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Distributed Predefined-Time Optimization and control for Multi-Bus DC Microgrid

IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第4期39卷 5769-5779页

作者： Wang, Yan-Wu Zhang, Yu Liu, Xiao-Kang Chen, Xia Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan Peoples R China

As an extension of single-bus DC microgrid, multi-bus DC microgrid has become a popular research topic due to its better availability and reliability and more reconfiguration options. The fundamental and challenging control objectives for multi-bus DC microgrids are bus voltage regulation and current sharing with transmission loss minimization. In this paper, a multi-objective optimization problem with penalty factors is formulated, and the global optimal solution is presented explicitly. A distributed predefined-time optimization and control approach is designed by employing a time-dependent function and a distributed predefined-time observer. Under the proposed approach, bus voltage, output current, and power flow converge within a predefined settling time. The performance of the proposed approach is evaluated by simulations and hardware-in-the-loop experiments in terms of trade-off ability, impact of parameter changes, robustness to load variation and grid reconfiguration, plug-and-play functionality, and comparison case.

关键词： Optimization Microgrids Voltage control Propagation losses Minimization Convergence Load flow Bus voltage regulation current sharing multi-bus DC microgrid predefined-time optimization and control transmission loss minimization

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Dynamic memory-event-triggered consensus of uncertain MASs with networked attacks and delays

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第2期361卷 852-866页

作者： Zhang, Qingcao An, Qing Su, Housheng Yin, Xiuxia Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist China Wuhan 430074 Peoples R China Wuchang Univ Technol Artificial Intelligence Sch Wuhan 430223 Peoples R China Nanchang Univ Sch Math & Comp Sci Nanchang 330031 Peoples R China

This paper addresses the problem of achieving consensus in leader-following multi-agent systems under event-triggered control, with considerations of model uncertainties, network time-delay, and random deception attacks. To address the challenges of limited network resources, we propose a novel event-triggered control scheme that employs a dynamic memorybased approach. The proposed scheme utilizes multiple historic event-triggered states that are correlated with the states of neighboring agents and the leader, and the threshold parameter varies dynamically with time. We establish a memory-based closed-loop system that considers both network delays and deception attacks. Using the Lyapunov stability theory, a sufficient condition is derived to ensure consensus performance, and the desired controller gains and triggered matrices are obtained by solving linear matrix inequalities. Besides, we provide a comparative analysis of the memoryless dynamic event-triggered control. Finally, we validate the effectiveness of proposed approach with simulation results.

关键词： Multi-agent systems Parameters uncertainty Networked delays Deception attacks Dynamic memory-event-triggered consensus

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Event-Based Distributed Average Tracking for Second-Order Heterogeneous Multiagent Systems With Uncertain Dynamics

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第5期54卷 2990-3000页

作者： Zhang, Junjia Wang, Yong Su, Housheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Army Acad Artillery & Air Def Sch Informat Secur Zhengzhou 450052 Peoples R China

The event-triggered distributed average tracking (ETDAT) problem for heterogeneous multiagent systems (MASs) with uncertain dynamics is investigated in this article. The ETDAT algorithms aim to build control laws for heterogeneous agents to follow the average states of multiple time-varying input signals in event-triggered communication networks. The uncertain dynamics of agents and the event-triggered communication mechanisms make the design of distributed average tracking (DAT) protocols difficult. To achieve ETDAT for heterogeneous MASs with uncertain dynamics, we designed two kinds of ETDAT protocols. First, on the basis of model reference adaptive control (MRAC) technology and sampling measurements, we present a class of static-gain ETDAT algorithms. In comparison to conventional DAT, the proposed ETDAT algorithms not only solve the DAT problem of heterogeneous MASs but also greatly reduce the cost of network communication. Second, dynamic-gain ETDAT algorithms based on self-adaptive principles are presented to minimize network global information needs. The above two algorithms adopt boundary layer approximation methods and dynamic event-triggered strategies, which can further reduce the chattering phenomenon and event-triggered frequency. Finally, the theoretical findings are shown with several examples.

