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A Novel Optimal Distributed Nonlinear Filter for Simultaneous State and Unknown Input Estimation in Multi-Sensor Networks

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IEEE Signal processing Letters 2025年 32卷 1745-1749页

作者： Ding, Bo Wei, Yuanchu Zhang, Enze Fang, Huajing Yangzhou University College of Information Engineering Yangzhou225127 China Huazhong University of Science and Technology School of Automation Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China

This letter investigates the problem of simultaneous state and unknown input estimation in a nonlinear system within multi-sensor networks. To avoid the linearization errors caused by existing methods, such as statistical linear regression techniques and first-order Taylor approximation, a novel optimal distributed nonlinear filter is proposed based on the unscented transformation and unbiased minimum variance criterion. First, the unscented transformation is applied to generate predicted estimates and the covariance matrix. Second, input and state estimation are carried out in an unbiased minimum variance manner using measurements from the nonlinear system. Third, a distributed strategy leveraging average consensus is employed to incorporate local estimates from neighboring sensors. Finally, simulation results confirm that the proposed method achieves substantial improvements in estimation accuracy compared to existing methods. © 1994-2012 IEEE.

关键词： Covariance matrix

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Finite-Time Consensus of Second-Order Multiagent Systems With Input Saturation via Hybrid Sliding-Mode control

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 14623-14632页

作者： Jiang, Xiaowei Jiao, Ranran Li, Bo Zhang, Xianhe Yan, Huaicheng China University of Geosciences School of Automation Wuhan430079 China Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Wuhan430074 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Anhui University of Finance and Economics School of Finance Bengbu233030 China Hubei Normal University School of Electrical Engineering and Automation Huangshi435002 China Ministry of Education East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China

This paper addresses the finite-time consensus (FTC) issue for second-order multi-agent systems (MASs) with nonlinear disturbances. To tackle the challenges posed by increasingly complex communication environments, an innovative integral sliding-mode surface is designed using event-triggered control. Furthermore, the study explores communication constraints between the leader and followers, employing impulsive control to facilitate communication only at specific intervals. A novel hybrid integral sliding-mode control protocol is advanced for the second-order MASs, which effectively eliminates the "Zeno phenomenon" and notably reduces both communication frequency and energy consumption, thereby enhancing overall communication efficiency. Ultimately, the efficacy of the put forward protocol is verified through simulation examples including comparison experiment. © 2004-2012 IEEE.

关键词： Protocols Sliding mode control Event detection Multi-agent systems Costs Training Topology Switches Sufficient conditions Stability analysis

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Curve-Suppression-Based Event-Triggered Mechanisms for Quasi-Synchronization of Fuzzy Delayed Neural Networks on Time Scales

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3174-3187页

作者： Wan, Peng Zhou, Yufeng Zeng, Zhigang Lai, Jingang Wuhan University of Science and Technology School of Artificial Intelligence and Automation Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent control of Education Ministry of China Wuhan430074 China

The vast majority of published event-triggered mechanisms (ETMs) are constructed based on measurement errors, which introduces a problem naturally that they are updated when the measurement errors exceed the thresholds although the current obtained sampling states can make systems converge well. With this problem in mind, we redesign ETMs for quasi-synchronization of T-S fuzzy neural networks (FNNs) with time delays on time scales. First, a novel ETM is designed for continuous-time FNNs with time-varying delays to achieve quasi-synchronization, with which synchronization errors is suppressed to globally exponentially converge to a ball. Second, we introduce the ETM for continuous-time FNNs to discrete-time FNNs, owing to the existence of discrete-time states, the Lypunov function of synchronization errors run over the exponentially decay curve, but it can be suppressed to evolve under another exponentially decay curve. Third, for FNNs on time scales with constant and time-varying delays, we estimate the forward jump operator of the Lyapunov functions and design ETMs to guarantee that the Lypunov functions evolve under the exponentially decay curves, so quasi-synchronization can be achieved. Last but not least, we prove that Zeno behavior will not happen and four numerical examples are introduced to verify the validity and the superiority of the proposed ETMs in reducing information transmission. © 2013 IEEE.

关键词： Continuous time systems

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Concentric Circles Nested Array Design Method for Acoustic Imaging Based on Differential Co-Array Model

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IEEE Sensors Journal 2025年第10期25卷 18106-18114页

作者： Xie, Zhiyuan Yang, Yan Zhang, Junyan Zhu, Wenzhao Bai, Zonglong North China Electric Power University Department of Electrical and Communication Engineering Baoding071003 China Hebei Provincial Key Laboratory of Power Internet of Things Baoding071003 China Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing Education Ministry of China Qinhuangdao066004 China

