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Distributed Observer for Full-Measured Nonlinear systems Based on Knowledge of FMCF

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 69-85页

作者： Haotian Xu Shuai Liu Yueyang Li Ke Li the School of Control Science and Engineering Shandong University and also with Key Laboratory of Machine Intelligence and System Control Ministry of Education IEEE the School of Electrical Engineering University of Jinan

Driven by practical applications, the achievement of distributed observers for nonlinear systems has emerged as a crucial advancement in recent years. However, existing theoretical advancements face certain limitations: They either fail to address more complex nonlinear phenomena, rely on hard-to-verify assumptions, or encounter difficulties in solving system ***, this paper aims to address these challenges by investigating distributed observers for nonlinear systems through the full-measured canonical form(FMCF), which is inspired by full-measured system(FMS) theory. To begin with, this study addresses the fact that the FMCF can only be obtained through the observable canonical form(OCF) in existing FMS *** paper demonstrates that a class of nonlinear systems can directly obtain FMCF through state space equations, independent of OCF. Also, a general method for solving FMCF in such systems is provided. Furthermore, based on the FMCF, A distributed observer is developed for nonlinear systems under two scenarios: Lipschitz conditions and open-loop bounded *** paper establishes their asymptotic omniscience and demonstrates that the designed distributed observer in this study has fewer design parameters and is more convenient to construct than existing approaches. Finally, the effectiveness of the proposed methods is validated through simulation results on Van der Pol oscillators and microgrid systems.

关键词： Distributed observer full-actuated system full-measured system (FMS) nonlinear observer sensor networks

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NO_(2) gas sensor with high selectivity and fast response based on Pt-loaded nanoporous GaN

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International Journal of Minerals,Metallurgy and Materials 2025年第4期32卷 964-972页

作者： Dan Han Xiaoru Liu Donghui Li Jiexu Shi Yu Wang Yuxuan Wang Hongtao Wang Shengbo Sang Shanxi Key Laboratory of Micro/Nano Sensors&Artificial Intelligence Perception College of Integrated CircuitsTaiyuan University of TechnologyTaiyuan030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030032China

In this work,we realized a room-temperature nitrogen dioxide(NO_(2))gas sensor based on a platinum(Pt)-loaded nanoporous gallium nitride(NP-GaN)sensing material using the thermal reduction method and coreduction with the catalysis of *** gas sensor gained excellent sensitivity to NO_(2) at a concentration range of 200 ppm to 100 ppb,benefiting from the loading of Pt nanoparticles,and exhibited a short response time(22 s)and recovery time(170 s)to 100 ppm of NO_(2) at room temperature with excellent selectivity to NO_(2) compared with other *** phenomenon was attributed to the spillover effect and the synergic electronic interaction with semiconductor materials of Pt,which not only provided more electrons for the adsorption of NO_(2) molecules but also occupied effective sites,causing poor sites for other *** low detection limit of Pt/NP-GaN was 100 ppb,and the gas sensor still had a fast response 70 d after ***,the gas-sensing mechanism of the gas sensor was further elaborated to determine the reason leading to its improved *** significant spillover impact and oxygen dissociation of Pt provided advantages to its synergic electronic interaction with semiconductor materials,leading to the improvement of the gas properties of gas sensors.

关键词： nanoporous gallium nitride platinum nitrogen dioxide gas sensor

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PAPI-Reg: Patch-to-Pixel Solution for Efficient Cross-Modal Registration between LiDAR Point Cloud and Camera Image

arXiv

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arXiv 2025年

作者： Yue, Yuanchao Li, Zhengxin Zhang, Wei Yuan, Hui Key Laboratory of Machine Intelligence and System Control Ministry of Education School of Control Science and Engineering Shandong University China

The primary requirement for cross-modal data fusion is the precise alignment of data from different sensors. However, the calibration between LiDAR point clouds and camera images is typically time-consuming and needs external calibration board or specific environmental features. Cross-modal registration effectively solves this problem by aligning the data directly without requiring external calibration. However, due to the domain gap between the point cloud and the image, existing methods rarely achieve satisfactory registration accuracy while maintaining real-time performance. To address this issue, we propose a framework that projects point clouds into several 2D representations for matching with camera images, which not only leverages the geometric characteristic of LiDAR point clouds more effectively but also bridge the domain gap between the point cloud and image. Moreover, to tackle the challenges of cross modal differences and the limited overlap between LiDAR point clouds and images in the image matching task, we introduce a multi-scale feature extraction network to effectively extract features from both camera images and the projection maps of LiDAR point cloud. Additionally, we propose a patch-to-pixel matching network to provide more effective supervision and achieve higher accuracy. We validate the performance of our model through experiments on the KITTI and nuScenes datasets. Our network achieves real-time performance and extremely high registration accuracy. On the KITTI dataset, our model achieves a registration accuracy rate of over 99%. © 2025, CC BY.

