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Fully distributed practical bipartite formation tracking of networked Lagrangian systems with actuator faults and bounded inputs

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INTERNATIONAL JOURNAL OF control 2023年第11期96卷 2928-2936页

作者： Ding, Teng-Fei Zhou, Kang Ge, Ming-Feng Lu, Xiang Ling, Guang China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Chem & Chem Engn Wuhan Peoples R China Wuhan Univ Technol Sch Sci Wuhan Peoples R China

This paper investigates the bipartite formation tracking (BFT) problem of networked Lagrangian systems (NLSs) with signed directed graphs. Each system suffers from actuator fault with bounded input. Firstly, the unknown input of the leader is estimated for all the followers based on an observer in the prescribed time, which is constructed by employing a time base generator (TBG). Then, a hierarchical fully distributed controller is proposed to achieve BFT without using the global information with respect to the communication graphs. Then, the related sufficient condition for the system stability is derived by means of the Lyapunov theory. Finally, a simulation example is proposed to demonstrate the performance of the hierarchical fully distributed controller for achieving BFT.

关键词： Bipartite formation tracking (BFT) networked Lagrangian systems (NLSs) hierarchical fully distributed controller signed digraphs

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Stability of homogeneous positive systems with time-varying delays

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AUTOMATICA 2023年第1期152卷

作者： Xiao, Qiang Huang, Zhenkun Zeng, Zhigang Huang, Tingwen Lewis, Frank L. Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Jimei Univ Sch Sci Xiamen Peoples R China Texas A&M Univ Qatar Doha Qatar Univ Texas Arlington UTA Res Inst Ft Worth TX USA

Stability analysis of positive linear systems has been well-developed, while attention to positive homogeneous nonlinear systems is notably less paid. This note deepens and extends some previous results on stability of homogeneous systems with bounded time-varying delays. Based on positivity property, exponential stability of a class of delayed homogeneous system with degree confined to an interval is studied, and its state boundary is further investigated in any fixed time, which is more precise than the former result in some cases. Additionally, finite-time stability is obtained for a delay-free homogeneous system and an explicit boundary is presented. (c) 2023 Published by Elsevier Ltd.

关键词： Positive system Homogeneous system Exponential stability Boundedness Time delay

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Consensus on Directed Matrix-Weighted Networks

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IEEE TRANSACTIONS ON AUTOMATIC control 2023年第4期68卷 2529-2535页

作者： Su, Housheng Miao, Suoxia Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist China Key Lab Image Proc & Intelligent Control Educ Wuhan 430074 Peoples R China East China Jiaotong Univ Dept Elect & Automat Engn Nanchang 330013 Peoples R China Nanchang Inst Technol Sch Sci Nanchang 330099 Peoples R China

Due to the interdependence of multidimensional states between agents in many practical scenarios, it is more accurate to model multiagent systems by matrix coupling networks. However, existing related results rely on undirected networks. In this article, we discuss the consensus problems for matrix-weighted continuous-time, discrete-time multiagent systems over fixed directed network topologies, and switched multiagent systems switching between continuous-time dynamics and discrete-time dynamics over fixed directed and switching undirected network topologies. Under the proposed matrix-weighted algorithms, for continuous-time, discrete-time, and switched multiagent systems, the consensus criteria are established over fixed directed network topologies by analysis theory and matrix theory. Besides, using Lyapunov stability theory, the consensus conditions of switched multiagent systems under undirected switching network topology are deduced. At last, some examples are given to demonstrate the correctness of the theoretical results.

关键词： Switches Network topology Eigenvalues and eigenfunctions Computed tomography Multi-agent systems Topology Laplace equations Continuous-time discrete-time matrix-weighted consensus multiagent systems switched system

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FCM-RDpA: TSK fuzzy regression model construction using fuzzy C-means clustering, regularization, Droprule, and Powerball Adabelief

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INFORMATION SCIENCES 2021年 574卷 490-504页

作者： Shi, Zhenhua Wu, Dongrui Guo, Chenfeng Zhao, Changming Cui, Yuqi Wang, Fei-Yue Huazhong Univ Sci & Technol Key Lab Sch Artificial Intelligence & Automat Minist Educ Image Proc & Intelligent Control Wuhan 430074 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100049 Peoples R China

To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which improves MBGD-RDA by replacing the grid partition approach in rule initialization by fuzzy c-means clustering, and AdaBound by Powerball AdaBelief, which integrates recently proposed Powerball gradient and AdaBelief to further expedite and stabilize parameter optimization. Extensive experiments on 22 regression datasets with various sizes and dimensionalities validated the superiority of FCM-RDpA over MBGD-RDA, especially when the feature dimensionality is higher. We also propose an additional approach, FCM-RDpAx, that further improves FCM-RDpA by using augmented features in both the antecedents and consequents of the rules. (c) 2021 Elsevier Inc. All rights reserved.