Acoustic imaging intuitively illustrates the locations of sound sources and has a wide range of applications, including fault detection. However, this technology requires a substantial number of microphones, which increases the complexity and cost of the data acquisition system. To reduce the number of microphones while maintaining the performance of acoustic imaging, this paper introduces a concentric circular nested array (CCNA) design method. By extending the array using a differential co-array model, a virtual uniform concentric circular array is achieved. This virtual array preserves the advantages of the circular array structure while minimizing the number of required microphones. The performance of the proposed array is validated through simulation experiments. The simulation results demonstrate that the CCNA offers higher resolution, a greater dynamic range (DR), and improved positioning accuracy in acoustic imaging compared to other arrays. Finally, this paper compares the acoustic imaging results of the CCNA with those of the uniform circular array (UCA) through experiments. The experimental results reveal that the positioning accuracy of the proposed CCNA surpasses that of the UCA, thereby confirming the superiority of the proposed design. © 2001-2012 IEEE.

关键词： Acoustic imaging

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Complementary Learning Subnetworks Towards Parameter-Efficient Class-Incremental Learning

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3240-3252页

作者： Li, Depeng Zeng, Zhigang Dai, Wei Suganthan, Ponnuthurai Nagaratnam Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Qatar University Kindi Center for Computing Research College of Engineering Doha2713 Qatar

In the scenario of class-incremental learning (CIL), deep neural networks have to adapt their model parameters to non-stationary data distributions, e.g., the emergence of new classes over time. To mitigate the catastrophic forgetting phenomenon, typical CIL methods either cumulatively store exemplars of old classes for retraining model parameters from scratch or progressively expand model size as new classes arrive, which, however, compromises their practical value due to little attention paid to parameter efficiency. In this paper, we contribute a novel solution, effective control of the parameters of a well-trained model, by the synergy between two complementary learning subnetworks. Specifically, we integrate one plastic feature extractor and one analytical feed-forward classifier into a unified framework amenable to streaming data. In each CIL session, it achieves non-overwritten parameter updates in a cost-effective manner, neither revisiting old task data nor extending previously learned networks;Instead, it accommodates new tasks by attaching a tiny set of declarative parameters to its backbone, in which only one matrix per task or one vector per class is kept for knowledge retention. Experimental results on a variety of task sequences demonstrate that our method achieves competitive results against state-of-the-art CIL approaches, especially in accuracy gain, knowledge transfer, training efficiency, and task-order robustness. Furthermore, a graceful forgetting implementation on previously learned trivial tasks is empirically investigated to make its non-growing backbone (i.e., a model with limited network capacity) suffice to train on more incoming tasks. © 2025 IEEE.

关键词： Contrastive Learning

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Enhancing Network Abnormal Detection With NMF-SECNN: Leveraging Deep Feature Learning for High-Precision Traffic Analysis

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IEEE Transactions on Network Science and Engineering 2025年第3期12卷 2069-2080页

作者： Yuan, Yazhou Yu, Ning Zheng, Zhuolin Yang, Yong Ma, Kai Liu, Zhixin Chen, Cailian Zhang, Jianmin Yanshan University Institute of Electrical Engineering Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing of the Ministry of Education of China Qinhuangdao066004 China State Grid Handan Electric Power Supply Company Hebei 056000 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Baosteel Research Institute Automation Department Shanghai200240 China

Detection of abnormalities in industrial network traffic plays a crucial role in maintaining network system security. However, current abnormal detection models suffer from low precision, and extracting deep-level feature information from industrial network traffic is difficult. This leads to the loss of partial feature information during the detection process, thereby affecting detection efficiency. To address this issue, this paper proposes an abnormal traffic detection framework for industrial networks. By employing a Non-negative Matrix Factorization (NMF)-based method for extracting abnormal traffic features and optimizing the NMF decomposition process through constructing label consistency constraints, we facilitate effective feature extraction. Additionally, the Squeeze-and-Excitation attention mechanism is introduced into a Convolutional Neural Network (CNN) to construct a classifier that enhances detection precision without increasing complexity, enabling efficient identification of complex network traffic patterns. This results in the NMF-Squeeze-and-Excitation-CNN (NMF-SECNN) model, which combines effective feature extraction capability with a lightweight structural design, achieving superior detection performance in industrial network environments. The proposed method achieves a detection accuracy of 99.4%, representing a 5.6% improvement over baseline methods, and a recall rate of 98.2%, showcasing the model's capability to identify abnormalities across diverse scenarios. Various classification metrics confirm the model's robustness and effectiveness, demonstrating its significant advantages over traditional methods. © 2013 IEEE.

