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Distributed Adaptive Tracking control for Fuzzy Nonlinear MASs Under Round-Robin Protocol

IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1488-1498页

作者： Fan, Sha Meng, Min Fu, Yukai Deng, Chao Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence Nanjing210023 China Tongji University Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai200070 China Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210049 China

In this article, the cooperative fuzzy tracking control problem for a type of high-order nonlinear multi-agent systems under the Round-Robin (RR) communication protocol is solved. To effectively reduce the communication among agents, an RR communication protocol is first proposed, where each agent can communicate with only one neighbor at any given time. Based on the developed communication protocol, a hierarchical control approach is introduced, consisting of a distributed observer layer that utilizes information from both local and neighboring agents, and a decentralized control layer that operates solely based on local agent information. Specifically, a distributed observer is designed in the distributed observer layer to achieve the distributed observation objective by introducing a time-varying gain in the observer input. Furthermore, an improved distributed observer with the upper triangular form is constructed to produce a signal with high-order derivatives. In the decentralized control layer, fuzzy adaptive controllers are designed for all agents by using the backstepping technique. By using the Lyapunov stability theory, rigorous stability analysis shows that the cooperative fuzzy tracking control problem is solved under the developed hierarchical control approach. In the end, a simulation example is presented to demonstrate the validity of our proposed method. © 1993-2012 IEEE.

关键词： Protocols Observers Stability analysis Decentralized control Backstepping Vectors Symmetric matrices Network topology Telecommunications System dynamics

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Robust Representation Learning Based on Deep Mutual information for Scene Classification Against Adversarial Perturbations

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11963-11978页

作者： Li, Linjuan Xie, Gang Zhang, Haoxue Xie, Xinlin Li, Heng Taiyuan University of Science and Technology School of Electronic Information Engineering Taiyuan030024 China Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan030024 China The Hong Kong Polytechnic University Department of Building and Real Estate Hong Kong

Remote sensing scene classification enables data-driven decisions for various applications, such as environmental monitoring, urban planning, and disaster management. However, deep learning models used for scene classification are highly vulnerable to adversarial samples, resulting in incorrect predictions and posing significant risks. While most current methods focus on improving adversarial robustness, they face a trade-off that compromises accuracy on clean, unperturbed images. To address this challenge, we utilized information theory by incorporating a mutual information (MI) representation (MIR) module, which allows the model to capture high-quality, robust features. Furthermore, a domain adversarial training (AdvT) strategy is applied to promote the learning of domain-invariant features, reducing the effect of distribution differences between clean images and adversarial samples. We propose a novel algorithm that accurately differentiates between clean and adversarial scenes by introducing the MI and domain adaptation-guided network (MIDANet). Extensive experiments demonstrate the effectiveness of our approach against adversarial attacks, revealing a positive correlation between adversarial perturbations and image information entropy, and a negative correlation with robust accuracy (RAcc). © 2008-2012 IEEE.

关键词： Deep neural networks

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Infrared Hot Spot Detection of Photovoltaic Arrays Based on Optimization and Improvement Using YOLOv8 1st

Infrared Hot Spot Detection of Photovoltaic Arrays Based on ...

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1st Electrical Artificial Intelligence Conference, EAIC 2024

作者： Peng, ZiRan Wang, SiYua Xiao, Shenping Department of Electrical and Information Engineering Hunan University of Technology Zhuzhou412007 China Hunan Key Laboratory of Electric Drive Control and Intelligent Equipment Hunan University of Technology Zhuzhou412007 China

ISBN: (纸本)9789819640621

Detecting hot spots on solar cells in mountainous photovoltaic power plants is challenging due to high false and missed detection rates. This paper proposes S-YOLO, a lightweight model based on YOLOv8, for efficient hot spot detection. The model introduces a Spatial Confusion Backbone to reduce parameters and computational complexity while enhancing feature extraction with attention mechanisms. A Multi-scale Feature Pyramid Network (MSBFPN) improves small-target detection, and the SIoU loss function replaces CIoU for faster convergence and precise regression. Compared to YOLOv8, S-YOLO reduces weights by 41.3%, parameters by 21.2%, and increases accuracy by 2.3%. Its lightweight design and high accuracy make it ideal for drone-based inspections in complex environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Infrared detection Loss function SIoU Network model Solar cells YOLOv8

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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Tissue P Systems with Look-Ahead Mode

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Chinese Journal of Electronics 2025年第1期23卷 81-86页

作者： Yun Jiang Tao Song Zheng Zhang School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Department of Control Science and Engineering Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China

Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.

