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Distributed Prescribed-Time Observer for Nonlinear Systems

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arXiv 2025年

作者： de Heij, Vincent Niazi, M.B. Umar Johansson, Karl H. Ahmed, Saeed Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper proposes a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite limited local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that asymptotically approach infinity as the prescribed time is approached. The theoretical convergence is rigorously proven and validated through numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Nonlinear simulations

Time-Constrained Model Predictive control for Autonomous Satellite Rendezvous, Proximity Operations, and Docking

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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

Decoupling While Coupling: Towards More Accurate Stereo Image Sand Removal Beyond Certainty

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Decoupling While Coupling: Towards More Accurate Stereo Imag...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bingcai Wei Hui Liu Chuang Qian Yi Jia Wangyu Wu Zhishan Li School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool UK School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns.

关键词： Couplings Uncertainty Neural networks Estimation Signal processing Robustness Stereo vision Image restoration Speech processing Autonomous vehicles

Linear-Nonlinear Feature Reconstruction Network for Emotion Recognition from Brain Functional Connectivity

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Fu, Baole Yu, Xiangkun Liu, Yinhua Qingdao University Institute for Future School of Automation Qingdao 266071 China Qingdao University College of Computer Science & Technology Qingdao 266071 China Qingdao University Institute for Future School of Automation Shandong Provincial Key Laboratory of Industrial Control Technology Qingdao 266071 China

Electroencephalogram (EEG) records both static performance and dynamic changes in brain electrical activity, with linear and nonlinear features capable of modelling these static and dynamic aspects. However, traditional EEG-based emotion recognition methods rely excessively on linear features (static analysis) without fully considering nonlinear features (dynamic changes). This limitation hinders a comprehensive capture of the complex interactions and dynamic variations between brain regions. This study proposes an EEG emotion recognition method based on a linear-nonlinear feature reconstruction network using brain functional connectivity. The method comprehensively captures the complex interactions and dynamic changes between brain regions by extracting linear and nonlinear features from dynamic and static connectivity. To enhance feature discriminability, a feature self-reconstruction encoder (FSRE) is introduced, which refines feature representation and improves the accuracy and discrimination of features. A feature interaction and reconstruction module (FIR Module) uses a bidirectional reconstruction strategy to interact with and reconstruct features based on their relationships, thereby enhancing the correlation between features and target classification. Experiments conducted on the SEED and SEED-IV datasets validate the effectiveness of this method, demonstrating that it achieves higher accuracy and robustness in emotion classification tasks compared to other advanced methods. © 1963-2012 IEEE.

关键词： brain functional connectivity electroencephalogram (EEG) Emotion recognition feature reconstruction linearnonlinear relationship

Theory of phase reduction for systems with higher-order interactions: a route to Kuramoto models on hypergraphs

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arXiv 2025年

作者： León, Iván Muolo, Riccardo Hata, Shigefumi Nakao, Hiroya Department of Applied Mathematics and Computer Science Universidad de Cantabria Santander39005 Spain Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Graduate School of Science and Engineering Kagoshima University Kagoshima890-0065 Japan Research Center for Autonomous Systems Materialogy Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8501 Japan

Phase reduction is a powerful technique in the study of nonlinear oscillatory systems. Under certain assumptions, it allows us to describe each multidimensional oscillator by a single phase variable, giving rise to simple phase models such as the Kuramoto model. Classically, the method has been applied in the case where the interactions are only pairwise (two-body). However, increasing evidence shows that interactions in real-world systems are not pairwise but higher-order, i.e., many-body. Although synchronization in higher-order systems has received much attention, analytical results are scarce because of the highly nonlinear nature of the framework. In this paper, we fill the gap by presenting a general theory of phase reduction for the case of higher-order interactions. We show that the higher-order topology is preserved in the phase reduced model at the first order and that only odd couplings have an effect on the dynamics when certain symmetries are present. Additionally, we show the power and ductility of the phase reduction approach by applying it to a population of Stuart-Landau oscillators with an all-to-all configuration and with a ring-like hypergraph topology;in both cases, only the analysis of the phase model can provide insights and analytical results. © 2025, CC BY-NC-ND.

