2025

最新文章

• Time Optimal Control for Data Harvesting in Mobile Wireless Sensor Networks AI翻译
  作者：Zhu, Yancheng; Andersson, Sean B.
  作者单位：Boston University; Boston University
  摘要：In this article, we consider the problem of finding an optimal trajectory of a single agent tasked with harvesting data from multiple mobile sensor nodes in a wireless sensor network. We describe data transmission using a free-space broadcast communication model and formulate an optimal control problem to extract the data from all the nodes in minimal time. In a 1-D mission space, we demonstrate that the optimal motion strategy can be expressed in parametric form. Within this strategy, the age...
• Phase Preservation of N-Port Networks Under General Connections AI翻译
  作者：Chen, Jianqi; Chen, Wei; Chen, Chao; Qiu, Li
  作者单位：Nanjing University; Peking University; Peking University; KU Leuven; Hong Kong University of Science & Technology; The Chinese University of Hong Kong, Shenzhen
  摘要：This study first introduces the frequency-wise phases of n-port linear time-invariant networks based on recently defined phases of complex matrices. Such a phase characterization can be utilized to quantify capacitive, inductive, and passive behaviors of n-port networks, as well as to relate to the power factor of the networks. Further, a class of matrix operations induced by fairly common n-port network connections is examined. The intrinsic phase properties of networks under such connections...
• An Improved Distributed Nesterov Gradient Tracking Algorithm for Smooth Convex Optimization Over Directed Networks AI翻译
  作者：Lin, Yifu; Li, Wenling; Zhang, Bin; Du, Junping
  作者单位：Beihang University; Beijing University of Posts & Telecommunications; Beijing University of Posts & Telecommunications
  摘要：This article explores the problem of distributed optimization for functions that are smooth and nonstrongly convex over directed networks. To address this issue, an improved distributed Nesterov gradient tracking (IDNGT) algorithm is proposed, which utilizes the adapt-then-combine rule and row-stochastic weights. A main novelty of the proposed algorithm is the introduction of a scale factor into the gradient tracking scheme to suppress the consensus error. By the estimate sequence approach, th...
• Uncertainty Propagation on Unimodular Matrix Lie Groups AI翻译
  作者：Ye, Jikai; Jayaraman, Amitesh S.; Chirikjian, Gregory S.
  作者单位：National University of Singapore; Stanford University; University of Delaware
  摘要：In this article, we propose a general method for uncertainty propagation on unimodular matrix Lie groups that have a surjective exponential map when the initial probability density function is concentrated. We derive the exact formula for the propagation of mean and covariance expressed in the form of expectation in a continuous-time setting from the governing Fokker-Planck equation. Two approximate propagation methods are discussed based on the exact formula. One uses numerical quadrature and...
• Stochastic Important-Data-Based Attack Power Allocation Against Remote State Estimation in Sensor Networks AI翻译
  作者：Tian, Engang; Fan, Mengge; Ma, Lifeng; Yue, Dong
  作者单位：University of Shanghai for Science & Technology; Nanjing University of Science & Technology; Nanjing University of Posts & Telecommunications
  摘要：In this article, a novel important-data-based (IDB) attack strategy and stochastic IDB attack power allocation scheme are proposed, from the attacker's perspective, to degrade the remote state estimation in sensor networks. The main feature of the proposed IDB attack is that, by intercepting the measurement output, the adversary can identify the important packets transmitting among sensing nodes, and by injecting more power to increase the attack success probability (ASP) of these packets, the...
• Minimum Bitrate Neuromorphic Encoding for Continuous-Time Gauss-Markov Processes AI翻译
  作者：Cuvelier, Travis; Ogden, Ronald; Tanaka, Takashi
  作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
  摘要：In this work, we study minimum data rate tracking of a dynamical system under a neuromorphic event-based sensing paradigm. We begin by bridging the gap between continuous-time (CT) system dynamics and information theory's causal rate distortion theory. We motivate the use of nonsingular source codes to quantify bitrates in event-based sampling schemes. This permits an analysis of minimum bitrate event-based tracking using tools already established in the control and information theory literatu...
• Classification of Stochastic Systems: Deep Learning and Hypothesis Testing AI翻译
  作者：Zhang, Qing; Ma, Xiaohang; Yin, George
  作者单位：University System of Georgia; University of Georgia; University of Connecticut
  摘要：This article is devoted to classification of stochastic systems given by stochastic differential equations in continuous time. We develop two novel approaches. The first one is based on the use of a deep neural network (NN), whereas the second one uses hypothesis test-based methods. The idea of deep learning method focuses on treating the given stochastic system models by generating Monte Carlo sample paths. These samples are used to train a deep neutral network. A least square error is used a...
• Ultimate Boundedness and Output Convergence of Prioritized Output Tracking Control Under Nonsmooth and Imperfect Feedback Linearization AI翻译
  作者：An, Sang-ik; Lee, Dongheui; Park, Gyunghoon
  作者单位：University of Seoul; Technische Universitat Wien
  摘要：The concept of priority has been introduced to the robotic systems in 1980s as an effort to overcome problems caused by singularity and it has attracted considerable attention from both the robotics and control societies. However, none of the previous works have successfully addressed two fundamental degenerate properties of singularity: nonsmoothness and imperfect inversion. This technical note proposes a prioritized output tracking control method that guarantees the ultimate boundedness and ...
• Model Approximation in MDPs With Unbounded Per-Step Cost AI翻译
  作者：Bozkurt, Berk; Mahajan, Aditya; Nayyar, Ashutosh; Ouyang, Yi
  作者单位：McGill University; University of Southern California
  摘要：In this article, we consider the problem of designing a control policy for an infinite-horizon discounted cost Markov decision process M when we only have access to an approximate model M<^>. How well does an optimal policy pi<^>(star) of the approximate model perform when used in the original model M? We answer this question by bounding a weighted norm of the difference between the value function of pi<^>(star) when used in M and the optimal value function of M. We then extend our results and...
• Robust Nonlinear Optimal Control via System Level Synthesis AI翻译
  作者：Leeman, Antoine P.; Kohler, Johannes; Zanelli, Andrea; Bennani, Samir; Zeilinger, Melanie N.
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; European Space Agency
  摘要：This article addresses the problem of finite horizon constrained robust optimal control for nonlinear systems subject to norm-bounded disturbances. To this end, the underlying uncertain nonlinear system is decomposed based on a first-order Taylor series expansion into a nominal system and an error (deviation) described as an uncertain linear time-varying system. This decomposition allows us to leverage system level synthesis to jointly optimize an affine error feedback, a nominal nonlinear tra...