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arXiv 2025年

作者： Behrendt, Gabriel Hale, Matthew Soderlund, Alexander Phillips, Sean Kain, Evan Georgia Institute of Technology AtlantaGA30332 United States Air Force Research Laboratory Kirtland AFB NM87117 United States School of Electrical and Computer Engineering United States Space Control Branch United States Spacecraft Component Technology United States

This paper presents a time-constrained model predictive control strategy for the six degree-of-freedom autonomous rendezvous, proximity, operations and docking problem between a controllable "deputy" satellite and an uncontrolled "chief" satellite. The objective is to achieve a docking configuration defined by both the translational and attitudinal states of the deputy relative to the chief, whose dynamics are respectively governed by both the Clohessy-Wiltshire equations and Euler’s second law of motion. The proposed control strategy explicitly addresses computational time constraints that are common to state-of-the-art space vehicles. Thus, a time-constrained model predictive control strategy is implemented on a space-grade processor. Although suboptimal with regards to energy consumption when compared to conventional optimal RPO trajectories, it is empirically demonstrated via numerical simulations that the deputy spacecraft still achieves a successful docking configuration while subject to computational time constraints. © 2025, CC BY.

关键词： Predictive control systems

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Design and Analysis of Weather Parameters Using MEMS technology with Suitable Characteristics for Integration into a Single Chip for Environment Monitoring

Design and Analysis of Weather Parameters Using MEMS Technol...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Abdurrashid S. Hassan Vimala A. Juliet G. Pius Agbulu G. Joselin Retna Kumar Raghavendra Reddy N V S Gunasekar Department of Electronics and Control/Instrumentation Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Mechatronic Engineering School of Engineering Sciences & Technology Chinhoyi University of Technology Chinhoyi Zimbabwe Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering RV Institute of Technology and Management Karnataka India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

This study designs and simulates weather parameters using MEMS technology with suitable characteristics for integration into a single chip for environment monitoring. The sensing and heating filament of the temperature sensor was designed using a thin film platinum resistor, examining its ability to experience stability, increase in resistivity, accuracy, and free-contamination. The temperature sensing and heating element are based on platinum RTD (Resistance Temperature Detector) with meander shape and aluminum as the bonding pad connecting the sensor to the external circuit. A capacitive humidity sensor is implemented in consolidation with a piezoresistive pressure sensor with a square membrane. The capacitive humidity sensor with hole pattern at the upper plate and a polyimide serves as water absorbent sandwiched between the electrodes. The simulation analysis was conducted using finite element analysis in COMSOL Multiphysics version 6.0 . It was observed that the temperature increases from -100oC to - 20oC, and 10oC to 50oC respectively as a result of deviation in relative humidity in the troposphere, and atmosphere. The average changes in capacitances were found at 0.0713/%RH, while the pressure was examined to vary from 1050hPa to 5hPa in a step of 200hPa with a sensitivity of 11.50mv/bar.

关键词： Temperature sensors Temperature Platinum Terrestrial atmosphere Humidity Stability analysis Finite element analysis

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Optimal Input Design for Sparse System Identification

Optimal Input Design for Sparse System Identification

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European control Conference (ECC)

作者： Javad Parsa Cristian R. Rojas Hå kan Hjalmarsson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9783907144077

ISBN: (纸本)9781665497336

In this contribution we consider sparse linear regression problems. It is well known that the mutual coherence, i.e. the maximum correlation of the regressors, is important for the ability of any algorithm to recover the sparsity pattern of an unknown parameter vector from data. A low mutual coherence improves the ability of recovery. In optimal experiment design this requirement may be in conflict with other objectives encoded by the desired Fisher matrix. In this contribution we alleviate this issue by combining optimal input design with a recently proposed approach to achieve low mutual coherence by way of a linear coordinate transformation. The resulting optimization problem is solved using cyclic minimization. Via simulations we demonstrate that the resulting algorithm is able to achieve a Fisher matrix which results in a performance close to the performance if the sparsity would have been known, while at the same time being able to recover the sparsity pattern.