关键词： Cameras

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Robust H∞ Analysis and control of Fractional-Order systems With Ellipsoidal Parametric Uncertainty

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Mathematical Methods in the Applied Sciences 2025年第8期48卷 9058-9070页

作者： Zhu, Long-Jun Lu, Jun-Guo Zhu, Zhen Zhang, Qing-Hao Department of Automation Shanghai Jiao Tong University Shanghai China School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

In the domain of systems control, the robust control of fractional-order systems (FOSs) with ellipsoidal parameter uncertainty has received scant attention, even though ellipsoidal uncertainty is significant for quantifying and modeling parameter uncertainties. This paper addresses the gap by focusing on (Formula presented.) analysis and control of FOSs with ellipsoidal parametric uncertainty, where the fractional order is delimited to the range (Formula presented.). Two distinct categories of ellipsoidal parameter uncertainty models are considered: one corresponding to FOSs with uncertain zero positions and the other to FOSs with uncertain pole and zero positions. First, sufficient conditions for the (Formula presented.) performance analysis of FOSs with ellipsoidal parametric uncertainty are established by extending the (Formula presented.) bounded real lemma for FOSs. Next, the problems of (Formula presented.) controller synthesis are analyzed, and the state feedback (Formula presented.) controllers are designed, along with the establishment of sufficient conditions for stabilization. These conditions are given in the form of linear matrix inequalities (LMIs). Lastly, several numerical examples are provided to verify the effectiveness of the proposed results. © 2025 John Wiley & Sons Ltd.

关键词： State feedback

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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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Binary-Encoding-Based Quantized Kalman Filter: An Approximate MMSE Approach

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IEEE Transactions on Automatic control 2025年第5期70卷 3181-3196页

作者： Liu, Qinyuan Nie, Yao Wang, Zidong Dong, Hongli Jiang, Changjun Tongji University School of Computer Science and Technology Shanghai201804 China Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Tongji University Shanghai Artificial Intelligence Laboratory Shanghai201804 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Northeast Petroleum University Artificial Intelligence Energy Research Institute Daqing163318 China Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Daqing163318 China

In this article, the Kalman filter design problem is investigated for linear discrete-time systems under binary encoding schemes. Under such a scheme, the local information is quantized into a bit string by the remote sensor based on a probabilistic quantizer, and then the bit string is transmitted via memoryless binary symmetric channels (BSCs). Due to the communication link noises, the bit flipping occurs in a random manner, and thus, the transmission of the bit string would suffer from specific bit-error rates. With the received bits, a recursive binary-encoding-based quantized Kalman filter is established in the approximate minimum mean-square error (MMSE) sense, which relies on the Gaussian approximation of the conditional probability density function at each iteration. Furthermore, the proposed estimator is shown to be of a Kalman-like type through performance analysis, which exhibits computational complexity comparable to the conventional Kalman filter. Subsequently, a posterior Cramér-Rao lower bound is derived for the proposed binary-encoding-based quantized Kalman filter. The effectiveness of the proposed estimator is demonstrated through numerical results. © 1963-2012 IEEE.

关键词： Mean square error

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C2P-Net: Comprehensive Depth Map to Planar Depth Conversion for Room Layout Estimation

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IEEE Transactions on Pattern Analysis and machine intelligence 2025年 PP卷 PP页

作者： Zhang, Weidong Zhou, Mengjie Cheng, Jiyu Liu, Ying Zhang, Wei Xi'an University of Posts & Telecommunications School of Communications and Information Engineering China Xi'an Key Laboratory of Image Processing Technology and Applications for Public Security China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China

Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requires the reconstruction of these planes. Some studies reconstruct indoor planes from perspective images by learning pixel-level or instance-level plane parameters. However, directly learning these parameters has the problems of susceptibility to occlusions and position dependency. In this paper, we introduce the Comprehensive depth map to Planar depth (C2P) conversion, which reformulates planar depth reconstruction into the prediction of a comprehensive depth map and planar visibility confidence. Based on the parametric representation of planar depth we propose, the C2P conversion is applicable to both panoramic and perspective images. Accordingly, we present an effective framework for room layout estimation that jointly learns the comprehensive depth map and planar visibility confidence. Due to the differentiability of the C2P conversion, our network autonomously learns planar visibility confidence by constraining the estimated plane parameters and reconstructed planar depth map. We further propose a novel approach for 3D layout generation through sequential planar depth map integration. Experimental results demonstrate the superiority of our method across all evaluated panoramic and perspective datasets. © 1979-2012 IEEE.

