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Zero-Sum Game Based Secure Tracking control of UAV Against FDI Attacks Using Fixed-Time Convergent Reinforcement Learning

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Zero-Sum Game Based Secure Tracking Control of UAV Against F...

IEEE Conference on Decision and control

作者： Zhenyu Gong Feisheng Yang Dongrui Wu Research & Development Institute of Northwestern Polytechnical University in Shenzhen School of Automation Northwestern Polytechnical University Xi'an China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial and Automation Huazhong University of Science and Technology Wuhan China

In this paper, a fixed-time convergent reinforcement learning (RL) algorithm is developed to realize the secure tracking control of the unmanned aerial vehicle (UAV) via the zero-sum game for the first time. To mitigate FDI attack on actuators that may cause the UAV to deviate from the reference trajectory, a zero-sum differential game framework is built in which the secure controller tries to minimize the common performance function, yet the attacker plays a contrary role. Obtaining the optimal secure tracking controller depends on solving the Hamilton-Jacobi-Isaacs (HJI) equation related to the zero-sum game. Therefore, a critic-only online RL algorithm is proposed that can converge in a fixed time interval, with the corresponding convergence proof provided. A simulation example is given to show the effectiveness of the raised method.

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Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

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arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

Object Detection Under Varying Illuminances Using the Fusion of Dual-Attention Mode and Far Infrared-Color image

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SSRN

SSRN 2023年

作者： Guo, Jiaren Tao, Yanyun Huang, Zihao Xu, Biao School of Rail Transportation Soochow University Suzhou215005 China Key Laboratory of Information Processing and Intelligent Control Shanghai Jiaotong University 350121 China Fuzhou350121 China State Key Laboratory of New Software Nanjing University Nanjing350121 China

The multi-scale object detection under varying illuminances is a difficult task. To deal with it, we propose a dual-attention mechanism object detection model called Dual-AttentionNet. Owing to the difference between the thermal object and background in the far infrared (FIR) images, the saliency region attention extracted from FIR images can successfully cover the thermal objects. However, sometimes, the saliency region attention cannot entirely cover the small-scale objects that are not so salient. Consequently, the segmentation attention is explored to focus on the super pixels containing the thermal objects and remove the background. Therefore, some small-scale objects missed by saliency attention can be still covered by the segmentation attention. The segmentation attention and saliency attention together form the dual-attention mechanism of Dual-AttentionNet. Additionally, the fusion of infrared and color images (FIR-color) is adopted to increase the details and contour information of objects under low illuminance. Through the dual-attention mode performed on FIR-color image (FIR-color+Dual-attention mode), the multiscale objects can be enhanced and concentrated, and the general detector in Dual-AttentionNet can detect objects with small computational efforts. Ablation experiment is conducted, and the FIR-color+Dual-attention mode greatly improves the robustness of detection compared with FIR-color, FIR+Saliency attention and FIR+Segmentation attention modes. Compared with the typical multispectral pedestrian detection models, Dual-AttentionNet yields a higher mean average precision and a lower miss rate in object detection at day and night in most cases. Moreover, Dual-AttentionNet is comprehensively tested on different FIR-color image datasets and obtains high accuracy on these examples. This outcome illustrates the strong generalization performance of Dual-AttentionNet. © 2023, The Authors. All rights reserved.

关键词： image fusion

Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems

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arXiv 2023年

作者： Hu, Fu-Long Zhang, Hai-Tao Xu, Bowen Hu, Zhe Ren, Wei School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University Xi’an710072 China Department of Electrical and Computer Engineering University of California Riverside92521 United States

In this paper, a Hankel matrix-based fully distributed algorithm is proposed to address a minimal-time deadbeat consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent can predict the consensus value with the minimum number of observable historical outputs of its own. Accordingly, compared to most existing algorithms only yielding asymptotic convergence, the present method can attain deadbeat consensus instead. Moreover, based on the consensus value prediction, instant individual disagreement degree value of MASs can be calculated in advance as well. Sufficient conditions are derived to guarantee both the minimal-time deadbeat consensus and the instant individual disagreement degree prediction. Finally, both the effectiveness and superiority of the proposed deadbeat consensus algorithm are substantiated by numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

Thermoelectric Tracking control of Solid Oxide Fuel Cell System Using Adaptive Dynamic Programming

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Thermoelectric Tracking Control of Solid Oxide Fuel Cell Sys...

Chinese control and Decision Conference, CCDC

作者： Xiao-Long Wu Hongli Liu Hong Zhang Jinting Fu Bin Xie Han Hu Xi Li School of Information Engineering Nanchang University Nanchang Jiangxi School of Artificial and Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan

ISBN: (纸本)9781665478977

An adaptive optimal control strategy for solid oxide fuel cell power generation systems (SOFCs) is proposed in this paper. A new estimator is proposed in a simple form to solve the modeling uncertainty, and express the control of SOFCs as an optimal control problem. Then, a new system thermoelectric parameter tracking control strategy is proposed. The strategy uses data-driven to build the recursive neural network model of the system thermoelectric parameters. In order to ensure convergence, an adaptive law based on parameter estimation error is proposed to update the weights of neural networks online. The simulation results show that the data-driven model can effectively identify the power generation process of solid oxide fuel cell system. At the same time, the control method can realize the optimal tracking control of power, voltage, stack temperature and afterburner temperature with limited input.

