2026

最新文章

• Fast and Robust State Estimation and Tracking via Hierarchical Learning AI翻译
  作者：Mclaughlin, Connor; Ding, Matthew; Erdogmus, Deniz; Su, Lili
  作者单位：Northeastern University; University of California System; University of California Berkeley
  摘要：Fast and reliable state estimation and tracking are essential for real-time situation awareness in cyber-physical systems operating in tactical environments or complicated civilian environments. Traditional centralized solutions do not scale well whereas existing fully distributed solutions over large networks suffer slow convergence, and are vulnerable to a wide spectrum of communication failures. In this article, we aim to speed up the convergence and enhance the resilience of state estimati...
• A Time-Delay Approach for Stabilization of Nonlinear Affine Systems With Unknown Control Direction AI翻译
  作者：Pan, Gaofeng; Zhu, Yang; Wu, Zheng-Guang
  作者单位：Zhejiang University; Zhejiang University
  摘要：We propose a novel stability analysis for affine systems with unknown control directions, focusing on stabilization through extremum seeking to minimize a Lyapunov function. Our approach draws inspiration from Lie bracket systems, employing a transformation using time-delay analysis to convert the closed-loop system into a nominal time-delay system. Leveraging techniques from input-to-state stability, we provide a proof of semiglobal practical asymptotic stability. Compared to the preceding an...
• Distributed Stochastic Zeroth-Order Optimization With Compressed Communication AI翻译
  作者：Hua, Youqing; Liu, Shuai; Hong, Yiguang; Ren, Wei
  作者单位：Shandong University; Tongji University; Tongji University; University of California System; University of California Riverside
  摘要：The dual challenges of high communication costs and gradient inaccessibility-common in privacy-sensitive systems or black-box environments-drive our work on communication-constrained, gradient-free distributed optimization. We propose a compressed distributed stochastic zeroth-order algorithm (Com-DSZO), which requires only two function evaluations per iteration and incorporates general compression operators. Rigorous analysis establishes a sublinear convergence rate for both smooth and nonsmo...
• Preventing Inactive CBF Safety Filters Caused by Incorrect Relative Degree Assumptions AI翻译
  作者：Brunke, Lukas; Zhou, Siqi; Schoellig, Angela P.
  作者单位：Technical University of Munich; University of Toronto; Vector Institute for Artificial Intelligence
  摘要：Control barrier function (CBF) safety filters emerged as a popular framework to certify and modify potentially unsafe control inputs, for example, provided by a reinforcement learning agent or a nonexpert user. Typical CBF safety filter designs assume that the system has a uniform relative degree. This assumption is restrictive and is frequently overlooked in practice. When violated, the assumption can cause the safety filter to become inactive, allowing large and possibly unsafe control input...
• Frequency-Dependent Switching Control for Disturbance Attenuation of Linear Systems AI翻译
  作者：Zhang, Jingjing; Heiland, Jan; Benner, Peter; Du, Xin
  作者单位：Shanghai University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Max Planck Society; Otto von Guericke University
  摘要：The generalized Kalman-Yakubovich-Popov lemma as established by Iwasaki and Hara in 2005 marks a milestone in the analysis and synthesis of linear systems from a finite-frequency perspective. Given a prespecified frequency band, it allows us to produce passive controllers with excellent in-band disturbance attenuation performance at the expense of some of the out-of-band performance. This article focuses on control design of linear systems in the presence of disturbances with nonstrictly or no...
• Choose Your Battles: Distributed Learning Over Multiple Tug of War Games AI翻译
  作者：Chandak, Siddharth; Bistritz, Ilai; Bambos, Nicholas
  作者单位：Stanford University; Tel Aviv University; Tel Aviv University
  摘要：Consider N players and K games taking place simultaneously. Each of these games is modeled as a Tug-of-War (ToW) game where increasing the action of one player decreases the reward for all other players. Each player participates in only one game at any given time. At each time step, a player decides the game in which they wish to participate in and the action they take in that game. Their reward depends on the actions of all players who are in the same game. This system of K games is termed a ...
• Using Dynamic Safety Margins as Control Barrier Functions AI翻译
  作者：Freire, Victor; Nicotra, Marco M.
  作者单位：University of Colorado System; University of Colorado Boulder
  摘要：This article presents an approach to design control barrier functions (CBFs) for arbitrary state and input constraints using tools from the reference governor literature. In particular, it is shown that dynamic safety margins (DSMs) are CBFs for an augmented system obtained by concatenating the state with a virtual reference. The proposed approach is agnostic to the relative degree and can handle multiple state and input constraints using the control-sharing property of CBFs. The construction ...
• On Informative State Vectors for Discrete Dynamic Estimation in Switched Systems Using the Model-Based Minimum Distance Method AI翻译
  作者：Motchon, Koffi M. D.; Guelton, Kevin
  摘要：This technical note addresses the discrete dynamic estimation problem in switched linear systems using the model-based minimum distance method (MB-MDM). Prior work has shown that, for this method, the state vector must be sufficiently far from the origin to guarantee an accurate estimation of the discrete dynamic, thereby confirming inherent conflicts between discrete dynamic estimation and feedback control objectives in switched systems. To enhance the accuracy of the MB-MDM, this work focuse...
• Optimization and Learning in Open Multiagent Systems AI翻译
  作者：Deplano, Diego; Bastianello, Nicola; Franceschelli, Mauro; Johansson, Karl H.
  作者单位：University of Cagliari; Royal Institute of Technology
  摘要：Modern artificial intelligence relies on networks of agents that collect data, process information, and exchange it with neighbors to collaboratively solve optimization and learning problems. This article introduces a novel distributed algorithm to address a broad class of these problems in open networks, where the number of participating agents may vary due to several factors, such as autonomous decisions, heterogeneous resource availability, or denial of service (DoS) attacks. Extending the ...
• A Unified Framework for Controller Synthesis via the Generalized KYP Lemma AI翻译
  作者：Huang, Guilin; Shakib, M. Fahim; Jaimoukha, Imad M.
  作者单位：Imperial College London; Eindhoven University of Technology
  摘要：Controller synthesis in control theory faces significant challenges due to the complexity of solving nonlinear matrix inequalities (NLMIs). Common methods, such as weighting filters and change-of-variable techniques, have drawbacks like increased system order and restrictive assumptions requiring a common Lyapunov variable. This article presents a unified framework that linearizes NLMIs without needing a common Lyapunov variable. Our approach handles continuous and discrete linear time-invaria...