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Observer-based Hierarchical Control of Heterogeneous Multiagent systems under Attacks and Disturbances 43

Observer-based Hierarchical Control of Heterogeneous Multiag...

引用

43rd Chinese Control Conference, CCC 2024

作者： Zhi, Yongran Fan, Huijin Liu, Lei Wang, Bo Huazhong University of Science and Technology The National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

The resilient cooperative output regulation of heterogeneous leader-follower multiagent systems (MASs) under asynchronous DoS attacks and unknown external disturbances is investigated in this paper. An observer-based hierarchical control frame is constructed, which contains communication layer and controller layer. In the communication layer, based on an improved dynamic event-triggered mechanism, a novel fully distributed observer is proposed to estimate the leader's state under the asynchronous DoS attack. In the controller layer, an anti-disturbance state observer is firstly designed to estimate the follower's state in the existence of unknown external disturbances, then an output feedback controller is constructed to track the estimate leader's state. The proposed two observers has increased the tolerance to DoS attacks and external disturbances. Finally, simulation results have demonstrated the effectiveness of the proposed hierarchical control frame. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Feedback control

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Unpaired Thick Cloud Removal with Multiscale SAR-optical Data Fusion 5

Unpaired Thick Cloud Removal with Multiscale SAR-optical Dat...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Xiang, Xuanyu Tan, Yihua Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9798350373820

Cloud removal is vital for the analysis of optical satellite images. To alleviate the impact of thick clouds, recent advances integrate deep learning withmultimodal data. To relax the requirements of paired training samples, existing methods utilized cycle-consistent architecture to learn the relation between cloudy and cloud-free images based on unpaired training samples. When considering thick cloud removal based on unpaired training data, the relevant studies are insufficient and they face two challenges: 1) The information in non-cloudy areas is not preserved well after cloud removal;2) The recovered information lacks consistency with the global textures and structures. Based on optical-SAR fusion and cycle-consistent training, this paper proposes Multiscale Cycle-consistent Fusion (MCF) model. MCF designs preservation loss to overcome the first challenge, and proposes global-local discriminator combined with Parallel Dilated Channel-weighted Module (PDCM) for the second challenge. MCF is evaluated both on a simulated dataset and a real dataset, which demonstrates its effectiveness. © 2024 IEEE.

关键词： Image fusion

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Trajectory optimization for passenger and freight trains on a single-track railway line 8

Trajectory optimization for passenger and freight trains on ...

引用

8th International Conference on Traffic Engineering and Transportation System, ICTETS 2024

作者： Wang, Dandan Deng, Heng Zhan, Jingyuan Zhang, Liguo Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent Systems Beijing100124 China

ISBN: (数字)9781510686298

ISBN: (纸本)9781510686281

This paper focuses on the trajectory optimization problem for both passenger and freight trains traveling in opposite directions on a single-track railway line. Firstly, we formulate a trajectory optimization problem that aims to simultaneously minimize the traction energy consumption of passenger and freight trains, as well as the total travel time of freight trains. Both types of trains are subject to their respective operational constraints, such as safety, punctuality and static speed limits. Secondly, we transform this problem into a multi-phase optimal control problem that incorporates the timetable, track gradients, speed limits and signal positions. The multi-phase optimal control problem is then solved by GPOPS-II software. Our approach enables us to model the multi-tracks at stations, allowing trains to meet and overtake at stations. Finally, the proposed method is applied to case studies of passenger and freight trains in a single-track railway, and the numerical simulation results validate the effectiveness of the proposed method. © 2024 SPIE.

关键词： Travel time

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A joint sparse Bayesian learning for wideband DOA estimation

A joint sparse Bayesian learning for wideband DOA estimation

引用

2024 Conference on Spectral technology and Applications, CSTA 2024

作者： Zhao, Yonghong Liu, Jisong Wang, Junlong Dong, Shuxin Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing Department of Automation and Information Engineering Xi'an University of Technology Shaanxi Xi'an710048 China

