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A Degeneracy-Aware LiDAR-Inertial-Visual SLAM for Degenerate Environments

IEEE Sensors Journal 2025年第11期25卷 19376-19385页

作者： Zhu, Hongwei Zhang, Guobao Huang, Yongming Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

Different sensors may experience degeneracy in specific scenarios, which can lead to failures in multi-sensor fusion optimization. To address the challenges of localization and mapping under such degeneracy conditions, this paper proposes a LiDAR-Inertial-Visual SLAM system with integrated degeneracy awareness. In the frontend, the system utilizes the ESIKF for tracking and incorporates a sensor degeneracy detection method to ensure that degraded sensor data do not affect the state error update. In the backend, we replace the conventional point cloud map with an adaptive voxel map to accelerate the retrieval of planar point cloud, and merge LiDAR planar features within a sliding window via region growing algorithm. Furthermore, the point-to-plane distance formula is optimized via the Rayleigh quotient theorem, which reduces the dimensionality of the optimization variables and simplifies the optimization process. Finally, we evaluate the proposed system on several challenging datasets and compare it with various state-of-the-art sensor fusion systems. The experimental results demonstrate that the proposed system achieves high localization accuracy in the majority of challenging scenarios. © 2001-2012 IEEE.

关键词： Inertial navigation systems

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Sensorless control of PMSM Drives Using Octant Newton-Raphson Method and Reduced-Order EKF Considering Speed Reversal and Noise

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IEEE Transactions on Power Electronics 2025年第8期40卷 10793-10803页

作者： Xiang, Fuyuan Yu, Ke Chen, Congyan Li, Shihua Southeast University School of Automation and the Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

For back electromotive force-based sensorless control in permanent magnet synchronous motor (PMSM) drives, the conventional phase-locked loop (PLL) exhibits a ±π position estimation error during speed reversal. This paper presents an octant Newton-Raphson method (ONRM) based PLL, which employs a newly defined objective function and a position octant method. It not only ensures the position estimation converges correctly during speed reversal but also improves dynamic performance compared to the conventional PLL. The applicability condition of the ONRM in the presence of noise is also analyzed in detail. Subsequently, due to the noise sensitivity of the ONRM, a reduced-order extended Kalman filter is designed to filter out the non-Gaussian noise in the position estimation while simultaneously estimating the speed. The effectiveness of the proposed sensorless control scheme is verified on a 1.5 kW PMSM drive platform. © 1986-2012 IEEE.

关键词： Extended Kalman filters

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BeltLineNet: A Shape-Prior-Guided Lightweight Network for Real-Time Deviation Detection in Circular Pipe Conveyors

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IEEE Sensors Journal 2025年第11期25卷 20024-20036页

作者： Zhao, Long Su, Jinhui Zhong, Yusheng Xie, Weiwei Su, Jinya Chen, Xisong Chen, Congyan Li, Shihua Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Fujian Longking Intelligent Conveying Engineering Co. Ltd Longyan China

Belt deviation in circular pipe conveyor systems could lead to material spillage, environmental contamination, reduced efficiency, and accelerated belt wear. Real-time belt deviation detection is crucial for ensuring safe and efficient operation. However, existing methods, primarily developed for flat-belt systems, heavily rely on distinct belt-background semantics, making them unsuitable for circular conveyors where indistinct target features, motion blur, and occlusions pose significant challenges. To address these issues, we reformulate the deviation detection task as an object detection problem, proposing BeltLineNet, a lightweight, real-time deviation detection network specifically designed for circular pipe conveyors. Our method integrates explicit feature learning with a coarse-to-fine global feature fusion mechanism, enhancing belt line representation under complex conditions. Additionally, a shape-prior loss strategy is introduced to improve supervision during training, ensuring more accurate detection of elongated targets. The model is also optimized for real-time deployment through layer-adaptive pruning, achieving a balance between accuracy and computational efficiency. Extensive comparative experiments involving 8 state-of-the-art (SOTA) feature extraction networks and 6 feature enhancement strategies on a self-collected dataset under industrial conditions demonstrate that BeltLineNet surpasses SOTA feature extraction and fusion networks, improving average precision (AP) by 8.23% before pruning and 6.33% after pruning. © 2001-2012 IEEE.

关键词： Belt conveyors

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Energy-based Swing-up control of the Soft Inverted Pendulum with Affine Curvature

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Nonlinear Dynamics 2025年 1-20页

作者： Wang, Yongjia Xin, Xin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Institute of Intelligent Unmanned Systems Southeast University Nanjing210096 China

This paper studies the energy-based swing-up control of an underactuated soft inverted pendulum, a template model of soft robots with its base attached to the ground and its curvature described by an affine function. First, this paper presents a necessary and sufficient condition for the linear controllability of the robot around its upright equilibrium point (UEP). Next, without any constraint on its stiffness, this paper derives an energy-based controller to swing up the robot to its UEP, where the pendulum is in its upright position, and provides a necessary and sufficient condition such that the controller is free of singular points. Then, this paper characterizes the patterns of closed-loop equilibrium points with respect to the physical parameters of the pendulum and the gains of the derived controller, and analyzes the closed-loop motion corresponding to the special case in which desired convergence of the total mechanical energy is achieved. In such case, this paper reveals that the UEP is generically the unique closed-loop equilibrium point, and provides clarification regarding its stability. These results serve as guidelines of the design of soft robots and the tuning of control gains to achieve the desired performance of the swing-up control. Finally, this paper conducts numerical simulations to validate the theoretical results and to demonstrate the effectiveness and performance of the derived controller in swinging up the robot to its UEP from initial states far from the UEP, even under the challenging condition of very low stiffness. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Affine transforms

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Stability and Stabilization of Sampled-Data Based LFC for Power systems:A Data-Driven Method

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 291-293页

作者： Yu-Long Fan Chuan-Ke Zhang Yong He the School of Automation China University of Geosciences the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education IEEE

Dear Editor,This letter is concerned with stability analysis and stabilization design for sampled-data based load frequency control(LFC) systems via a data-driven method. By describing the dynamic behavior of LFC systems based on a data-based representation, a stability criterion is derived to obtain the admissible maximum sampling interval(MSI) for a given controller and a design condition of the PI-type controller is further developed to meet the required MSI. Finally, the effectiveness of the proposed methods is verified by a case study.

