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Dynamic-memory event-triggered secure consensus for nonlinear MASs with constrained scaling attacks

INFORMATION SCIENCES 2024年 685卷

作者： Zhang, Qingcao An, Qing Chen, Yin Su, Housheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Wuhan 430074 Peoples R China Wuchang Univ Technol Articial Intelligence Sch Wuhan 430223 Peoples R China Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Scotland

In this paper, we propose a dynamic-memory event-triggered scheme (DMETS) to defend against a class of constrained scaling attacks for nonlinear leader-following multi-agent systems (MASs). Our scheme takes into account a time-varying memory package, which allows for the extension of the scheme to either an event-triggered scheme (ETS) or a memory-based event-triggered scheme (METS) with system signals. We model and analyze the characteristics of the time-constrained scaling attack, and obtain sufficient conditions of security consensus for nonlinear MASs based on the attack duration parameters. Moreover, we derive the dynamic-memory gains and the event- triggered matrices that vary with the attacks scaling factor. Finally, we present simulation results to demonstrate the effectiveness and superiority of our proposed DMETS in controlling MASs under insecure network environments.

关键词： Nonlinear multi-agent systems Dynamic-memory event-triggered scheme Constrained scaling attacks Secure consensus

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Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016

arXiv

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arXiv 2020年

作者： Wu, Dongrui Xu, Yifan Lu, Bao-Liang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications - motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks - are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain computer interface

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Distributed leaderless impulsive consensus of non-linear multi -agent systems with input saturation

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NONLINEAR ANALYSIS-HYBRID SYSTEMS 2020年 36卷 100855-100855页

作者： Liu, Xiaolu Chen, Duxin Liu, Zhiwei Wang, Yan-Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China China Three Gorges Univ Hubei Prov Collaborat Innovat Ctr New Energy Micr Yichang 443002 Peoples R China Southeast Univ Sch Math Nanjing 211096 Peoples R China

This paper aims to propose dynamic consensus protocols for a class of non-linear saturated multi-agent systems. Due to the existence of non-linear dynamics of each agent, the system does not satisfy the condition named asymptotically null controllable with bounded controls (ANCBC). As a result, the widely-adopted method of low-gain feedback to solve consensus control problems for linear multi-agent systems with input saturation cannot be used. To step back, even if the system is linear and meets the ANCBC condition, the existing low-gain method or that combined with adaptive method can only make the system eventually reach zero consensus, which is unable to bring the system to dynamic consensus. In addition, from the view of energy conservation, this study proposes the impulsive control method to reduce the energy consumption of the controller. By taking advantages of the stability theory of impulsive systems, as well as the properties of Laplacian matrix and convex hull, corresponding distributed dynamic consensus protocols are proposed. Still, the shape reference set is introduced to assess the attraction domain of the leaderless MASs. Finally, the performance of the proposed anti-saturation impulsive control algorithm is demonstrated by extensive numerical experiments. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Multi-agent systems Impulsive control Consensus Non-linear systems Input saturation

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Peer-to-peer trading price and strategy optimization considering different electricity market types, tariff systems, and pricing models

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ENERGY AND BUILDINGS 2023年 300卷

作者： Zhang, Ruixiaoxiao Lee, Minhyun Zhao, Dongqi Kang, Hyuna Hong, Taehoon Hong Kong Polytech Univ Dept Bldg & Real Estate Hung Hom Kowloon Hong Kong Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan Hubei Peoples R China Yonsei Univ Dept Architecture & Architectural Engn Seoul South Korea