关键词： Computers Computational modeling Medical services Parallel processing Polynomials Computational efficiency Registers Business

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A Unifying Complexity-Certification Framework for Branch-and-Bound Algorithms for Mixed-Integer Linear and Quadratic Programming

arXiv

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arXiv 2025年

作者： Shoja, Shamisa Arnström, Daniel Axehill, Daniel Division of Automatic Control Department of Electrical Engineering Linköping University Sweden Division of Systems and Control Department of Information Technology Uppsala University Sweden

In model predictive control (MPC) for hybrid systems, solving optimization problems efficiently and with guarantees on worst-case computational complexity is critical, particularly in real-time applications. These optimization problems often take the form of mixed-integer linear programs (MILPs) or mixed-integer quadratic programs (MIQPs) that depend on system parameters. A common approach for solving such problems is the branch-and-bound (B&B) method. This paper extends existing complexity certification methods by presenting a unified complexity-certification framework for B&B-based MILP and MIQP solvers, specifically for the family of multi-parametric MILP and MIQP problems that arise in, e.g., hybrid MPC applications. The framework provides guarantees on worst-case computational metrics, including the maximum number of iterations or relaxations B&B algorithms require to reach optimality. It systematically accounts for different branching and node selection strategies, as well as heuristics integrated into B&B, ensuring a comprehensive certification framework. By offering theoretical guarantees and practical insights for solver customization, the proposed framework enhances the reliability of B&B for real-time application. The usefulness of the proposed framework is demonstrated through numerical experiments on both random MILPs and MIQPs, as well as on MIQPs arising from a hybrid MPC problem. © 2025, CC BY.

关键词： Mixed-integer linear programming

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Parallel Domain-Decomposition Algorithms for Complexity Certification of Branch-and-Bound Algorithms for Mixed-Integer Linear and Quadratic Programming

arXiv

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arXiv 2025年

When implementing model predictive control (MPC) for hybrid systems with a linear or a quadratic performance measure, a mixed-integer linear program (MILP) or a mixed-integer quadratic program (MIQP) needs to be solved, respectively, at each sampling instant. Recent work has introduced the possibility to certify the computational complexity of branch-and-bound (B&B) algorithms when solving MILP and MIQP problems formulated as multi-parametric MILPs (mp-MILPs) and mp-MIQPs. Such a framework allows for computing the worst-case computational complexity of standard B&B-based MILP and MIQP solvers, quantified by metrics such as the total number of LP/QP iterations and B&B nodes. These results are highly relevant for real-time hybrid MPC applications. In this paper, we extend this framework by developing parallel, domain-decomposition versions of the previously proposed algorithm, allowing it to scale to larger problem sizes and enable the use of high-performance computing (HPC) resources. Furthermore, to reduce peak memory consumption, we introduce two modifications to the existing (serial) complexity certification framework, integrating them into the proposed parallel algorithms. Numerical experiments show that the parallel algorithms significantly reduce computation time while maintaining the correctness of the original framework. © 2025, CC BY.

关键词： Mixed-integer linear programming

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Investigating Crowdsourced Neural Radiance Maps for Autonomous Vehicles

Investigating Crowdsourced Neural Radiance Maps for Autonom...