关键词： Topology

Numerical Modeling of Oxygen Diffusion in Tissue Spheroids Undergoing Fusion Using Functional Representation and Finite Volumes

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arXiv 2025年

作者： Vilinski-Mazur, Katherine Kirillov, Bogdan Rogozin, Oleg Kolomenskiy, Dmitry Center for Materials Technologies Skolkovo Institute of Science and Technology Moscow Russia Center for Precision Genome Editing and Genetic Technologies for Biomedicine Institute of Gene Biology Russian Academy of Sciences Moscow Russia Federal Research Center"Computer Science and Control" Russian Academy of Sciences Moscow Russia

A three-dimensional cell culture called a spheroid serves as a foundational entity in a wide variety of modern tissue engineering applications, including 3D-bioprinting and preclinical drug testing. Lack of oxygen within tissue spheroids hinders metabolism of cells and eventually leads to cell death. Prevention of necrosis is crucial to success of tissue engineering methods and such prevention requires estimation of cell viability in the spheroid. We propose a novel approach for numerical modeling of diffusion in tissue spheroids during their fusion. The approach is based on numerical solutions of partial differential equations and the application of Function Representations (FRep) framework for geometric modeling. We present modeling of oxygen diffusion based on meshes derived from the geometry of fusing spheroids, a method for selecting optimal spheroid size, and several statistics for estimating cellular viability. Our findings provide insights into oxygen diffusion in three-dimensional cell cultures thus improving the robustness of biotechnological methods that employ tissue spheroids. © 2025, CC BY.

关键词： Cell death

Secure-by-Construction Synthesis for control Systems

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IEEE Transactions on Automatic control 2025年

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Thrust of Artificial Intelligence Information Hub The Thrust of Intelligent Transportation System Hub China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Technical University of Munich TUM School of Engineering and Design Germany University of Colorado Boulder Department of Computer Science United States Ludwig Maximilian University Department of Computer Science Munich Germany

In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Finally, we validate the effectiveness of our results through a case study of a vehicle. © 1963-2012 IEEE.

关键词： Iterative methods

A meta-reinforcement learning method for adaptive payload transportation with variations

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Neurocomputing 2025年 638卷

作者： Chen, Jingyu Ma, Ruidong Xu, Meng Candan, Fethi Mihaylova, Lyudmila Oyekan, John Institute of Software Chinese Academy of Sciences Beijing China Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom School of Information Technology & Management University of International Business and Economics Beijing China Department of Computer Science University of York York United Kingdom

The safe transport of cable-suspended payloads by a group of Unmanned Aerial Vehicles (UAVs) depends on their capacity to effectively respond to fluctuations in the dynamics caused by external variations, such as wind gusts. For group transportation with obstacles, internal variations, such as changes in formation, can also alter the space occupancy of the system related to collision detection. However, traditional adaptive learning methods are challenging to adapt to these two variations. In this paper, we present a learning-based method for collision-free dual-UAV–payload transportation in the presence of varied wind force and formation change. It consists of an adaptive trajectory tracking controller based on meta-model-based reinforcement learning with online adaptation and a novel correction policy, and a path planner that can sample collision-free goal states of the system for the controller based on the meta-collision predictor. The simulation results demonstrate that the proposed trajectory tracking controller outperforms state-of-the-art model-free, model-based, and variational inference methods in terms of payload tracking error reduction and robustness when dealing with the variations mentioned above. Specifically, the proposed controller reduces the average payload tracking error to less than 0.1 metres in most tasks without obstacles. Furthermore, by following the adapted paths generated by the path planner, the trajectory tracking controller can effectively track the payload while ensuring collision-free safety of the dual-UAV–payload system during navigation among obstacles. The success rate of the proposed method is more than 80% in all scenarios with obstacles. Our project website can be seen at https://***/view/meta-payload-fly/ and the source code is available at https://***/wawachen/Meta-load-fly. © 2025

关键词： Unmanned aerial vehicles (UAV)

KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learning

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arXiv 2025年

作者： Niazi, M.B. Umar Cao, John Barreau, Matthieu Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States

This paper proposes a novel learning approach for designing Kazantzis-Kravaris/Luenberger (KKL) observers for autonomous nonlinear systems. The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state, whose dynamics is linear and stable. The observer’s state is then mapped back to the original system coordinates via the inverse map to obtain the state estimate. However, finding this transformation and its inverse is quite challenging. We propose to sequentially approximate these maps by neural networks that are trained using physics-informed learning. We generate synthetic data for training by numerically solving the system and observer dynamics. Theoretical guarantees for the robustness of state estimation against approximation error and system uncertainties are provided. Additionally, a systematic method for optimizing observer performance through parameter selection is presented. The effectiveness of the proposed approach is demonstrated through numerical simulations on benchmark examples and its application to sensor fault detection and isolation in a network of Kuramoto oscillators using learned KKL observers. © 2025, CC BY.

关键词： State estimation