关键词： Non-negative matrix factorization

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Formation control and path planning of multi-robot systems via large language models

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Science china(Information Sciences) 2025年第5期68卷 72-88页

作者： Dong XUE Xuanjie ZHOU Ming WANG Fangzhou LIU Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology Research Institute of Intelligent Control and Systems School of Astronautics Harbin Institute of Technology

Existing path planning and coordination control methods for multi-robot systems(MRS) typically rely on predefined rules and rudimentary algorithms. However, these methods often struggle to adapt flexibly to complex environments and to adjust motion targets appropriately. To address this challenge, this study presents a large language model(LLM)-assisted framework. By integrating textual descriptions of complex motion constraints, robot information, and local environmental data as inputs, LLMs generate motion objectives and translate them into executable control commands for the robots, thereby achieving coordinated control and path planning. This framework facilitates the generation, maintenance, and reshaping of formations in MRSs during path planning, applicable to both obstacle-free and obstacle-avoidance environments. Simulation results demonstrate that LLM-based control strategies enhance the autonomy, adaptability, flexibility, and robustness of MRS by processing complex information, making intelligent decisions, adapting to environmental changes, and handling disturbances and uncertainties.

关键词： multi-robot systems formation control path planning obstacle avoidance large language models

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DFIMat: Decoupled Flexible Interactive Matting in Multi-person Scenarios 17th

DFIMat: Decoupled Flexible Interactive Matting in Multi-per...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Jiao, Siyi Zeng, Wenzheng Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819609710

Interactive portrait matting refers to extracting the soft portrait from a given image that best meets the user’s intent through their inputs. Existing methods often underperform in complex scenarios, mainly due to three factors. (1) Most works apply a tightly coupled network that directly predicts matting results, lacking interpretability and resulting in inadequate modeling. (2) Existing works are limited to a single type of user input, which is ineffective for intention understanding and also inefficient for user operation. (3) The multi-round characteristics have been under-explored, which is crucial for user interaction. To alleviate these limitations, we propose DFIMat, a decoupled framework that enables flexible interactive matting. Specifically, we first decouple the task into 2 sub-ones: localizing target instances by understanding scene semantics and the flexible user inputs, and conducting refinement for instance-level matting. We observe a clear performance gain from decoupling, as it makes sub-tasks easier to learn, and the flexible multi-type input further enhances both effectiveness and efficiency. DFIMat also considers the multi-round interaction property, where a contrastive reasoning module is designed to enhance cross-round refinement. Another limitation for multi-person matting task is the lack of training data. We address this by introducing a new synthetic data generation pipeline that can generate much more realistic samples than previous arts. A new large-scale dataset SMPMat is subsequently established. Experiments verify the significant superiority of DFIMat. With it, we also investigate the roles of different input types, providing valuable principles for users. Our code and dataset will be public. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Large datasets

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Extended State Observer-based Hybrid Dynamic Event-Triggered Consensus control with External Disturbances 24

Extended State Observer-based Hybrid Dynamic Event-Triggered...

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2nd International Conference on Frontiers of intelligent Manufacturing and Automation, CFIMA 2024

作者： Pei, Wenliang Hua, Changchun Cui, Hailong Zhao, Guanglei Ma, Zhuang The Institute of Electrical Engineering The Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Yanshan University Hebei China Tangshan University Hebei China

ISBN: (纸本)9798400710681

This paper solves the event-triggered robust consensus problem of disturbed multi-agent systems (MASs) in fully distributed fashion. First, a distributed extended state observer (DESO) is developed for appropriately estimating system states and external disturbances. Then, by utilising these estimates, a fully distributed event-triggering control protocol, that composes of an adaptive distributed controller to avoid using global information while maintaining desired consensus property, and a novel hybrid dynamic event-triggering mechanism (ETM) to save communication resources, is put forward for the MASs. It is notable that an auxiliary differential equation is incorporated in the triggering condition, with which, positive minimum interevent times (MIETs) are available to ease the hardware implementation, while being with adaptive coefficients, the proposed hybrid dynamic ETM can be independent of any global information. Finally, the obtained main results are testified via a numerical example. © 2024 Copyright held by the owner/author(s).

关键词： State estimation

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Attitude Stabilization control for Unmanned Aerial Vehicle with Robotic Arm Based on Fixed-Time Observer Active Disturbance Rejection control 3rd

Attitude Stabilization Control for Unmanned Aerial Vehicle w...

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3rd International Conference on Machine Learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Mo, Fan Gu, Chaochen Wang, Kunkun Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9789819616930

To address the periodic disturbances introduced by the manipulator mounted on the drone, as well as the overall system parameter variations caused by the robotic arm carried by the Unmanned Aerial Vehicle (UAV), the fixed-time convergence observer (FTCO) is introduced to ADRC to enhance the dynamic characteristics, including overshoot and convergence speed. The Active Disturbance Rejection controller (ADRC) is employed as the fundamental controller for UAV systems while the FTCO is used to rapidly observe disturbances and compensate them to the controller, aiming to counteract interference. The UAV system is established to verify the effect of Fixed-Time Convergence Observer-Active Disturbance Rejection controller (FTCO-ADRC) when it is compared to conventional ADRC and PID controller. The simulation outcome reveals that the FTCO-ADRC perform better in convergence speed, overshoot and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Disturbance rejection

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