关键词： TSK fuzzy system Mini-batch gradient descent DropRule Powerball AdaBelief Fuzzy c-means clustering

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Design and Assessment of Sweep Coverage Algorithms for Multiagent Systems With Online Learning Strategies

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第9期52卷 5494-5505页

作者： Zhai, Chao Zhang, Hai-Tao Xiao, Gaoxi Chen, Michael Z. Q. China Univ Geosci Sch Automat Engn Res Ctr Intelligent Technol Geoexplorat Minist Educ Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Adv Control & Intelligent Automat C Engn Res Ctr Intelligent Technol Geoexplorat Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipments & Technol Wuhan 430074 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China

Cooperative sweep coverage of multiagent systems (MASs) has found broad applications in various fields. This article proposes a scheme to address the sweep coverage problem of MASs within uncertain environments. In the proposed formulation, the coverage region is divided into multiple stripes, of which each has the workload completed by MASs in sequence. When the workload on the current stripe is completed, all the agents switch to the next together. The temporal dependence between the switching time computation and the sweep coverage operation is taken into account, and an online learning strategy is designed to handle environmental uncertainties and balance the workload among agents on the same stripe. Thereby, the distributed sweep coverage algorithm is developed to guarantee the complete sweep coverage, which consists of three operations, i.e., communication, workload partition, and sweeping. Theoretical analysis is afterward conducted to estimate the upper bound for the error between the actual and optimal coverage time. Finally, numerical simulations are carried out to substantiate the effectiveness and superiority of the proposed scheme.

关键词： Uncertainty Upper bound Switches Task analysis Robot sensing systems Partitioning algorithms Multi-agent systems Distributed control distributed estimation learning control systems

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A Transfer Learning-Based Method for Personalized State of Health Estimation of Lithium-Ion Batteries

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第1期35卷 759-769页

作者： Ma, Guijun Xu, Songpei Yang, Tao Du, Zhenbang Zhu, Limin Ding, Han Yuan, Ye Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

State of health (SOH) estimation of lithium-ion batteries (LIBs) is of critical importance for battery management systems (BMSs) of electronic devices. An accurate SOH estimation is still a challenging problem limited by diverse usage conditions between training and testing LIBs. To tackle this problem, this article proposes a transfer learning-based method for personalized SOH estimation of a new battery. More specifically, a convolutional neural network (CNN) combined with an improved domain adaptation method is used to construct an SOH estimation model, where the CNN is used to automatically extract features from raw charging voltage trajectories, while the domain adaptation method named maximum mean discrepancy (MMD) is adopted to reduce the distribution difference between training and testing battery data. This article extends MMD from classification tasks to regression tasks, which can therefore be used for SOH estimation. Three different datasets with different charging policies, discharging policies, and ambient temperatures are used to validate the effectiveness and generalizability of the proposed method. The superiority of the proposed SOH estimation method is demonstrated through the comparison with direct model training using state-of-the-art machine learning methods and several other domain adaptation approaches. The results show that the proposed transfer learning-based method has wide generalizability as well as a positive precision improvement.

关键词： Estimation Feature extraction Data models Batteries Training Integrated circuit modeling Testing Convolutional neural network (CNN) lithium-ion batteries (LIBs) maximum mean discrepancy (MMD) state of health (SOH) estimation transfer learning

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CS2DT: Cross Spatial-Spectral Dense Transformer for Hyperspectral image Classification

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷 1页

作者： Xu, Hao Zeng, Zhigang Yao, Wei Lu, Jiayue Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ China Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China South Cent Minzu Univ Sch Comp Sci Wuhan 430074 Peoples R China Nanjing Univ Software Inst Nanjing 210093 Peoples R China

Compared with general optical images, hyperspectral images (HSIs) contain richer spectral information. On one hand, this provides a sufficient basis for ground object recognition. On the other hand, it results in the intermingling of spatial and spectral information. In order to make better use of the rich spatial and spectral information in HSIs, we resort to vision transformer (ViT). To be specific, we propose the cross spatial-spectral dense transformer (CS2DT) for spatial-spectral feature extracting and feature fusing. For feature extraction, CS2DT employs the adaptive dense encoder (ADE) module, which enables the extraction of multiscale semantic information. During the features fusion stage, we use the cross spatial-spectral attention (CS2A) module based on the cross-attention (CA) operation to better integrate spatial and spectral features. We evaluate the classification performance of the proposed CS2DT on three well-known datasets by conducting extensive experiments. Experimental results demonstrate that CS2DT can achieve higher accuracy and higher stability when compared with the state-of-the-art (SOTA) methods. The source code will be made available at https://***/shouhengx/CS2DT.