关键词： Adaptation models Adaptive systems Fuel cells Process control Optimal control Solids Temperature control

Cooperative Label-Free Moving Target Fencing for Second-Order Multi-Agent Systems with Rigid Formation

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arXiv 2023年

作者： Hu, Bin-Bin Zhang, Hai-Tao Shi, Yang School of Artificial Intelligence Automation Key Laboratory of Image Processing Intelligent Control State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada

This paper proposes a label-free controller for a second-order multi-agent system to cooperatively fence a moving target of variational velocity into a convex hull formed by the agents whereas maintaining a rigid formation. Therein, no label is predetermined for a specified agent. To attain a rigid formation with guaranteed collision avoidance, each controller consists of two terms: a dynamic regulator with an internal model to drive agents towards the moving target merely by position information feedback, and a repulsive force between each pair of adjacent agents. Significantly, sufficient conditions are derived to guarantee the asymptotic stability of the closed-loop systems governed by the proposed fencing controller. Rigorous analysis is provided to eliminate the strong nonlinear couplings induced by the label-free property. Finally, the effectiveness of the controller is substantiated by numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

Data alignment based adversarial defense benchmark for EEG-based BCIs

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Neural Networks 2025年 188卷 107516页

作者： Chen, Xiaoqing Jia, Tianwang Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518063 China Zhongguancun Academy Beijing100080 China

Machine learning has been extensively applied to signal decoding in electroencephalogram (EEG)-based brain–computer interfaces (BCIs). While most studies have focused on enhancing the accuracy of EEG-based BCIs, more attention should be given to their security. Recent findings reveal that EEG-based BCIs are vulnerable to adversarial attacks. To address this, we present the first adversarial defense benchmark based on data alignment, aiming to enhance both the accuracy and robustness of EEG-based BCIs. This study evaluates nine adversarial defense approaches (including five defense strategies) across five EEG datasets (covering three paradigms), three neural networks, and four experimental scenarios. Our results show for the first time that integrating data augmentation, data alignment, and robust training can further improve both the accuracy and robustness of BCIs compared to using only one or two of them. Furthermore, we provide insights into the characteristics of various adversarial defense approaches based on EEG data alignment, offering valuable guidance for developing more accurate and secure EEG-based BCIs. © 2025 Elsevier Ltd

关键词： Adversarial attack Adversarial defense Brain–computer interface Data alignment Electroencephalogram Security

Transition in collective motion decision making

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Physical Review E 2022年第1期106卷 014611-014611页

作者： Biao Wang Yue Wu Guangwei Wang Lan Liu Guanrong Chen Hai-Tao Zhang School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan 430074 China National Engineering Laboratory for Educational Big Data Central China Normal University Wuhan 430079 China Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China

Collective decision making in a biological motion group requires fast and robust transmission of information. Typically, directional switching information propagation across the whole group obeys a linear dispersion law. However, conventional dynamic collective motion models, such as the Vicsek model and the Couzin model did not take into account ultrafast directional synchronous motions. In the present paper, a multiparticle model is proposed based on inertial spin self-propel action, which can provide adequate description of such group motion. By considering both spin mechanism and collision avoidance, the proposed self-propelled particle spin model can nicely describe collective motion with fast directional switching. By analyzing the order parameter of the group-velocity synchronization, a mechanism of group decision making is revealed, which is based on the difference between two clusters of divergent leaders, showing a transition from the compromising phase (i.e., following the group average) to the preferred phase (i.e., aligning to a leader cluster). The finding provides new insight to the decision-making process of followers when they face with divergent leaders in group motion.

关键词： Collective behavior in networks Network phase transitions Pattern formation Self-organized systems

Temporally-Adaptive Models for Efficient Video Understanding

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arXiv 2023年

作者： Huang, Ziyuan Zhang, Shiwei Pan, Liang Qing, Zhiwu Zhang, Yingya Liu, Ziwei Ang, Marcelo H. Advanced Robotics Centre National University of Singapore Singapore DAMO Academy Alibaba Group China S-Lab National Technological University Singapore Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science China

Spatial convolutions are extensively used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in different frames. This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modeling complex temporal dynamics in videos. Specifically, TAdaConv empowers spatial convolutions with temporal modeling abilities by calibrating the convolution weights for each frame according to its local and global temporal context. Compared to existing operations for temporal modeling, TAdaConv is more efficient as it operates over the convolution kernels instead of the features, whose dimension is an order of magnitude smaller than the spatial resolutions. Further, kernel calibration brings an increased model capacity. Based on this readily plug-in operation TAdaConv as well as its extension, i.e., TAdaConvV2, we construct TAdaBlocks to empower ConvNeXt and Vision Transformer to have strong temporal modeling capabilities. Empirical results show TAdaConvNeXtV2 and TAdaFormer perform competitively against state-of-the-art convolutional and Transformer-based models in various video understanding benchmarks. Our codes and models are released at: https://***/alibaba-mmai-research/TAdaConv. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution