveness of the design method of the FDF.

关键词： Fault detection Dynamic event-triggered communication Singularly perturbed systems Networked systems

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Multi-Objective Cooperative Salvo Attack Against Group Target

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Journal of systems Science & complexity 2018年第1期31卷 244-261页

作者： ZENG Jie DOU Lihua XIN Bin School of Automation State Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology

This paper proposes a solution for the problem of cooperative salvo attack of multiple cruise missiles against targets in a group. Synchronization of the arrival time of missiles to hit their common target, minimizing the time consumption of attack and maximizing the expected damage to group targets are taken into consideration simultaneously. These operational objectives result in a hierarchical mixed-variable optimization problem which includes two types of subproblems, namely the multi-objective missile-target assignment（MOMTA） problem at the upper level and the time-optimal coordinated path planning（TOCPP） problems at the lower level. In order to solve the challenging problem, a recently proposed coordinated path planning method is employed to solve the TOCPP problems to achieve the soonest salvo attack against each target. With the aim of finding a more competent solver for MOMTA, three state-of-the-art multi-objective optimization methods（MOMs）,namely NSGA-II, MOEA/D and DMOEA-εC, are adopted. Finally, a typical example is used to demonstrate the advantage of the proposed method. A simple rule-based method is also employed for comparison. Comparative results show that DMOEA-εC is the best choice among the three MOMs for solving the MOMTA problem. The combination of DMOEA-εC for MOMTA and the coordinated path planning method for TOCPP can generate obviously better salvo attack schemes than the rule-based method.

关键词： Coordinated path planning missile-target assignment multi-missile salvo attack multiobjective optimization

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： complex networks

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Large-scale hyperspectral image clustering using contrastive learning

arXiv

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arXiv 2021年

作者： Cai, Yaoming Zhang, Zijia Liu, Yan Ghamisi, Pedram Li, Kun Liu, Xiaobo Cai, Zhihua School of Computer Science China University of Geosciences Wuhan430074 China Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Vienna1030 Austria College of Electronic Science and Technology National University of Defense Technology Changsha410073 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China

Clustering of hyperspectral images is a fundamental but challenging task. The recent development of hyperspectral image clustering has evolved from shallow models to deep and achieved promising results in many benchmark datasets. However, their poor scalability, robustness, and generalization ability, mainly resulting from their offline clustering scenarios, greatly limit their application to large-scale hyperspectral data. To circumvent these problems, we present a scalable deep online clustering model, named Spectral-Spatial Contrastive Clustering (SSCC), based on self-supervised learning. Specifically, we exploit a symmetric twin neural network comprised of a projection head with a dimensionality of the cluster number to conduct dual contrastive learning from a spectral-spatial augmentation pool. We define the objective function by implicitly encouraging withincluster similarity and reducing between-cluster redundancy. The resulting approach is trained in an end-to-end fashion by batch-wise optimization, making it robust in large-scale data and resulting in good generalization ability for unseen data. Extensive experiments on three hyperspectral image benchmarks demonstrate the effectiveness of our approach and show that we advance the state-of-the-art approaches by large margins. Copyright © 2021, The Authors. All rights reserved.

关键词： Spectroscopy

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Voltage Sensitivity of Tag Antennas for Inductively Coupled RFID systems

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Journal of Physics: Conference Series 2021年第1期1738卷

作者： Cheng Jin Jixuan Zhu School of Automation China University of Geosciences (Wuhan) Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China

This paper describes a parameter of voltage sensitivity to recognize the performance differences of tag antennas for inductively coupled RFID systems. Based on the equivalent circuit model of the RFID tag and reader, an expression for the voltage sensitivity is educed. Then, the design steps of measuring platform to obtain the voltage sensitivity of tag antenna are introduced in detail. Finally, the feasibility of the proposed method was verified with the market available tag antennas through the measuring platform.

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Vibration fatigue analysis and research of URV shunt reactor 14

Vibration fatigue analysis and research of URV shunt reactor

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14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019

作者： Li, Shengmin Zheng, Zhicong Liang, Jining Xiao, Yamin Faculty of Automation and Information Engineering Xi'an University Ot Technology Xi'an710048 China Intelligent Information Processing Shaanxi Key Laboratory of Complex System Control Xi' an710048 China

ISBN: (纸本)9781538694909

Problems of vibration fatigue in UHV reactor is mentioned, comparative analysis of several commonly used fatigue analysis methods, finally, the modal superposition method is used to calculate the structural fatigue caused by vibration. Through analysis of reactor vibration data, the reason for using frequency domain signals as fatigue frequency domain load spectrum to calculate the service life of reactors. In order to perform reactor fatigue analysis, a finite element model of the reactor was first built using ANSYS Workbench finite element integration software, the frequency response function FRF is obtained by harmonic response analysis, and then the vibration time domain signal are transformed into frequency signal as load spectrum input with the power density spectrum, finally, nCode fatigue analysis software is used to estimate the service life of the reactor. © 2019 IEEE.

关键词： Finite element method

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Adaptive control for Fractional-order Interconnected systems

Adaptive Control for Fractional-order Interconnected Systems

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第三十八届中国控制会议

作者： Bingyun Liang Shiqi Zheng Zichao Yang Feng Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems

This paper focuses on a class of fractional-order interconnected system. A decentralized adaptive controller is designed based on the fractional Lyapunov direct method. In order to solve the interactions between subsystems, a compensation term is presented. In addition, the various uncertainties, such as unknown parameters, disturbances and nonlinear functions, are considered and estimated by the proposed fractional-order update laws. Especially, neural networks are adopted to approximate the unknown nonlinear functions. Furthermore, a fractional-order auxiliary system is constructed to deal with the input nonlinearities with backlash and saturation. Finally, the effectiveness of the proposed control scheme is verified by some simulation examples.

关键词： Adaptive control fractional-order system interconnected system input nonlinearity neural networks

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