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Chinese Automation Congress (CAC)

作者： Zeyang Dong Yuanqing Qin Zewei Li Hanwen Wang School of Artificial Intelligence and Automation MOE Lab of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

With the further integration of information technology and industry, the computer numerical control (CNC) production line has gradually changed from the original isolated and closed mode to an open one. It faces not only internal threats, but also threats from the Internet. Starting from the characteristics and cybersecurity problem analysis of open CNC production lines, this paper proposes a security and trust protection framework based on trusted computing 3.0 and the concept of defense in depth, where a tri-element peer architecture is introduced to realize two-way authentication between the access device and the platform and an active immunity scheme for open CNC system is designed to ensure the trust from the source, and the trusted NC-Link agent/adaper are utilized to connect the heterogeneous equipments trustedly in the production line. The proposed framework can improve the information security level of the open CNC production line while maintaining its openness.

关键词： Trusted computing Processor scheduling Operating systems Authentication Optimization methods Production Computer architecture

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A Novel Memristive Chaotic Neuron Circuit and Its Application in Chaotic Neural Networks for Associative Memory

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2021年第3期40卷 521-532页

作者： Pan, Chaoxun Hong, Qinghui Wang, Xiaoping Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

In this article, we propose a novel chaotic neuron circuit with memristive neural synapses, construct an architecture of memristive chaotic neural network (MCNN) and implement associative memory application of bipolar images. The proposed neuron circuit mainly consists of synapse module and neuron module with chaotic dynamics characteristics. The synapse module is composed of memristors which represent synaptic weights. The neuron module employs voltage feedback operational amplifiers to accomplish integral operation and output function. MCNN utilizes a memristor crossbar array to perform matrix operations and can process the information in parallel. In addition, the proposed circuit of MCNN can accomplish continuous recursive operations and meet different applications due to the programmability of the memristor. The ex-situ method is utilized to train the memristor crossbar array. Furthermore, the associative memory applications of bipolar images are carried out based on the constructed circuits of MCNN with three and nine neurons. The simulation results in PSPICE software testify the functions of the MCNN circuit.

关键词： Neurons Biological neural networks Mathematical model Memristors Chaos Associative memory Synapses Associative memory chaotic neural network (CNN) chaotic neuron memristor

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A Robust Distributed Interval Observer for LTI Systems

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IEEE TRANSACTIONS ON AUTOMATIC control 2023年第3期68卷 1337-1352页

作者： Wang, Xiaoling Su, Housheng Zhang, Fan Chen, Guanrong Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Jiangsu Engn Lab IOT Intelligent Robots IOT Robot Nanjing 210023 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Wuhan 430074 Peoples R China Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen 518107 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China

In this article, the state estimation problem of a continuous-time linear time-invariant system is investigated for the situation with unknown external disturbance and measurement noise. A robust distributed interval observer is designed, which consists of a group of sensors communicating with others through a directed graph where each sensor can only access partial information from the output of the plant. The communication among the sensors together with the heterogeneity and undetectability of the sensors result in some stringent requirements on the robust distributed interval observer construction. To resolve these restrictions, the internally positive representation originated from a single agent system is introduced into the robust distributed interval observer. After presenting detailed design and analysis, numerical simulations are demonstrated to verify the theoretical results.

关键词： Observers Linear systems Noise measurement Pollution measurement Intellectual property Sensors Uncertainty Bounding information distributed interval observer Index Terms external disturbance internally positive representation (IPR) linear time-invariant (LTI) system

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JCS: An Explainable Surface Defects Detection Method for Steel Sheet by Joint Classification and Segmentation

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IEEE ACCESS 2021年 9卷 140116-140135页

作者： Zhou, Shiyang Liu, Huaiguang Cui, Ketao Hao, Zhiqiang Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Wuhan Univ Sci & Technol Precis Mfg Inst Wuhan 430081 Peoples R China

For surface defect images that captured from a practical steel production line, different shape, size, location and texture of defect object may cause inter-class similarity and intra-class difference of defect images. Despite attractive results have been achieved in some surface methods for defect classification and segmentation, it is still far from meeting the needs of real-world applications due to lack of adaptiveness of these methods. Considering the surface defect image can be decomposed into defect foreground image and defect-free background image, the paper develops a novel joint classification and segmentation (JCS) approach to perform surface defects detection for steel sheet. It comprises of the classification method based on a class-specific and shared discriminative dictionary learning (CASDDL) and the segmentation method based on a double low-rank based matrix decomposition (DLMD), respectively. For the proposed CASDDL method, we learn a shared sub-dictionary as well as several class-specific sub-dictionaries to explicitly capture common information shared by all classes and class-specific information belonging to corresponding class. We adopt a mutual incoherence constrain for each sub-dictionary, a Fisher-like discriminative criterion and low-rank constrain on coding vector to improve the discriminative ability of learned dictionary. For the proposed DLMD method, we formulate the segmentation task as a double low-rank based matrix factorization problem, and the Laplacian and sparse regularization terms are introduced into the matrix decomposition framework. Experimental results demonstrate that our proposed JCS method achieve a comparable or better performance than the state-of- the-art methods in classifying and segmenting surface defects of steel sheet.