关键词： Hyperspectral images (HSIs) image classification neural network vision transformer (ViT)

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Disentangling Spatial and Temporal Learning for Efficient image-to-Video Transfer Learning

Disentangling Spatial and Temporal Learning for Efficient Im...

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International Conference on Computer Vision (ICCV)

作者： Zhiwu Qing Shiwei Zhang Ziyuan Huang Yingya Zhang Changxin Gao Deli Zhao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group ARC National University of Singapore

Recently, large-scale pre-trained language-image models like CLIP have shown extraordinary capabilities for understanding spatial contents, but naively transferring such models to video recognition still suffers from unsatisfactory temporal modeling capabilities. Existing methods insert tunable structures into or in parallel with the pre-trained model, which either requires back-propagation through the whole pre-trained model and is thus resource-demanding, or is limited by the temporal reasoning capability of the pre-trained structure. In this work, we present DiST, which disentangles the learning of spatial and temporal aspects of videos. Specifically, DiST uses a dual-encoder structure, where a pre-trained foundation model acts as the spatial encoder, and a lightweight network is introduced as the temporal encoder. An integration branch is inserted between the encoders to fuse spatio-temporal information. The disentangled spatial and temporal learning in DiST is highly efficient because it avoids the back-propagation of massive pre-trained parameters. Meanwhile, we empirically show that disentangled learning with an extra network for integration benefits both spatial and temporal understanding. Extensive experiments on five benchmarks show that DiST delivers better performance than existing state-of-the-art methods by convincing gaps. When pre-training on the large-scale Kinetics-710, we achieve 89.7% on Kinetics-400 with a frozen ViT-L model, which verifies the scalability of DiST. Codes and models can be found in https://***/alibaba-mmai-research/DiST.

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Point-Query Quadtree for Crowd Counting, Localization, and More

arXiv

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arXiv 2023年

作者： Liu, Chengxin Lu, Hao Cao, Zhiguo Liu, Tongliang Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China The University of Sydney Australia

We show that crowd counting can be viewed as a decomposable point querying process. This formulation enables arbitrary points as input and jointly reasons whether the points are crowd and where they locate. The querying processing, however, raises an underlying problem on the number of necessary querying points. Too few imply underestimation;too many increase computational overhead. To address this dilemma, we introduce a decomposable structure, i.e., the point-query quadtree, and propose a new counting model, termed Point quEry Transformer (PET). PET implements decomposable point querying via data-dependent quadtree splitting, where each querying point could split into four new points when necessary, thus enabling dynamic processing of sparse and dense regions. Such a querying process yields an intuitive, universal modeling of crowd as both the input and output are interpretable and steerable. We demonstrate the applications of PET on a number of crowd-related tasks, including fully-supervised crowd counting and localization, partial annotation learning, and point annotation refinement, and also report state-of-the-art performance. For the first time, we show that a single counting model can address multiple crowd-related tasks across different learning paradigms. Code is available at https://***/cxliu0/PET. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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A Robust Recognition and Segmentation Algorithm for Retaining Walls in Mining Scenes

A Robust Recognition and Segmentation Algorithm for Retainin...

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Chinese Automation Congress (CAC)

作者： Tian Zeng Li Xiao Gang Peng Aoze Wang Zhuo Wang Zhigang Sun Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Intelligence and Automation Huazhong University of Science and Technology Wuhan China

The key for bulldozers to realize automatic operation in mine scenes is whether they can accurately identify and accurately segment the retaining walls, however, because the point cloud dataset of the mine site is too few, resulting in the deep learning-based identification and segmentation algorithm cannot be applied in this scene. At the same time, because of the rugged road surface of the mine scene, the existence of a lot of dust, numerous disturbances, retaining wall features are not obvious and other problems, the algorithms proposed by previous scholars are not perfect enough to solve the problem. We propose a point cloud recognition and segmentation algorithm based on clustering and evaluation function of integrated features. Ours firstly compensates the skew of the point cloud map with RANSAC and down samples the data by gridding the point cloud. Then reduces the influence of dust and truck materials in the scene by normal vector and variance information. Finally, screens out the candidate target class by density clustering, and identifies and segments the retaining wall by integrated feature. Our proposed algorithm is validated in several different real mine scenarios and the results show that ours has high accuracy and strong robustness.

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