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Composite Finite-Time Resilient control for Cyber-Physical Systems Subject to Actuator Attacks

IEEE TRANSACTIONS ON CYBERNETICS 2023年第2期53卷 1063-1077页

作者： Zhao, Yue Zhou, Chunjie Tian, Yu-Chu Qin, Yuanqing Huazhong Univ Sci & Technol Key Lab Sch Artificial Intelligence & Automat Minist Educ Image Proc & Intelligent Control Wuhan 430074 Peoples R China Queensland Univ Technol Sch Comp Sci Brisbane Qld 4001 Australia

Cyber-physical systems (CPSs) seamlessly integrate communication, computing, and control, thus exhibiting tight coupling of their cyber space with the physical world and human intervention. Forming the basis of future smart services, they play an important role in the era of Industry 4.0. However, CPSs also suffer from increasing cyber attacks due to their connections to the Internet. This article investigates resilient control for a class of CPSs subject to actuator attacks, which intentionally manipulate control commands from controllers to actuators. In our study, the supertwisting sliding-mode algorithm is adopted to construct a finite-time converging extended state observer (ESO) for estimating the state and uncertainty of the system in the presence of actuator attacks. Then, for the attacked system, a finite-time converging resilient controller is designed based on the proposed ESO. It integrates global fast terminal sliding-mode and prescribed performance control. Finally, an industrial CPS, permanent magnet synchronous motor control system, is investigated to demonstrate the effectiveness of the composite resilient control strategy presented in this article.

关键词： Actuators Uncertainty control systems Asymptotic stability Cyberattack Convergence Observers Actuator attacks cyber-physical systems (CPSs) extended state observer (ESO) finite-time converging global fast terminal sliding-mode control prescribed performance control resilient control

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Dynamic Graph Dismantling with Reinforcement Supervised Learning 42

Dynamic Graph Dismantling with Reinforcement Supervised Lear...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Yumei Tang, Chuancong Zhang, Hai-Tao School of Artificial Intelligence and Automation Huazhong University of Science and Technology The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

There are always some 'key' nodes in a big complex network, which can joint the most connected subgraphs. How to identify these nodes, finding a minimum set of nodes to attack for reducing the size of residual network's Largest Connected Component(LCC) to break up the original network, has become a research hotspot. Therefore, a method for determining the 'key' nodes based on reinforcement learning framework and supervised learning model is proposed. This algorithm can not only utilize the dynamic exploration ability of reinforcement learning to collect a rich training dataset, but also take advantage of the characteristics that supervised learning is adaptive and has strong generalization ability to possess high efficiency and strong robustness. In order to further improve the algorithm's performance, ϵ-greedy mechanism is used to explore more network states. The experiment results show that given the same fraction of removed nodes, our algorithm can make the residual LCC smaller in various networks which is superior to the state-of-the-art algorithms in terms of effectiveness and generalization. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Complex networks

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Uniform Solution to QSAT by P Systems with Proteins

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Chinese Journal of Electronics 2023年第4期21卷 667-672页

作者： Chun LU Xiaolong SHI Department of Control Science and Engineering Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

P systems are distributed parallel computing models in the area of membrane computing, which are inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order structures. P systems with proteins on membranes are a class of P systems, which have proved to be very efficient computing devices. Specifically, it was known that the Quantified satisfiability problem (QSAT) of a Boolean formula can be solved by a semi-uniform family of P systems with proteins on membranes and with membrane division. However, it remains open whether a uniform families of P systems with proteins on membranes can solve in polynomial time exactly the class of problems PSPACE. In this work, we present a uniform solution to QSAT problem by P systems with proteins on membranes in a linear time with respect to both the number $n$ of Boolean variables and the number $m$ of clauses of the instance, which answers the above open problem.

关键词： Proteins image processing Computational modeling Organizations Parallel processing intelligent control

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Spiking Neural P Systems With Communication on Request and Mute Rules

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第2期34卷 734-745页

作者： Wu, Tingfang Pan, Linqiang Soochow Univ Sch Comp Sci & Technol Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China

Spiking neural P systems with communication on request (SNQP systems) are neurally inspired computing devices, where a neuron actively seeks spikes from presynaptic neurons instead of passively waiting for spikes. In this work, we consider SNQP systems with mute rules (SNQPM systems), where mute rules have no communication functioning, namely the application of a mute rule only affects the number of spikes in the neuron where the rule resides, without effect on other neurons. It is demonstrated the computation capability of SNQPM systems with only mute rules does not exceed that of register machines with two registers, thereby not Turing universal. SNQPM systems are Turing universal when both mute rules and request rules are employed. Furthermore, two universal SNQPM systems with 7 neurons or 13 neurons are constructed as devices of number generating and function computing, respectively. Comparing to the universal SNQP system with 14 neurons and two types of spikes, SNQPM systems show the capability of trading-off mute rules and the types of spikes.