ISBN: (纸本)9781510683082

Wideband direction of arrival (DOA) estimation using sensor array is a noteworthy problem frequently occurring in many applications involving radar, sonar, and communication. We present a wideband DOA method based on a sparse Bayesian learning of multiple sub-bands jointly with the redundant dictionary matrix at the reference frequency. Explicitly enforcing the focusing operation and the singular value decomposition (SVD) to sensor measurements is motivated by a desire to decrease the computational complexity and storage capacity. A joint sparse Bayesian learning framework is proposed to deal with multiple sub-bands under one segment to obtain the DOA information. Moreover, the proposed method can be directly extended to multiple segments. Comparing to other wideband DOA estimation methods, the proposed method has the advantages of increased resolution, higher accuracy, and correlation of the sources, as well as not requiring the initial angles for the focusing matrix. Simulations are provided to show the effectiveness and improved performance of the proposed method. © 2024 SPIE.

关键词： Direction of arrival

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Complex Mission Planning for UAVs: A Multiple Objective Hybrid Neighborhood Search Approach

Complex Mission Planning for UAVs: A Multiple Objective Hybr...

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2024 China Automation Congress, CAC 2024

作者： Zhao, Ming Pi, Han-Lin Zhang, Jiao-Yang Wang, Bo Liu, Lei Fan, Huijin National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798350368604

In this paper, we deal with the complex mission planning problem for unmanned aerial vehicles (UAVs) via a multiple objective hybrid neighborhood search approach, in which resource allocation, task assignment and flight profile selection are incorporated in a unified framework. Three objective functions, namely, the maximum completion time, the total energy consumption and the task payback are optimized. To address the coupled submissions simultaneously, we propose a Multi-Objective Hybrid Adaptive Large Neighborhood Search (MO-HALNS) algorithm which extends the conventional Adaptive Large Neighborhood Search (ALNS) algorithm to a muli-objective version by using a population-based approach. Additionally, an evolutionary mechanism is developed, thereby managing the Pareto-optimal fronts while reducing computational costs. Simulation results demonstrate the effectiveness and superiority of the proposed framework. © 2024 IEEE.

关键词： Energy utilization

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Relative Position-based Distributed Fixed-time Formation Control of Multiple UAVs under GNSS-denied environment 43

Relative Position-based Distributed Fixed-time Formation Con...

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43rd Chinese Control Conference, CCC 2024

作者： Yang, Jiahao Zhi, Yongran Xue, Chenlong Liu, Lei Fan, Huijin Wang, Bo Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

In this paper, a novel fixed-time formation control algorithm is proposed for multiple unmanned aerial vehicles (UAVs) in environments where global navigation satellite system(GNSS) is denied, where the absolute positions of UAVs are unmeasurable, and only relative positions are accessible. To address this challenge, firstly, a composite observers is designed with one estimating relative positions between leader and followers and the other being a distributed fixed-time velocity observer. The integration of these observers enables each follower to obtain relative position and velocity of the leader within a fixed time. Subsequently, based on the composite observers and the desired formation matrix, a fixed-time controller is formulated aiming to achieve the formation task within a fixed time. Furthermore, Lyapunov stability theory demonstrates that the designed observers and controller can successfully accomplish the formation task within a fixed time. Finally, numerical simulations validate the effectiveness of the analytical results. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Unmanned aerial vehicles (UAV)

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A shape and texture feature blending network for bone marrow cells classification using auxiliary learning 12

A shape and texture feature blending network for bone marrow...

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SPIE 12th International Symposium on Multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Guo, Zhengyang Yang, Zengbiao Xiang, Xuanyu Tan, Yihua National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510674981

ISBN: (纸本)9781510674974

Morphological examination of bone marrow cells is crucial for diagnosing blood diseases. However, manual classification of bone marrow cells is time-consuming and subjective. Therefore, it is necessary to develop an autoclassification method for bone marrow cells. Although deep learning methods are commonly used for cell classification like Resnet50, they don't take advantage of the features of bone marrow cells such as the shape features of cells. However, the shape of cell and nucleus plays a significant role in distinguishing between different cell types. In this paper, we proposed a Shape and Texture Feature Blending Network (STFB-Net) for bone marrow cells classification based on auxiliary learning. We used ResNext50 as the backbone network for STFB-Net due to its exceptional ability to extract texture features from cells. In addition, we proposed the Shape Feature Extraction Module (SFEM) to enhance the backbone network's ability for capturing shape features. SFEM shares a part of parameters with the backbone network. SFEM extracts features at multiple scales and up-samples them, then fuses multi-scale features to predict the shape of cells. We performed experiments on two bone marrow cell datasets. The results show that the proposed STFB-Net can effectively extract texture and shape features, which brings better performance than other cell classification methods. By using the Grad-CAM method to visualize the features extracted by STFB-Net, we proved the reliability and the effectiveness of the STFB-Net in extracting cell shape features. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Feature extraction

