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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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Semi-Supervised Learning for Low-Light Image Enhancement by Pseudo Low-Light Image

Semi-Supervised Learning for Low-Light Image Enhancement by ...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Shuo Xie Yingjie Ma Wei Xu Song Qiu Yifei Sun School of Communication and Electronic Engineering East China Normal University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai

Building paired datasets in low-light enhancement entails significant cost and time, making such datasets precious commodities. Many researchers focus on how to enable models to learn more information from limited datasets. A prevalent strategy involves employing semi-supervised learning techniques to enhance model performance through additional unpaired images. However, one of the main challenges faced is the scarcity of a vast number of unpaired images from the same domain as the original low-light images. Consequently, we introduce a semi-supervised image enhancement method using pseudo-low-light images. Initially, we generate pseudo low-light images with less noise compared with the source domain image by the Signal-to-Noise Ratio prior and diffusion models. We then employ the Mean-Teacher network and the feature constraints of the pseudo-low-light images to realize low-light image enhancement. Comprehensive experimental results validate the efficacy of our approach on real-world datasets.

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive control For Tractor-trailer Trajectory Tracking With Unknown Parameters

K-BMPC: Derivative-based Koopman Bilinear Model Predictive C...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zehao Wang Han Zhang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC.

关键词： Trajectory tracking Computational modeling Estimation Predictive models control systems Data models Nonlinear dynamical systems

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New Impact-Time-control Guidance Law for Maneuvering Targets with Field-of-View Constraint

New Impact-Time-Control Guidance Law for Maneuvering Targets...

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第42届中国控制会议

作者： Bin Yuru Wang Hui Guo Kaiyang Dai Xin Yang Haorui School of Aerospace Engineering Beijing Institute of Technology Beijing Key Laboratory of UAV Autonomous Control Beijing Institute of Technology Beijing Institute Of Electronic System Engineering

In this paper,a novel guidance law aimed at intercepting targets of various motion forms with impact time and additional field-of-view(FOV) constraints is *** polynomial function in range domain is designed to derive the guidance command in new-defined virtual relative frame,and the function of seeker's virtual FOV is introduced in the form of additional feedback term to achieve simultaneous constraints on impact time and *** with existing methods based on numerical routines with open-loop formulations,the proposed guidance method has the potential to gain better performance in practical applications due to its analytical *** availability and practicability of the guidance method are demonstrated by simulations in various combat scenarios.

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Physics-informed neural networks for parameter learning of wildfire spreading

arXiv

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arXiv 2024年

作者： Vogiatzoglou, Konstantinos Papadimitriou, C. Bontozoglou, V. Ampountolas, K. System Dynamics Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece Transport Processes & Process Equipment Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece Automatic Control & Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop datadriven and physics-informed digital twins for wildfire prevention, monitoring, intervention, and response. In this direction of research, this work introduces a physics-informed neural network (PiNN) designed to learn the unknown parameters of an interpretable wildfire spreading model. The considered modeling approach integrates fundamental physical laws articulated by key model parameters essential for capturing the complex behavior of wildfires. The proposed machine learning framework leverages the theory of artificial neural networks with the physical constraints governing wildfire dynamics, including the first principles of mass and energy conservation. Training of the PiNN for physics-informed parameter identification is realized using synthetic data on the spatiotemporal evolution of one- and two-dimensional firefronts, derived from a high-fidelity simulator, as well as empirical data (ground surface thermal images) from the Troy Fire that occurred on June 19, 2002, in California. The parameter learning results demonstrate the predictive ability of the proposed PiNN in uncovering the unknown coefficients of the wildfire model in one- and two-dimensional fire spreading scenarios as well as the Troy Fire. Additionally, this methodology exhibits robustness by identifying the same parameters even in the presence of noisy data. By integrating this PiNN approach into a comprehensive framework, the envisioned physics-informed digital twin will enhance intelligent wildfire management and risk assessment, providing a powerful tool for proactive and reactive strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Optimization of Tiered Pricing Strategy for Smart Grid Considering Clean Energy

Optimization of Tiered Pricing Strategy for Smart Grid Consi...

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Chinese Automation Congress (CAC)

作者： Xiaocheng Wang ZeLong Li Pengjiao Sun Qiaoni Han Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Ministry of Education College of Electronic and Communication Engineering Tianjin Normal University Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin Normal University Key Laboratory of System Control and Information Processing Ministry of Education School of Electrical and Information Engineering Tianjin University Shanghai Jiao Tong University

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

With the rapid development of new energy technology, it has become a trend to combine wind and solar power generation into the smart grid. In this paper, wind power generation and photovoltaic power generation are introduced into the smart grid, and taking into account the consumption capacity of consumers at different levels, a tiered pricing scheme considering the uncertainty of wind and solar energy is proposed. Firstly, we establish probability density functions of power generation for wind power generation and photovoltaic power generation respectively to express the uncertainty of wind energy and solar energy. To meet the energy demand of consumers at all levels, we present the energy demand model of consumers after considering their electricity demand and temperature influence. Then, we formulate the optimal pricing problem for the electricity market under the framework of tiered pricing, so as to make the energy market get satisfactory income while meeting the energy demand of consumers. Finally, we use the particle swarm optimization algorithm to determine the optimal pricing strategy of the energy market. The simulation results show that when meeting the energy demand of consumers in different temperature conditions, the optimal pricing can be found that meets the satisfactory income of the energy market.