关键词： Image-Text Matching Medical Report Generation Sample-graph Consistency Self-boosting framework

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An online self-organizing radial basis function neural network based on Gaussian Membership

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Applied intelligence 2025年第6期55卷 1-17页

作者： Jia, Lijie Li, Wenjing Qiao, Junfei Zhang, Xinliang School of Electrical Engineering and Automation Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment Henan Polytechnic University Jiaozuo454003 China Faculty of Information Technology Beijing University of Technology No. 100 Pingleyuan Chaoyang District Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System No. 100 Pingleyuan Chaoyang District Beijing100124 China

Radial basis function neural network (RBFNN) is one of the most popular neural networks, and an appropriate selection of its structure and learning algorithms is crucial for its performance. Aiming to alleviate the sensitivity of the RBFNN to its parameters and improve the overall performance of the network, this study proposes a Gaussian Membership-based online self-organizing RBF neural network (GM-OSRBFNN). First, the Gaussian Membership is introduced to enhance network insensitivity to network parameters and used as a similarity metric to indicate the similarity between the sample to a hidden neuron and that between hidden neurons. Second, the similarity metric is used to design the neuron addition and merging rules to achieve a self-organizing network structure, and error constraints are introduced to the neuron addition rule;also, the noisy neuron deletion rule is defined to make the network structure more compact. In addition, an online fixed mini-batch gradient algorithm is used for online learning of network parameters, which can guarantee fast and stable convergence of the network. Finally, the proposed GM-OSRBFNN is tested on common nonlinear system modeling problems to verify its effectiveness. The experimental results show that compared to the existing models, the GM-OSRBFNN can achieve competitive prediction performance with a more compact network structure, faster convergence speed, and, more importantly, better insensitivity to network parameters. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Radial basis function networks

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Concept-Level Semantic Transfer and Context-Level Distribution Modeling for Few-Shot Segmentation

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Luo, Yuxuan Chen, Jinpeng Cong, Runmin Ip, Horace Ho Shing Kwong, Sam City University of Hong Kong Department of Computer Science Hong Kong Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Lingnan University Hong Kong

Few-shot segmentation (FSS) methods aim to segment objects using only a few pixel-level annotated samples. Current approaches either derive a generalized class representation from support samples to guide the segmentation of query samples, which often discards crucial spatial contextual information, or rely heavily on spatial affinity between support and query samples, without adequately summarizing and utilizing the core information of the target class. Consequently, the former struggles with fine detail accuracy, while the latter tends to produce errors in overall localization. To address these issues, we propose a novel FSS framework, CCFormer, which balances the transmission of core semantic concepts with the modeling of spatial context, improving both macro and micro-level segmentation accuracy. Our approach introduces three key modules: (1) the Concept Perception Generation (CPG) module, which leverages pre-trained category perception capabilities to capture high-quality core representations of the target class;(2) the Concept-Feature Integration (CFI) module, which injects the core class information into both support and query features during feature extraction;and (3) the Contextual Distribution Mining (CDM) module, which utilizes a Brownian Distance Covariance matrix to model the spatial-channel distribution between support and query samples, preserving the fine-grained integrity of the target. Experimental results on the PASCAL-5i and COCO-20i datasets demonstrate that CCFormer achieves state-of-the-art performance, with visualizations further validating its effectiveness. Our code is available at ***/lourise/ccformer. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Surface-PMSM Sensorless control Strategy Based on Reduced-Order Linear Kalman Filter Cooperating with Prediction Error Rolling Compensation

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IEEE Transactions on Power Electronics 2025年

作者： Xu, Sang Shen, Anwen Tang, Qipeng Wang, Miaomiao Luo, Pan Luo, Xin Xu, Jinbang National Key Laboratory of Science and Technology on Multispectral Information Processing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

Kalman filter (KF) is increasingly attracted for sensorless control of surface permanent magnet synchronous motors (SPMSMs) due to its strong robustness against measurement and system noise. However, conventional method suffers from poor position and speed estimation accuracy under dynamic conditions, and high computational cost. In order to solve these problems, an enhanced sensorless control strategy based on reduced-order linear Kalman filter (RLKF) cooperating with prediction error rolling compensation is introduced in this paper. Differing from conventional method, a reduced-order linear state equation encompassing rotor position, speed and torque disturbance is constructed. Then, by obtaining the prediction error of system output using the motor current equation in the predictive reference frame, the optimal estimation of rotor position and speed in KF algorithm is achieved based on the reduced-order model. This not only simplifies the calculation process, but also enhances the position and speed estimation accuracy under dynamic conditions. Meanwhile, a prediction error rolling compensation algorithm is developed to minimize the estimation deviations caused by motor parameters variations. Additionally, the system's dynamic performance is further improved with an adopted speed controller based on torque disturbance feedforward and acceleration feedback. Finally, experimental results verify the effectiveness and feasibility of the proposed strategy.1 © 1986-2012 IEEE.

关键词： Permanent magnets

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