关键词： Sampled data control systems

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Parallel Seeds:From Foundation Models to Foundation Intelligence for Agricultural Sustainability

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 481-484页

作者： Laiyi Fu Shunkang Ling Danyang Wu Mengzhen Kang Fei-Yue Wang Hequan Sun the Shool of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong University the College of Information Engineering Northwest A&F University IEEE the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences

THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the development of sustainable agriculture, where a fundamental step is crop breeding to improve agronomic or economic traits, e.g., increasing yields of crops while decreasing resource usage and minimizing pollution to the environment [2].

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Recent advancements of nonlinear dynamics in mode coupled microresonators:a review

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Applied Mathematics and Mechanics(English Edition) 2025年第2期46卷 209-232页

作者： Xuefeng WANG Zhan SHI Qiqi YANG Yuzhi CHEN Xueyong WEI Ronghua HUAN Department of Engineering Mechanics MIIT Key Laboratory of Dynamics and Control of Complex SystemsNorthwestern Polytechnical UniversityXi’an 710129China Department of Mechanics Key Laboratory of Soft Machines and Smart Devices of Zhejiang ProvinceZhejiang UniversityHangzhou 310027China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710054China

Due to scale effects,micromechanical resonators offer an excellent platform for investigating the intrinsic mechanisms of nonlinear dynamical phenomena and their potential *** review focuses on mode-coupled micromechanical resonators,highlighting the latest advancements in four key areas:internal resonance,synchronization,frequency combs,and mode *** origin,development,and potential applications of each of these dynamic phenomena within mode-coupled micromechanical systems are investigated,with the goal of inspiring new ideas and directions for researchers in this field.

关键词： mode coupling micro-electro-mechanical system(MEMS)resonator nonlinear dynamics

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Transformer-Based Scenario Generation of Multi-segment Monotonic Increasing Bidding Curves of Power Plants

Transformer-Based Scenario Generation of Multi-segment Monot...

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International Conference of Electrical, Electronic and Networked Energy systems, EENES 2024

作者： Xie, Haochuan Lv, Jianhu Wang, Ying Yuan, Kun College of Software Southeast University Suzhou China China Electric Power Research Institute Beijing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9789819618637

With the relaxation of electricity market regulation, power plants are permitted to engage in bidding. The bidding outcomes in the competitive market differ significantly from those in the previously regulated market. These differences in bidding outcomes will further impact power flow and system security. To provide a reference for practical electricity market operations, make market simulation more realistic, it is crucial to analyze and establish a model to simulate the bidding behavior of power generators. This paper applies Transformer networks to predict multi-segment bidding behaviors of power plants, we have improved the classic Transformer network by adopting a one encoder-two decoder format, considering six key factors influencing bidding behavior: unit ID, fuel type, capacity, location, system load, and load ratio. Additionally, monotonicity of the multi-segment bidding curves of power plants is taken into account, and monotonicity loss functions are introduced to achieve this goal. We apply this method to predict bidding behaviors in power plants in the Australian electricity market, validating the accuracy of the model. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： bidding curves generation monotonicity constraint power market Transformer-based

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Fixed-Time Adaptive Tracking Event-Based control for Uncertain Stochastic Nonlinear systems With Output Constraints

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International Journal of Robust and Nonlinear control 2025年第0期

作者： Wang, Kun Xie, Liping Yuan, Yixuan Zhang, Kanjian Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

This paper focuses on an adaptive fixed-time tracking control problem for nonstrict-feedback stochastic nonlinear systems with unknown control coefficients and output constraints. To overcome this problem, this article introduces a unified system transformation function to facilitate the transition of the system from constrained to unconstrained states. Subsequently, a fuzzy approximation principle is utilized for handling unknown terms in the transformed system, while Nussbaum functions are employed to manage unknown control directions. Then, combined with the backstepping technique, an adaptive fixed-time tracking controller is designed with an event-triggered mechanism, which not only achieves the convergence of tracking errors within a limited time independent of initial states, but also conserves resources. Under the proposed tracking controller, practical fixed-time stability can be ensured for the closed-loop system. Finally, two simulation examples demonstrate the efficacy of the proposed scheme. © 2025 John Wiley & Sons Ltd.

关键词： Stochastic systems

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Attention-Modulated Feature Fusion Neural Network for Inverse Modeling of Microwave Filters

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IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 376-379页

作者： Guo, Linwei Cao, Weihua Hu, Wenkai Lu, Zhengyang Wu, Min China University of Geosciences School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Inverse modeling is extensively applied in the design and tuning of microwave filters (MFs). Inverse models (IMs) take the features extracted from the high-dimensional electromagnetic parameters as input. How to make full use of features from multiple perspective is a critical issue for improving model accuracy. To solve it, this letter proposes an attention-modulated feature fusion neural network (AMFFNN). AMFFNN achieves multiperspective feature fusion (MPFF) at the input side and independent feature fusion (IFF) at the output side. In addition, feature fusion in AMFFNN is enhanced by attention modules to dynamically identify the importance of each feature. Statistical and comparative results of simulations demonstrate that AMFFNN outperforms existing methods in terms of accuracy, stability, and generalization. © 2023 IEEE.

关键词： Inverse problems

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