关键词： Photovoltaic systems Wind energy generation Energy consumption Uncertainty Wind energy Electricity Pricing Solar energy Wind power generation Smart grids

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An OpenPLC-based Active Real-time Anomaly Detection Framework for Industrial control systems

An OpenPLC-based Active Real-time Anomaly Detection Framewor...

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2022 Chinese Automation Congress, CAC 2022

作者： Zheng, Chengxu Wang, Xiaopeng Luo, Xiaoyu Fang, Chongrong He, Jianping Shanghai Jiao Tong University The Key Laboratory of System Control and Information Processing Ministry of Education of China And Shanghai Engineering Research Center of Intelligent Control and Management The Dept. of Automation Shanghai China NSFOCUS Technologies Group Co. Ltd Beijing China

ISBN: (纸本)9781665465335

In recent years, the design of anomaly detectors has attracted a tremendous surge of interest due to security issues in industrial control systems (ICS). Restricted by hardware resources, most anomaly detectors can only be deployed at the remote monitoring ends, far away from the control sites, which brings potential threats to anomaly detection. In this paper, we propose an active real-time anomaly detection framework deployed in the controller of OpenPLC, which is a standardized open-source PLC and has high scalability. Specifically, we add adaptive active noises to control signals, and then identify a linear dynamic system model of the plant offline and implement it in the controller. Finally, we design two filters to process the estimated residuals based on the obtained model and use χ2 detector for anomaly detection. Extensive experiments are conducted on an industrial control virtual platform to show the effectiveness of the proposed detection framework. © 2022 IEEE.

关键词： Anomaly detection

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Adaptive Virtual Synchronous Machine control Strategy for MMC Interconnect Converter Based on Distributed Measurement

Adaptive Virtual Synchronous Machine Control Strategy for MM...

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Power and Electrical engineering (ACPEE), 5th Asia Conference on

作者： Shaoqun Lu Zhengrong Xiang Kunyan Zhong Jinxue Meng Yinning Dang School of Mechanical and Electrical Engineering Guangxi Science&Technology Normal University Laibin China Hunan Engineering Laboratory for Photovolatic System Control and Optimization Hunan Vocational Institute of Technology Xiangtan China

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

Aiming at the problem of poor stability and response of traditional voltage source control, an adaptive virtual synchronous machine control strategy for Modular Multilevel Converter (MMC) interconnect converter based on distributed measurement is proposed. Through the introduction of virtual synchronous machine control, the interconnect converter can actively participate in grid stabilisation, and its adaptive strategy is adopted to enhance control system suppression capability on frequency oscillation, and the distributed measurement-based MCC control method can solve the insufficient flexibility and scalability of the traditional MMC centralised control strategy. The experimental results show that a large inertia in the DC bus dynamic process helps to enhance the voltage stability of the DC microgrid system. The AC microgrid bus frequency also has a large inertia, which contributes to the AC microgrid frequency stability. The transmitted power has greater inertia during load variations, especially when the load variations are greater. In addition, the distributed measurement-based MMC interconnect converter excels in steady-state control performance, balancing the capacitive voltages of the sub-modules and maintaining stable operation of the system through steady-state distributed control.

关键词： Voltage measurement Adaptive systems Atmospheric measurements Microgrids Power system stability Particle measurements Load management

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Event-triggered learning of Euler-Lagrange systems: A Koopman operator framework

Event-triggered learning of Euler-Lagrange systems: A Koopma...

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IEEE Conference on Decision and control

作者： Kaikai Zheng Dawei Shi Shilei Li Yang Shi MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing China Department of Mechanical Engineering Faculty of Engineering University of Victoria Victoria BC Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Euler-Lagrange (EL) systems represent a crucial and large class of dynamical systems, and a precise model of the true system would be beneficial in planning and tracking problems. This work aims to learn an unknown EL system using noisy measurement data to achieve improved data utilization efficiency. Specifically, for the considered EL system, a linear representation of the system is constructed using the Koopman operator, which is further characterized by sample data using Willems’ fundamental lemma. Moreover, an event-triggered learning mechanism is proposed to improve data utilization efficiency, and it is designed based on the analysis of the learning error bounds. The effectiveness of the proposed event-triggered learning approach is validated through a manipulator example.

关键词： Learning systems Event detection Manipulators Safety Planning Noise measurement Dynamical systems

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Performance-Aware control of Modular Batteries For Fast Frequency Response

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IEEE Transactions on Sustainable Energy 2025年

作者： He, Yutong Ruan, Guangchun Zhong, Haiwang Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Massachusetts Institute of Technology Lab for Information and Decision Systems CambridgeMA02139 United States

Modular batteries can be aggregated to deliver frequency regulation services for power grids. Although utilizing the idle capacity of battery modules is financially attractive, it remains challenging to consider the heterogeneous module-level characteristics such as dynamic operational efficiencies and battery degradation. In addition, real-time decision making within seconds is required to enable fast frequency response. In order to address these issues, this paper proposes a performance-aware scheduling approach for battery modules to deliver fast frequency response (FFR) support. In particular, the conduction loss and switching loss of battery packs as well as converters are captured within a mix-integer quadratic constrained program (MIQCP). The cycle-based aging model identifies the aging cost of battery modules during frequent cycling by introducing the aging subgradient calculation and linearization. Case studies based on real-world battery data show that the proposed scheduling approach can effectively reduce power loss cost by nearly 28%-57% and battery aging cost by 4%-15% compared to conventional methods, which can also enhance the SoC balance. © 2010-2012 IEEE.

关键词： Battery management systems

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