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Collision-free motion control with learning features for automated vehicles in roundabouts

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Transportation Research Procedia 2023年 72卷 3545-3552页

作者： Balázs Németh Zsófia Farkas Zoltán Antal Dániel Fényes Péter Gáspár Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Kende utca 13-15. 1111 Budapest Hungary Budapest University of Technology and Economics Department of Control for Transportation and Vehicle Systems Stoczek utca 2. 1111 Budapest Hungary

control design for safe and time-efficient motion of automated vehicles in roundabout scenarios poses various challenges, especially adaptation to the actual traffic scenario and coordination of the vehicles. This paper proposes the design of a hierarchical motion control with learning feature for roundabout scenarios. The control is designed on two levels, such as on cloud level and on vehicle level. The control on the cloud level is designed by using reinforcement learning ( RL ), with which the energy efficient motion of the vehicle is achieved. The vehicle level contains a robust controller and a supervisor, with which the collision avoidance of the vehicles is guaranteed. The proposed control on a Hardware-in-the-Loop environment with small-scaled indoor vehicles in augmented reality is implemented. The effectiveness of the control and the safe motion of the automated vehicles under multi -vehicle scenario are demonstrated. The provided scenario illustrates that safe, i.e., collision-free motion of all automated vehicles can be guaranteed.

关键词： automated vehicles learning features roundabouts collision-free motion control design

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Personalized Federated Learning with Kolmogorov-Arnold Enhanced CNN for Rotating Equipment Fault Diagnosis

Personalized Federated Learning with Kolmogorov-Arnold Enhan...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Delanyo Kwame Bensah Kulevome Qiu Man Ernest Smith Mawuli Edward Opoku-Mensah Department of Computer and Information Engineering Qilu Institute of Technology Jinan Shandong China Department of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan Shandong China Department of Electrical/Electronic Technology Education Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Kumasi Ghana Department of Information Technologies HEC Montréal Canada

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Fault diagnosis of rotating equipment plays a crucial role in ensuring operational reliability and minimizing downtime in industrial systems. This study proposes a novel approach that integrates personalized federated learning (PFL) with a Kolmogorov-Arnold network (KAN)-enhanced convolutional neural network (CNN) to improve diagnostic accuracy across heterogeneous operational environments. The framework is designed to address variations in equipment conditions and data distributions, providing personalized models tailored to specific clients. A comprehensive evaluation is conducted using benchmark datasets, demonstrating significant improvements in fault classification accuracy, model interpretability, and training efficiency under non-IID data settings using the KAN-enhanced CNN model. This study contributes to advancing predictive maintenance practices, paving the way for resilient and intelligent industrial systems.

关键词： Fault diagnosis Vibrations Training Accuracy Federated learning Predictive models Data models Convolutional neural networks Thermal stability Predictive maintenance

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Stability and Similarity Detection for the Biologically Inspired Temporal Pooler Algorithms

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Procedia computer Science 2022年 213卷 570-579页

作者： Ivan Rodkin Petr Kuderov Aleksandr I. Panov Moscow Institute of Physics and Technology Dolgoprudny Russia Artificial Intelligence Research Institute (AIRI) Moscow Russia Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia

The biology of the brain can inspire the development of a wide range of intelligent information processing algorithms. Spatiotem-poral processing allows the brain to aggregate and represent information on multiple time and space scales. This paper examines temporal pooling, a biologically inspired spatiotemporal processing algorithm, in the task of creating stable sequential representations of objects while preserving their relative similarity. We compare two temporal pooling algorithm implementations: an existing Hierarchical Temporal Memory framework implementation called Union Temporal Pooler and our proposed Sandwich Temporal Pooler. We evaluate the temporal poolers on synthetic sequences using various aggregating metrics. It is shown that both implementations preserve high similarity; however, Union Temporal Pooler has better ability to preserve low and moderate similarity, while the proposed Sandwich Temporal Pooler features better output representation stability and computational efficiency.

关键词： Spatiotemporal Processing Temporal Pooling Hierarchical Temporal Memory Sparse Distributed Representations

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PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image

arXiv

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arXiv 2023年

作者： Li, Jianhui Li, Jianmin Zhang, Haoji Liu, Shilong Wang, Zhengyi Xiao, Zihao Zheng, Kaiwen Zhu, Jun Department of Computer Science and Technology Institute for AI BNRist Tsinghua University China State Key Laboratory of Astronautic dynamics Xi’an Satellite Control Center China RealAI China