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2nd International Conference on Cognitive Mobility, COGMOB 2023

作者： Zhu, Morui Szász, Erik Szántó, Mátyás Vaitkus, Márton Sörös, Gábor Department of Control Engineering and Information Technology Budapest University of Technology and Economics Budapest Hungary Nokia Bell Labs Budapest Hungary

ISBN: (纸本)9783031876196

We present a crowdsourcing and mapping pipeline that enables the generation of accurate neural radiance field (NeRF) maps from camera feeds of autonomous vehicles. We generate synthetic image feeds from the CARLA driving simulator, we compare three different models (Nerfacto, TensoRF, Instant-NGP), and evaluate the accuracy of the reconstructions based on the number of acquisition vehicles and trajectory variations. We also demonstrate interactive visualization of the NeRF maps in a virtual reality headset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D reconstruction

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Precision Synchronous control of Multiple Motion Systems: A Tube-Based MPC Approach

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 974-988页

作者： Shuaiqi Chen Fazhi Song Yue Dong Ning Cui Yang Liu Xinkai Chen Department of Control Science and Engineering School of Aeronautics Harbin Institute of Technology Harbin China Center of Ultra-precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin China Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technology Harbin China Department of Electronic and Information Systems Shibaura Institute of Technology Tokyo Japan

Lithography machines operate in scanning mode for the fabrication of large-scale integrated circuits (ICs), requiring high-precision synchronous motion between the reticle and wafer stages. Disturbances generated by each stage during high-acceleration movements are transmitted through the base frame, resulting in degradation of synchronization performance. To address this challenge, this paper proposes a tube-based model predictive control (tube-MPC) approach for synchronization in lithography machines. First, the proposed modeling method accurately characterizes the coupling disturbances and synchronization dynamics. Subsequently, a tube-MPC approach is developed to ensure that the states of the nominal system are constrained within the terminal constraint set. To reduce the complexity of online computations, an approach is employed to transform online optimization problems into offline problems by creating an online lookup table. This enables the determination of optimal control inputs via a simplified online optimization algorithm. The robustness and trajectory tracking performance of the proposed approach are verified through simulation experiments, demonstrating its effectiveness in enhancing the synchronization performance of multiple motion systems.

关键词： Semiconductor device modeling Trajectory tracking Lithography Optimal control Transforms Prediction algorithms Robustness Synchronization Optimization Predictive control

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Integrated wellbore-surface pressure control production optimization for shale gas wells

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Natural Gas Industry B 2025年第2期12卷 123-134页

作者： Zhou, Xingyu Zhang, Liming Qi, Ji Wang, Yanxing Zhang, Kai Zhang, Ruijia Sun, Yaqi School of Petroleum Engineering China University of Petroleum (East China) Qingdao 266580 China Exploration and Development Research Institute Shengli Oilfield Company SINOPEC Dongying 257015 China Oilfield Development Department Changqing Oilfield Company CNPC Xi'an 710016 China School of Information and Control Engineering Qingdao University of Technology Qingdao 266520 China

Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high- and low-pressure wells within the same development block, which leads to issues involving mixed-pressure flows. Traditional pipeline optimization methods used in conventional gas well blocks fail to address the unique needs of shale gas wells, such as the precise planning of airflow paths, pressure distribution, and compression. This study proposes a pressure-controlled production optimization strategy specifically designed for shale gas wells operating under mixed-pressure flow conditions. The strategy aims to improve production stability and optimize system efficiency. The decline in production and pressure for individual wells over time is forecasted using a predictive model that accounts for key factors of system optimization, such as reservoir depletion, wellbore conditions, and equipment performance. Additionally, the model predicts the timing and impact of liquid loading, which can significantly affect production. The optimization process involves analyzing the existing gathering pipeline network to determine the most efficient flow directions and compression strategies based on these predictions, while the strategy involves adjusting compressor settings, optimizing flow rates, and planning pressure distribution across the network to maximize productivity while maintaining system stability. By implementing these strategies, this study significantly improves gas well productivity and enhances the adaptability and efficiency of the gathering and transportation system. The proposed approach provides systematic technical solutions and practical guidance for the efficient development and stable production of shale gas fields, ensuring more robust and sustainable pipeline operations. © 2025 Sichuan Petroleum Administration

关键词： Integration Pipeline optimization Production optimization Productivity prediction Shale gas

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