This study investigated the peer-to-peer (P2P) trading price and strategy based on market-driven methods within a residential microgrid consists of two prosumers (i.e., P1 and P2) and one consumer (i.e., C1), which establishes a diverse microgrid trading market in terms of peer characteristics (e.g., system configurations and electrical load profile). Five cases are proposed in this study considering different electricity market types (i.e., peer-to-grid and P2P markets), retail tariff systems (i.e., progressive and time-of-use (ToU) tariff systems) as well as market-driven P2P trading pricing models (i.e., uniform and individual pricing models). Peer electrical load data are collected from three typical four-person households in Hong Kong along with their domestic electrical appliance utilization pattern. The results indicate that the individual pricing model has led to the dynamics of P2P electricity trading price than the uniform pricing model, and P2P trading is more economically efficient under the ToU tariff system. The maximum electricity bill saving of the entire microgrid can be achieved by 31.33% in summer and by 43.02% in winter. Besides, it is observed that the installation of battery energy storage system (BESS) has facilitated the self-consumption ratio of the renewable energy system to 91.98% in summer and 100% in winter. This implies that the BESS plays a pivotal role in improving the flexibility in managing the P2P trading strategy, and enhancing the efficiency in electricity dispatching within the microgrid. This study contributes to the novelty in science since it provides a comprehensive framework that can adapt to changing market conditions.

关键词： Peer-to-Peer energy trading Time-of-Use Energy storage system Photovoltaic system Price optimization

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Hybrid model based on similar power extraction and improved temporal convolutional network for probabilistic wind power forecasting

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ENERGY 2024年 304卷

作者： Chen, Yuejiang He, Yingjing Xiao, Jiang-Wen Wang, Yan-Wu Li, Yuanzheng 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 State Grid Zhejiang Elect Power Co Ltd Econ & Technol Res Inst Hangzhou 310008 Peoples R China

Accurate wind power generation forecasting is of great significance to improve the operation of power system. Probabilistic forecasting has a higher application value in power grid because it can provide more abundant forecasting information than deterministic forecasting. In addition, multi -step forecasting can provide forecasting results in a longer time range, so that decision makers can make longer -term planning and strategic arrangements. In this paper, we propose a novel multi -step improved temporal convolutional network based on quadratic spline quantile function (MITCN-QSQF) for probabilistic wind power forecasting. First, we combine maximum information coefficient, Gaussian similarity and adaptive resample to propose an effective similar power generation feature extraction method (MGR) for power generation. Then the temporal convolutional network is improved to construct the multi -step time series forecasting model MITCN. By combining the proposed model and the powerful probabilistic forecasting method quadratic spline quantile function (QSQF), high -quality probabilistic forecasting of wind power is achieved. Through comprehensive simulations on an open -source dataset, the superiority and efficiency of the proposed method are verified. Compared with some advanced benchmarks, the proposed model can obtain more accurate deterministic and probabilistic forecasting results.

关键词： Wind power probabilistic forecasting Temporal convolutional network Multi-step forecasting Quadratic spline quantile function

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Locating and Sizing Charging Station in Multi-Period to Promote Electric Vehicles Adoption in Urban Areas

SSRN

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SSRN 2023年

作者： Hu, Dandan Huang, Liu Liu, Chen Liu, Zhi-Wei The School of Management South-central Minzu University Wuhan430074 China The School of Engineering RMIT University MelbourneVIC3001 Australia The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China

The layout of public fast charging stations often fails to meet the charging demand of electric vehicle (EV) users, resulting in charging anxiety. To address this issue and promote the widespread use of EVs in urban areas, this paper presents a multiperiod locating and sizing optimization model under dynamic demand. The model incorporates GPS trajectory extraction data to identify potential charging demand points and considers the mobile load characteristics of EVs for fusion modeling. A multiperiod change formula is proposed to reflect the interaction between public fast charging stations and EV market penetration, incorporating charging opportunity and waiting time satisfaction as measures of charging service quality. To solve the model, a heuristic algorithm combining genetic algorithm (GA) and k-medoids clustering algorithm is designed. We apply the model to an actual case of Shenzhen and find that increasing investment in charging infrastructure can boost EV market penetration in the later planning period. Moreover, the gap between charging opportunity and waiting time satisfaction tends to narrow as the planning progresses. Our findings highlight the importance of optimizing the location and size of public fast charging stations to improve the charging service quality and promote the adoption of EVs. © 2023, The Authors. All rights reserved.

关键词： Location

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Evolution of Cooperation in Snowdrift Game with Multi-Memory Mechanism

Evolution of Cooperation in Snowdrift Game with Multi-Memory...