We study the 3D-aware image attribute editing problem in this paper, which has wide applications in practice. Recent methods solved the problem by training a shared encoder to map images into a 3D generator’s latent space or by per-image latent code optimization and then edited images in the latent space. Despite their promising results near the input view, they still suffer from the 3D inconsistency of produced images at large camera poses and imprecise image attribute editing, like affecting unspecified attributes during editing. For more efficient image inversion, we train a shared encoder for all images. To alleviate 3D inconsistency at large camera poses, we propose two novel methods, an alternating training scheme and a multi-view identity loss, to maintain 3D consistency and subject identity. As for imprecise image editing, we attribute the problem to the gap between the latent space of real images and that of generated images. We compare the latent space and inversion manifold of GAN models and demonstrate that editing in the inversion manifold can achieve better results in both quantitative and qualitative evaluations. Extensive experiments show that our method produces more 3D consistent images and achieves more precise image editing than previous work. Source code and pretrained models can be found on our project page: https://***/Preim3D/. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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OnSort: An O(n) Comparison-Free Sorter for Large-Scale Dataset With Parallel Prefetching and Sparse-Aware Mechanism

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IEEE Transactions on Circuits and Systems II: Express Briefs 2025年

作者： Gao, Muxuan Jiang, Juntao Lei, Shuangming Wu, Huifeng Chen, Jun Liu, Yong Zhejiang University College of Control Science and Engineering Hangzhou310027 China Hangzhou Dianzi University Institute of Intelligent and Software technology Hangzhou China Zhejiang Normal University National Special Education Resource Center for Children with Autism Hangzhou311231 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

This paper proposes OnSort, a parallel comparison-free sorting architecture with O(n) time complexity, utilizing the SRAM structure to support large-scale datasets efficiently. The performance of existing comparison-free sorters is limited by uneven value distribution and variable element numbers. To address these issues, we introduce a parallel prefetching strategy to accelerate the indexing process and a sparse-aware mechanism to narrow the indexing search range. Furthermore, OnSort implements streaming execution through a pipelined design, thereby optimizing the previously overlooked latency of the counting phase. Experimental results show that, under the configuration of sorting 65,536 16-bit data elements, OnSort achieves a 1.97× speedup and a 22.6× throughput-to-area ratio compared to the existing design. © 2004-2012 IEEE.

关键词： Indexing (materials working)

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Goal and Force Switching Policy for DMP-Based Manipulation 1

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7th International Conference on Interactive Collaborative Robotics, ICR 2022

作者： Gorodetsky, Andrey Mironov, Konstantin Pushkarev, Daniil Panov, Aleksandr Moscow Institute of Physics and Technology Institutskiy per. 9 Moscow Region Dolgoprudny141701 Russia AIRI Artificial Intelligence Research Institute Kutuzovskii pr. 32/1 Moscow121165 Russia Federal Research Center "Computer Sciences and Control" Vavilova 44/2 Moscow119333 Russia

ISBN: (数字)9783031236099

ISBN: (纸本)9783031236082

Effective solving of manipulation tasks is significant for collaborative robots to act within human-oriented environments. It may be executed using classical or learning-based control methods. Classical methods are accurate;however, they require complicated tuning of the regulators. Learning-based control provide obtaining the parameters of the process model while training, but this model is rough. We apply a combined approach to solving manipulation tasks, where the robot moves to the target vicinity under learning-based control and then operates the target under simplified classical control. On the first stage, control system generates reference trajectory for execution using dynamic movement primitives (DMP). The parameters of the DMP are determined by output of a neural network and trained via policy optimization. On the second stage the forcing term of the DMP is set to zero, while goal is defined by the simplified predictive control model. We evaluate our approach on the tasks of reaching target point by end effector and pushing elevator button with UR5 collaborative manipulator. Evaluation is made in Isaac and URSim simulation taking in mind dynamics and functionality of the robot. The approach is successfully reproduced on a real robot. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Model predictive control

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Platoons Coordination Based on Decentralized Higher Order Barrier Certificates

Platoons Coordination Based on Decentralized Higher Order Ba...

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Mediterranean Conference on control and Automation (MED)

作者： Maryam Sharifi Dimos V. Dimarogonas ABB Corporate Research Västerås Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper presents control strategies based on time-varying convergent higher order control barrier functions for the coordination of networks of platoons. This network could be modelled by a class of leader-follower multi-agent systems, where the leaders have knowledge on the associated tasks and control the performance of their platoon involved vehicles. The followers are not aware of the tasks, and do not have any control authority to reach them. They follow their platoon leader commands for the task satisfaction. Signal temporal logic (STL) tasks are defined for the platoons coordination. Robust solutions for the task satisfaction, based on the leader’s accessibility to the follower vehicles’ states are suggested. In addition, using the notion of higher order barrier functions, decentralized barrier certificates for each vehicle evolving in a formation dynamic structure are proposed. Our approach finds solutions to guarantee the satisfaction of STL tasks independent of the agents’ initial conditions.

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