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An Edge-Guided Network for Building Footprint Extraction from Optical Remote Sensing Images 12

An Edge-Guided Network for Building Footprint Extraction fro...

引用

SPIE 12th International Symposium on Multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Zhou, Yuhang Tan, Yihua National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Accurate building footprint extraction from optical remote sensing images remains challenging due to the diverse appearance and complex scenarios. Although recent deep learning-based methods have been shown to greatly improve the accuracy of building footprint extraction, vanilla deep networks still suffer from ambiguous predictions of edge pixels. The building edge contains abundant location and shape information, which is important for downstream applications such as building positioning and area measurement. Therefore, the problem of inaccurate edge prediction needs to be resolved urgently. To this end, we propose a novel edge-guided network (EGNet) that makes ample use of the edge prior in an end-to-end manner. First, an edge extraction module (EEM) is proposed to extract the building edge map. Then, an edge-guidance module (EGM) is designed to utilize the edge map to guide each encoder block in extracting edge-related features. Furthermore, a multi-scale context aggregation module (MCAM) is built to enhance the feature representation by aggregating contextual semantics with different receptive fields. EGNet can effectively mine edge semantics and guide the representation learning of boundaries, achieving 75.21% and 91.16% IoU on the Massachusetts and WHU datasets, respectively. Experimental results demonstrate that the proposed EGNet has a certain superiority in both accuracy and efficiency compared with current state-of-the-art (SOTA) methods. © 2024 SPIE.

关键词： Extraction

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Time-varying Formation-containment Tracking Control of Heterogeneous Multi-agent System under Denial of Service Attack and Sensor Fault

Time-varying Formation-containment Tracking Control of Heter...

引用

2024 China Automation Congress, CAC 2024

作者： Niu, Baojian Fan, Huijin Liu, Lei Wang, Bo National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350368604

In this paper, the problem of output-feedback time-varying formation-containment tracking control for heterogeneous multi-agent systems with sensor fault under distributed denial of service (DoS) attacks is studied. The leaders are non-cooperative, which are embedded with unknown input. Firstly, in order to estimate the sensor fault and the states of the followers, a distributed fault-detecting observer is designed. Secondly, in order to deal with distributed DoS attacks and the unknown input of the leaders, a leader state estimator is designed. Finally, a distributed output-feedback containment control law is proposed to realize the time-varying formation tracking of the convex hull of multiple leaders. It is proved that the proposed control scheme guarantees the exponential convergence of all errors of the multi-agent system, and the effectiveness and superiority of the proposed scheme are verified by simulation experiments. © 2024 IEEE.

关键词： Denial-of-service attack

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Adaptive Tracking of Uncertain Markovian Jump Nonlinear systems With Random Actuator Faults

Adaptive Tracking of Uncertain Markovian Jump Nonlinear Syst...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Jiao-Yang Fan, Huijin Liu, Lei Wang, Bo National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798350368604

This article deals with the adaptive fault-tolerant tracking problem for uncertain Markovian jump nonlinear systems (MJNSs) in a stochastic framework. A joint and a scalar Markovian process are employed to describe behaviors of the unknown parameters and actuator faults, respectively, thereby characterizing uncertainties in a random manner. With the aid of the backstepping technique, an adaptive tracking controller as well as the corresponding parameter update laws is constructed by treating the regimes of all Markovian variables as constants. It is shown that with appropriate design parameters, the output tracking error can converge into a small adjustable residual set in the sense of mean quadratic value while all closed-loop signals are bounded in probability, regardless of infinite number of random abrupt jumps in both uncertain parameters and the control input. An illustrative example is provided to show the effectiveness of the theoretical results. © 2024 IEEE.

关键词： Markov processes

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