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Chinese control Conference (CCC)

作者： Yaojun Liu Xingwen Liu Nyima Tashi Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission College of Electrical Engineering Southwest Minzu University Chengdu Sichuan China Engineering Research Center for Tibetan Information Processing School of Information Science and Technology Tibet University Lhasa China

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavior, memory-based research on evolutionary games has proliferated in recent years. In the present paper, inspired by the well-known “Stanford marshmallow experiment” (SME), we proposed a multi-memory mechanism in Snowdrift game (SDG) on spatial lattice networks whose core lies in what follows: Players are divided into two groups according to individual self-control; different memory lengths are set by group and by time in a single realization. The simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary game. More specifically, as memory length increases to an intermediate length, the cooperation level increases as well. For mechanism with short memory length, the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative behavior. These findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation.

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Integration of nonlinear disturbance observer within proxy-based sliding mode control for pneumatic muscle actuators

arXiv

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arXiv 2019年

作者： Cao, Yu Huang, Jian Wu, Dongrui Zhang, Mengshi Xiong, Caihua Li, Zhijun Key Laboratory for Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical Science Engineering State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China College of Automation Science and Engineering South China University of Technology Guangzhou510641 China

This paper presents an integration of nonlinear disturbance observer within proxy-based sliding mode control (IDO-PSMC) approach for Pneumatic Muscle Actuators (PMAs). Due to the nonlinearities, uncertainties, hysteresis, and timevarying characteristics of the PMA, the model parameters are difficult to be identified accurately, which results in unmeasurable uncertainties and disturbances of the system. To solve this problem, a novel design of proxy-based sliding mode controller (PSMC) combined with a nonlinear disturbance observer (DO) is used for the tracking control of the PMA. Our approach combines both the merits of the PSMC and the DO so that it is effective in both reducing the "chattering" phenomeno n and improving the system robustness. A constrained Firefly Algorithm is used to search for the optimal control parameters. Based on the Lyapunov theorem, the states of the PMA are shown to be globally uniformly ultimately bounded. Extensive experiments were conducted to verify the superior performance of our approach, in multiple tracking scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： Sliding mode control

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Enhancing accessibility through rail transit in congested urban areas: A cross-regional analysis

引用

JOURNAL OF TRANSPORT GEOGRAPHY 2024年 115卷

作者： Zhang, Hui Zhan, Bo Ouyang, Min Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

The efficacy of urban rail transit in reducing the impact of road congestion is the subject of debate in policy and academic communities. Despite huge challenges arising from multifaceted factors such as data limitation, the recently released annual average congestion data enables us to empirically analyze the effect of urban rail transits in terms of their contribution to the average accessibility under road congestion. This paper presents a framework to evaluate the accessibility improvement benefit of urban rail transit under road congestion from a mean-field perspective. A cross-regional analysis in 43 Chinese cities demonstrates that the rail transit networks enhance the potential accessibility and contour accessibility by up to 5.07% and 12.09%, respectively. Besides, the accessibility improvement benefit largely depends on the road congestion level, rail network size, and layout of rail network. Furthermore, simulations on randomly generated rail transit networks indicate that the population coverage and proximity to roads are two structural determinants on the performance of rail transit. The proposed framework, together with the empirical findings, provides insights for city authorities to plan urban rail networks.

关键词： Urban rail transit Accessibility improvement Cross-regional analysis Road congestion

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Extended State Observer Based Fault-Tolerant Predictive control for a Four-Mecanum Wheels Mobile Robot

Extended State Observer Based Fault-Tolerant Predictive Cont...

引用

IEEE International Conference on Cyber Technology in Automation, control, and intelligent Systems

作者： Binghao Yang Dongliang Wang Ziling Wen Wu Wei Wenji Li Zhun Fan Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Guangdong China School of Department of Electronic and Information Engineering Shantou University Guangdong China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou Guangdong China School of Automation Science and Engineering South China University of Technology Guangzhou Guangdong China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment.

关键词： Fault tolerance Trajectory tracking Fault tolerant systems Wheels Transportation Kinematics Predictive models Safety State estimation Predictive control

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