关键词： Distributed average tracking (DAT) event-triggered mechanisms (ETMs) heterogeneous multiagent systems (MASs) model reference adaptive control (MRAC)

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A New Binary Encoding Method for Energy Consumption Patterns Quantification

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Fang, Hongliang Xiao, Jiang-Wen Wang, Yan-Wu Chung, C. Y. Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Hong Kong Polytech Univ PolyU Dept Elect Engn Hong Kong Peoples R China

Extracting users' energy consumption patterns (ECPs) from smart meter data is an important work for retailers. The existing literature usually describe these patterns by clustering the daily load curves (DLCs), but lack a clear and quantified representation to explain what the exact schema of a user is. Therefore, this article proposes a new binary encoding method for ECPs quantification. Specifically, first, both time and value intervals are divided for dimensionality reduction based on the similarity of adjacent timestamp loads. Then, a binary aggregate approximation (BAX) method is proposed to encode each DLC into a BAX word, and the BAX words of users are merged to obtain the schemas with a three-element alphabet. Finally, based on the schemas, the stability scores of users' patterns are quantified and are used to select target users for demand response (DR) measures. Case studies on a real dataset with 5566 users show that each target user averagely contributes to 0.172% of peak reduction, while each unselected user only contributes to 0.026%. Furthermore, to obtain target schemas and to find new users in future DR measures, a $K$ -means symbolic algorithm is designed to cluster BAX words of target users. The proposed encoding method and the findings can provide guidance of finding typical target users for DR measures.

关键词： Binary aggregate approximation (BAX) dimension reduction energy consumption pattern (ECP) K-means symbolic algorithm stability quantification

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Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures

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APPLIED ENERGY 2024年 355卷

作者： Xu, Maoshu Zhang, E. Wang, Sheng Shen, Yi Zou, Binchen Li, Haomiao Wan, Yiming Wang, Kangli Jiang, Kai Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China 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 Peoples R China

Accurate estimation of the State of Charge (SoC) plays a vital role in ensuring the efficient and safe operation of lithium iron phosphate (LFP) batteries. However, the flat open circuit voltage (OCV) curve of the LFP battery implies a low sensitivity to SoC, which results in large SoC estimation errors in the presence of noisy terminal voltage measurements. To address this challenge, an SoC estimation methodology utilizing an ultrasonic reflection response model is proposed, which is the first methodology regarding highly accurate and robust ultrasonic model-based SoC estimation under dynamic load profiles. Since ultrasound waves enable nondestructive acquisition of battery internal physical property changes directly associated with SoC, the ultrasonic battery near-surface reflection feature is extracted and demonstrated to exhibit a highly linear correlation with and higher sensitivity to SoC. We pioneeringly construct an empirical differential ultrasonic model to describe how the ultrasonic feature depends on the SoC and dynamic current. The advantage of such an ultrasonic model is demonstrated by theoretical and experimental results of an Adaptive Extend Kalman Filter (AEKF) and an Adaptive H-infinity Filter (AHIF) under various dynamic load profiles and temperatures. The Root Mean Square Error (RMSE) for ultrasonic model-based SoC estimation remains at approximately 1% across all tests, reducing by 36.7% compared to the voltage model, which shows its great potential in accurate SoC estimation.

关键词： Lithium -ion battery State of charge Battery ultrasonic response model Adaptive extend Kalman filter Adaptive H -infinity filter

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Exponential Stabilization of Semi-Markov Reaction-Diffusion Memristive NNs via Event-Based Spatially Pointwise-Piecewise Switching control

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第2期35卷 2655-2666页

作者： Man, Jingtao Zeng, Zhigang Xiao, Qiang Zhang, Hao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Agr Univ Coll Informat Wuhan 430070 Peoples R China

This article considers both the semi-Markov jumping phenomenon and spatial distribution characteristics when investigating the exponential stabilization of memristive neural networks (MNNs). The introduction of the semi-Markov jumping parameters relaxes the restriction on the sojourn time of Markovian MNNs. To increase the operability while ensuring control effect, a novel event-based spatially pointwise-piecewise switching control scheme is presented under a unified spatial division criterion, in which the pointwise and piecewise control can switch according to the preset event condition for the applicability to different control requirements. Moreover, by constructing a semi-Markov Lyapunov functional and utilizing the properties of the considered cumulative distribution function, the final exponential stabilization criterion and two related corollaries are obtained. Finally, simulation results illustrate the effectiveness and superiority of the proposed control strategy.

关键词： control systems Switches Sensors Actuators Memristors Markov processes Synchronization Event-based switching law exponential stabilization pointwise-piecewise control reaction-diffusion memristive neural networks (MNNs) semi-Markov jumping parameters

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TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2022年 30卷 2567-2576页

作者： Peng, Ruimin Zhao, Changming Jiang, Jun Kuang, Guangtao Cui, Yuqi Xu, Yifan Du, Hao Shao, Jianbo Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430000 Peoples R China Childrens Hosp Wuhan 430016 Peoples R China

Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. However, manual seizure subtype classification is expensive and time-consuming, whereas automatic classification usually needs a large number of labeled samples for model training. This paper proposes an EEGNet-based slim deep neural network, which relieves the labeled data requirement in EEG-based seizure subtype classification. A temporal information enhancement module with sinusoidal encoding is used to augment the first convolution layer of EEGNet. A training strategy for automatic hyper-parameter selection is also proposed. Experiments on the public TUSZ dataset and our own CHSZ dataset with infants and children demonstrated that our proposed TIE-EEGNet outperformed several traditional and deep learning models in cross-subject seizure subtype classification. Additionally, it also achieved the best performance in a challenging transfer learning scenario. Both our code and the CHSZ dataset are publicized.