关键词： Bio-inspired computing distributed architectures neural computation spiking neural P system computation capability

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Neighborhood-based particle swarm optimization with discrete crossover for nonlinear equation systems

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 69卷 101019-101019页

作者： Pan, Linqiang Zhao, Yi Li, Lianghao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

Solving nonlinear equation systems (NESs) is important in engineering and science. In general, NESs have more than one root, and it is a great challenge to locate multiple roots in a single run for numerical computation. In order to tackle this challenging task, we propose a neighborhood-based particle swarm optimization with discrete crossover (MNPSO). MNPSO has three main characteristics: (1) it adopts a two-stage framework to balance exploitation and exploration well;(2) a distance-based adaptive neighborhood technique is proposed, which can form an appropriate neighborhood for each particle;(3) a discrete crossover operator improves the performance of PSO in the high dimension problems. We choose thirty NESs with different features as the test suite to evaluate the performance of MNPSO. The experimental results reveal that MNPSO can locate multiple roots in a single run, and the root ratio and the success ratio of MNPSO are higher than other methods.

关键词： Nonlinear equation system Particle swarm optimization Evolutionary algorithm Niching technique

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Asynchronous homogenous spiking neural P systems with local rule synchronization

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THEORETICAL COMPUTER SCIENCE 2022年 926卷 51-61页

作者： Zhang, Luping Xu, Fei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Minist China Wuhan 430074 Hubei Peoples R China

Homogenous spiking neural P systems (HSNP systems) are a class of neuron-inspired computing models, where each neuron contains an identical set of rules. It remains open how to design universal asynchronous HSNP systems. In this work, we introduce local rule synchronization into asynchronous HSNP systems, and such systems are abbreviated as AHSNPR systems. Specifically, a family of the rule sets is specified;a rule in a specified rule set is applied synchronously with all the other rules in the same set, and a rule not in any specified rule set is applied asynchronously. We investigate the number generating power of AHSNPR systems. It is proved that general and unbounded AHSNPR systems are universal, and bounded AHSNPR systems are only able to characterize the semilinear sets of numbers achieving the corresponding properties of decidability and closure. The results show that the local rule synchronization is useful in constructing universal asynchronous HSNP systems. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Bio-inspired computing Membrane computing Spiking neural P system Homogenous neuron Asynchronous mode Rule synchronization

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A dynamic multi-objective optimization approach for integrated timetabling and vehicle scheduling

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Shen, Yindong Xie, Wenliang Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol MOE Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Dynamic integrated timetabling and vehicle scheduling (D-ITVS) is essential for mitigating the negative impacts of service disruptions arising from the stochastic nature of traffic flow and passenger demand fluctuations in public transit. Existing optimization approaches for the D-ITVS problem typically conduct optimization independently at each rescheduling stage. In contrast, this paper proposes a novel dynamic multi-objective optimization approach that considers evolving patterns across different rescheduling stages from a holistic perspective. This approach formulates the optimization problem for all rescheduling stages during a day's operation as a dynamic multi-objective optimization problem, modeled using a dynamic time-space network flow framework. To leverage these evolving patterns for achieving optimal solutions throughout, a dynamic multi-objective optimization approach for the D-ITVS problem (DMO-TVS) is introduced. The DMO-TVS approach learns the evolving patterns, and incorporates a dynamic solution representation alongside three key mechanisms: (1) change detection mechanism, (2) change response mechanism, and (3) multi-objective optimization mechanism. These mechanisms work in tandem to dynamically adjust the initial solution set at each rescheduling stage based on predicted optimal solutions derived from learned evolving patterns, balance conflicting objectives in the DITVS problem, and select optimal solutions with diversity throughout the optimization process. Experimental results demonstrate that the dynamic multi-objective optimization approach is capable of generating timetables and vehicle schedules with reduced costs, enhanced robustness, and improved convergence and diversity across all rescheduling stages. By balancing operational costs and passenger service quality, these improvements benefit transit operators, and during daily operations, passengers enjoy reduced travel costs and enhanced service reliability.