关键词： image segmentation Steel Dictionaries Object segmentation Machine learning Matrix decomposition Training Joint classification and segmentation for image class-specific and shared dictionary learning double low-rank matrix decomposition surface defects of steel sheet

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Frequency Adaptive Proportional-Repetitive control for Grid-Connected Inverters

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第9期68卷 7965-7974页

作者： Ye, Jie Liu, Linguo Xu, Jinbang Shen, Anwen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

The repetitive control (RC) scheme, which can achieve zero steady-state error tracking of any periodic signal with a known integer period, is widely employed in grid-connected inverters to mitigate feed-in current harmonics. However, the conventional RC (CRC) scheme cannot exactly compensate harmonics with fractional period caused by grid frequency variations. Furthermore, the small RC gain of the CRC scheme causes slow transient response. In this article, a proportional-repetitive control (PRC) scheme, which can accommodate a larger RC gain than the CRC scheme, is proposed to provide fast transient response for RC systems. In order to enhance the frequency adaptability of the PRC scheme with a fixed sampling rate, the Thiran all-pass filter, which is a type of infinite impulse response filter for approximating the fractional delay (FD), is used as the FD filter to achieve fast online tuning of the FD and the fast update of the coefficients. The proposed frequency adaptive PRC (FA-PRC) scheme provides grid-connected inverters with a control solution with excellent dynamic performance and accurate frequency adaptability to eliminate harmonic distortion even in the presence of grid frequency variations. A comprehensive series of experiments have demonstrated the effectiveness of the proposed FA-PRC scheme.

关键词： Harmonic analysis Frequency control Inverters Transient response Stability analysis State feedback Convergence Fractional delay (FD) frequency adaptive (FA) grid-connected inverters harmonics repetitive control (RC)

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Sensitivity analyses and measurements for group decisions using weighted scoring rules

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2021年第2期28卷 959-975页

作者： Zhao, Yong Wu, Fan Chen, Yang Huazhong Univ Sci & Technol Sch Automat Inst Syst Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Weighted scoring rules (WSRs) are usually used for solving problems of group decisions based on individual ordinal preferences. Generally, individuals are influenced by some subjective and objective factors, which lead to the mutability of individual preferences in group decisions. Therefore, while getting optimal solutions, decision makers often need to make qualitative or quantitative sensitivity analyses of their decisions or choices, and to evaluate reliabilities of the solutions. First, by using inclined angles of score vectors, this paper proposes a sensitivity measuring method and some related concepts of group decision using WSRs. Then, a calculation method is presented, and a size of a minimum disturbance group is discussed in depth because this size is also a useful index to evaluate sensitivities or reliabilities of the decisions. Next, we investigate and discuss the sensitivities of WSRs via their weights, and obtain some suggestions about designing WSRs. Finally, we use the sensitivity degree to analyze a controversy of the National Basketball Association Most Valuable Player evaluation. Our measuring methods are: well visualized, scale invariant, commensurable, and calculability.

关键词： sensitivity degree weighted scoring rule group decision disturbance

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A Universal Discrete Memristor With Application to Multi-Attractor Generation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第8期71卷 3764-3774页

作者： Zhang, Sen Li, Yongxin Lu, Daorong Wang, Xiaoping Zeng, Zhigang Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Jiangsu Peoples R China Nanjing Normal Univ Sch Elect & Automat Engn Nanjing 210097 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

Discrete memristors have been employed in discrete maps for the purpose of chaos generation and regulation. In this paper, a novel universal model for discrete memristors is proposed to generate multi-attractors. The classical H & eacute;non map and Rulkov neuron are chosen as two examples to verify the effectiveness of the proposed memristor. Coexisting homogeneous attractors are identified in the phase space by memristor-induced offset boosting. An arbitrarily desired number of coexisting attractors is extracted by the appropriate feedback strength of the memristor. What adds further interest to this case is that the amplitude is rescaled by a memristor-related parameter that works well over an infinite range. Number-related parameters are extracted to rescale the oscillation range of the chaotic signals. Moreover, CH32-based circuit implementation is built, which aligns with numerical simulation results. Finally, coexisting homogeneous chaotic signals are tested to explore their robust performance in the application of pseudo-random number generator.