关键词： Convolution Kernel Electroencephalography Brain modeling Epilepsy Feature extraction Encoding EEG seizure subtype classification EEGNet deep learning positional encoding

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Prescribed Performance-Based Secondary control for DC Microgrid

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2022年第4期37卷 2610-2619页

作者： Yuan, Qi-Fan Wang, Yan-Wu Liu, Xiao-Kang Liu, Zhi-Wei Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence andAutomat Wuhan 430074 Peoples R China

In this article, based on the prescribed performance control approach, a distributed secondary control scheme is developed for an islanded DC microgrid, to achieve current sharing among the converters and voltage regulation of the DC bus while guaranteeing the prescribed transient performance of the system. With the proposed controller, the evolution of both the bus voltage error and the current sharing error can be constrained within a predefined imposed bound. In this way, the safety of the electrical devices can be ensured and the dynamic performance of the microgrid can be improved. In addition, the controller is quite simple for implementation without sampling and delivering the information of the DC bus voltage. Simulation studies and experiment tests are carried out to demonstrate the effectiveness of the proposed secondary control scheme.

关键词： DC microgird distributed secondary control prescribed performance current sharing voltage regulation

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Predictor-Based Global Sampled-Data Output Feedback Stabilization for Nonlinear Uncertain Systems Subject to Delayed Output

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IEEE TRANSACTIONS ON AUTOMATIC control 2023年第3期68卷 1839-1846页

作者： Sun, Jiankun Yang, Jun Li, Shihua Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU Leics England Southeast Univ Sch Automat Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China

In this article, we address the problem of global sampled-data output feedback stabilization for a class of nonlinear uncertain systems with delayed output using the continuous-discrete method. Thanks to the prediction technique and feedback domination method, a novel coupled design method of predictor-based continuous-discrete observer and linear controller is proposed when only delayed sampled-data output is accessible. The proposed predictor-based observer can effectively estimate the unknown state by compensating the influences of sampling and output delay. The main advantage of the proposed control method is that the full state information and accurate model nonlinearities do not need to be known any more. The global exponential stability of the overall hybrid control system can be ensured when there hold some sufficient conditions with respect to the maximum allowable sampling period and output delay.

关键词： Observers control systems Nonlinear systems Delays Uncertain systems Uncertainty Output feedback Feedback domination global output feedback stabilization nonlinear uncertain systems output predictor

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Pose-Guided Hierarchical Semantic Decomposition and Composition for Human Parsing

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第3期53卷 1641-1652页

作者： Yang, Beibei Yu, Changqian Yu, Jin-Gang Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China Ctr Brain Comp Interface Pazhou Lab Guangzhou 510335 Peoples R China

Human parsing is a fine-grained semantic segmentation task, which needs to understand human semantic parts. Most existing methods model human parsing as a general semantic segmentation, which ignores the inherent relationship among hierarchical human parts. In this work, we propose a pose-guided hierarchical semantic decomposition and composition framework for human parsing. Specifically, our method includes a semantic maintained decomposition and composition (SMDC) module and a pose distillation (PC) module. SMDC progressively disassembles the human body to focus on the more concise regions of interest in the decomposition stage and then gradually assembles human parts under the guidance of pose information in the composition stage. Notably, SMDC maintains the atomic semantic labels during both stages to avoid the error propagation issue of the hierarchical structure. To further take advantage of the relationship of human parts, we introduce pose information as explicit guidance for the composition. However, the discrete structure prediction in pose estimation is against the requirement of the continuous region in human parsing. To this end, we design a PC module to broadcast the maximum responses of pose estimation to form the continuous structure in the way of knowledge distillation. The experimental results on the look-into-person (LIP) and PASCAL-Person-Part datasets demonstrate the superiority of our method compared with the state-of-the-art methods, that is, 55.21% mean Intersection of Union (mIoU) on LIP and 69.88% mIoU on PASCAL-Person-Part.

关键词： Semantics Task analysis image segmentation Pose estimation Legged locomotion Hierarchical systems Merging Human parsing pose distillation (PC) semantic maintained decomposition and composition (SMDC)

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