Zobrazeno 1 - 10
of 2 654
pro vyhledávání: '"Huang, Tingwen"'
Geomagnetic navigation has drawn increasing attention with its capacity in navigating through complex environments and its independence from external navigation services like global navigation satellite systems (GNSS). Existing studies on geomagnetic
Externí odkaz:
http://arxiv.org/abs/2410.15837
Privacy leakage and Byzantine failures are two adverse factors to the intelligent decision-making process of multi-agent systems (MASs). Considering the presence of these two issues, this paper targets the resolution of a class of nonconvex optimizat
Externí odkaz:
http://arxiv.org/abs/2409.18632
This paper addresses the problem of distributed online generalized Nash equilibrium (GNE) learning for multi-cluster games with delayed feedback information. Specifically, each agent in the game is assumed to be informed a sequence of local cost func
Externí odkaz:
http://arxiv.org/abs/2407.03578
Autor:
Yu, Yajie, Ma, Xuehui, Zhang, Shiliang, Wang, Zhuzhu, Shi, Xubing, Li, Yushuai, Huang, Tingwen
This paper presents an adaptive ensemble control for stochastic systems subject to asymmetric noises and outliers. Asymmetric noises skew system observations, and outliers with large amplitude deteriorate the observations even further. Such disturban
Externí odkaz:
http://arxiv.org/abs/2405.09973
Autor:
Yang, Songnan, Zhang, Xiaohui, Zhang, Shiliang, Ma, Xuehui, Bai, Wenqi, Li, Yushuai, Huang, Tingwen
Various animals exhibit accurate navigation using environment cues. The Earth's magnetic field has been proved a reliable information source in long-distance fauna migration. Inspired by animal navigation, this work proposes a bionic and data-driven
Externí odkaz:
http://arxiv.org/abs/2403.08808
This paper considers a class of noncooperative games in which the feasible decision sets of all players are coupled together by a coupled inequality constraint. Adopting the variational inequality formulation of the game, we first introduce a new loc
Externí odkaz:
http://arxiv.org/abs/2402.03669
This paper develops a Decentralized Multi-Agent Reinforcement Learning (Dec-MARL) method to solve the SoC balancing problem in the distributed energy storage system (DESS). First, the SoC balancing problem is formulated into a finite Markov decision
Externí odkaz:
http://arxiv.org/abs/2308.15394
This paper is concerned with the robust tracking control of linear uncertain systems, whose unknown system parameters and disturbances are bounded within ellipsoidal sets. We propose an adaptive robust control that can actively learn the ellipsoid se
Externí odkaz:
http://arxiv.org/abs/2308.03727
Breaking safety constraints in control systems can lead to potential risks, resulting in unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even among similar tasks. In this paper, we develop a novel adaptive safe contr
Externí odkaz:
http://arxiv.org/abs/2307.00828