关键词： Memristors Hysteresis Neurons Frequency locked loops Boosting Bifurcation Regulation Discrete memristor chaos multi-attractor chaotic map initial offset boosting pseudo-random number generator

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Dynamic model predictive control for constrained cyber-physical systems subject to actuator attacks

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2021年第4期52卷 821-831页

作者： Qin, Yuanqing Zhao, Yue Huang, Kaixing Tian, Yu-Chu Zhou, Chunjie Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat MOE Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld Australia

Modern networked control systems are cyber-physical systems (CPSs), in which the cyber space is tightly integrated with the physical world and human intervention. Forming the basis of future smart services, CPSs are expected to significantly promote our life. However, CPSs also suffer from cyber-attacks due to their increasing connections to the Internet. This paper investigates secure model predictive control (MPC) for constrained CPSs subject to actuator attacks, which intentionally manipulate control commands from a controller to actuators. In our study, the theory of invariant set is used to construct a Luenberger observer for error-bounded state estimation in the presence of actuator attacks. Then, a robust output feedback MPC controller is designed for constrained and attacked systems based on a set-membership state estimator. After that, stability conditions are theoretically established for the proposed MPC controller. A numerical example is also given to demonstrate the effectiveness of the proposed secure MPC approach.

关键词： Cyber-physical system model predictive control cyber-security actuator attack stability analysis

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State-of-charge dependent equivalent circuit model identification for batteries using sparse Gaussian process regression

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JOURNAL OF PROCESS control 2022年 112卷 1-11页

作者： Fan, Kesen Wan, Yiming Jiang, Benben Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Tsinghua Univ Ctr Intelligent & Networked Syst Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China

Due to their ease of implementation, equivalent circuit models (ECMs) of batteries are widely used in battery management systems. Generally, ECM parameters vary with operating conditions, thus how such parameter dependencies are addressed substantially influences the accuracy of an ECM over a wide operating range. In this paper, we identify an ECM whose parameters have nonlinear dependence on state-of-charge (SOC). By transforming the SOC-dependent ECM into a linear parameter varying (LPV) input-output model, we propose a non-parametric sparse Gaussian process regression (GPR) approach, which alleviates the difficulty of specifying parametric functional SOC-dependencies of model parameters. The proposed approach derives the posterior distributions of ECM parameters, thus is capable to provide both parameter estimates and their associated uncertainties. The computational cost over large datasets is significantly reduced by adopting the sparse GPR. The proposed approach is applied to the above LPV model with two noise model structures, i.e., white and colored noises. Identification results using experimental data illustrate the efficacy of the proposed approach. The use of colored noise enhances robustness under different noise levels, and achieves higher output prediction accuracy over experimental datasets. (C)& nbsp;2021 Published by Elsevier Ltd.

关键词： Equivalent circuit model State-of-charge dependent parameters Linear parameter varying system Sparse Gaussian process regression

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Synchronization of Nonidentical Neural Networks With Unknown Parameters and Diffusion Effects via Robust Adaptive control Techniques

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第2期51卷 660-672页

作者： Zhang, Hao Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist China Key Lab Image Proc & Intelligent Control Educ Wuhan 430074 Peoples R China

This paper considers the self-synchronization and tracking synchronization issues for a class of nonidentically coupled neural networks model with unknown parameters and diffusion effects. Using the special structure of neural networks with global Lipschitz activation function, nonidentical terms are treated as external disturbances, which can then be compensated via robust adaptive control techniques. For the case where no common reference trajectory is given in advance, a distributed adaptive controller is proposed to drive the synchronization error to an adjustable bounded area. For the case where a reference trajectory is predesigned, two distributed adaptive controllers are proposed, respectively, to address the tracking synchronization problem with bounded and unbounded reference trajectories, different decomposition methods are given to extract the heterogeneous characteristics. To avoid the appearance of global information, such as the spectrum of the coupling matrix, corresponding adaptive designs on coupling strengths are also provided for both cases. Moreover, the upper bounds of the final synchronization errors can be gradually adjusted according to the parameters of the adaptive designs. Finally, numerical examples are given to test the effectiveness of the control algorithms.

关键词： Adaptive control Adaptive control reaction-diffusion neural networks tracking synchronization unknown parameter

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