关键词： Public transport Timetabling Vehicle scheduling Dynamic multi-objective optimization

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Identifying influential nodes based on the disassortativity and community structure of complex network

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SCIENTIFIC REPORTS 2024年第1期14卷 1-23页

作者： Wang, Zuxi Huang, Ruixiang Yang, Dian Peng, Yuqiang Zhou, Boyun Chen, Zhong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Natl Key Lab Multispectral Informat Intelligent Pr Wuhan 430074 Peoples R China Minist Educ China Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China

The complex networks exhibit significant heterogeneity in node connections, resulting in a few nodes playing critical roles in various scenarios, including decision-making, disease control, and population immunity. Therefore, accurately identifying these influential nodes that play crucial roles in networks is very important. Many methods have been proposed in different fields to solve this issue. This paper focuses on the different types of disassortativity existing in networks and innovatively introduces the concept of disassortativity of the node, namely, the inconsistency between the degree of a node and the degrees of its neighboring nodes, and proposes a measure of disassortativity of the node (DoN) by a step function. Furthermore, the paper analyzes and indicates that in many real-world network applications, such as online social networks, the influence of nodes within the network is often associated with disassortativity of the node and the community boundary structure of the network. Thus, the influential metric of node based on disassortativity and community structure (mDC) is proposed. Extensive experiments are conducted in synthetic and real networks, and the performance of the DoN and mDC is validated through network robustness experiments and immune experiment of disease infection. Experimental and analytical results demonstrate that compared to other state-of-the-art centrality measures, the proposed methods (DoN and mDC) exhibits superior identification performance and efficiency, particularly in non-disassortative networks and networks with clear community structures. Furthermore, we find that the DoN and mDC exhibit high stability to network noise and inaccuracies of the network data.

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Prediction Study of Solid Oxide Fuel Cell Performance Degradation Using Data-Driven Approaches

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ENERGY TECHNOLOGY 2025年第1期13卷

作者： Huo, Haibo Chen, Yu Afun, Gifty Pamela Kuang, Xinghong Xu, Jingxiang Li, Xi Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai Engn Res Ctr Marine Renewable Energy Shanghai 201306 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan 430074 Peoples R China

Performance degradation in solid oxide fuel cells (SOFCs) leads to shorter service life and unexpected downtime. To reduce economic losses and accelerate commercialization, accurately predicting the degradation is conducted in this study. First, a comprehensive analysis of performance degradation through experiments on a real SOFC system is investigated. Then, three dada-driven robust models, that is, vector autoregressive moving average (VARMA), radial basis function neural network (RBFNN), and neural basis expansion analysis for time series (N-BEATS) models are proposed to predict the SOFC's performance degradation. Herein, the top 60-90% of the experimental datasets are used for training and the bottom 40-10% for testing. After training, the prediction performance testing of these 3 models is compared with that of the bi-long short-term memory networks (bi-LSTM) and bi-gated recurrent units (bi-GRU) models. Simulation results show that both the VARMA and N-BEATS models are superior to the bi-LSTM and bi-GRU models in predicting the performance degradation of the SOFC. While the test performance of the RBFNN model is worst, especially under the top 60% training datasets condition. These indicate it is feasible to respectively establish the VARMA model and the N-BEATS model for predicting the SOFC's performance degradation.

关键词： data-driven deep learnings performance degradations solid oxide fuel cells (SOFCs)

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Deep Learning-based Serialized Model and Feature Extraction Algorithm for Solid Oxide Fuel Cell State Prediction 43

Deep Learning-based Serialized Model and Feature Extraction ...

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43rd Chinese control Conference, CCC 2024

作者： Wu, Jiajian Zhu, Kanglin Wu, Han Xiang, Guohui Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Hubei Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen China

ISBN: (纸本)9789887581581

Deep learning-based methods have shown their wide application prospects in the field of solid oxide fuel cell(SOFC) prediction. However, the irrationality of the prediction object and the lack of prediction accuracy have hindered the progress of the prediction methods for SOFC systems. Therefore, two algorithms are proposed in this study, a feature selection algorithm based on maximum information coefficient and Bayesian information criterion for identifying key variables that are sensitive to changes in the SOFC system, and a prediction model based on a genetic algorithm optimized for sequence to sequence architecture. The M/BIC algorithm reduces the number of features by 62.5% (from 40 to 15), which contributes to the acceleration of the algorithm training and the subsequent sensor layout optimization. The prediction model incorporates a two-dimensional convolutional neural network and a multi-head attention mechanism to capture more information in the input sequence, an encoder-decoder architecture and a Gated recurrent unit to better predict performance trends. Tests with real experimental data show significant changes in feature dimensionality reduction and prediction accuracy. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Dimensionality reduction

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