Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Dai, Xiaobing"'
In this work, we develop an analytical framework that integrates opinion dynamics with a recommendation system. By incorporating elements such as collaborative filtering, we provide a precise characterization of how recommendation systems shape inter
Externí odkaz:
http://arxiv.org/abs/2411.11687
With the increasing complexity of real-world systems and varying environmental uncertainties, it is difficult to build an accurate dynamic model, which poses challenges especially for safety-critical control. In this paper, a learning-based control p
Externí odkaz:
http://arxiv.org/abs/2408.05319
Safe control for dynamical systems is critical, yet the presence of unknown dynamics poses significant challenges. In this paper, we present a learning-based control approach for tracking control of a class of high-order systems, operating under the
Externí odkaz:
http://arxiv.org/abs/2405.00822
Autor:
Huang, Tzu-Yuan, Zhang, Sihua, Dai, Xiaobing, Capone, Alexandre, Todorovski, Velimir, Sosnowski, Stefan, Hirche, Sandra
In many control system applications, state constraint satisfaction needs to be guaranteed within a prescribed time. While this issue has been partially addressed for systems with known dynamics, it remains largely unaddressed for systems with unknown
Externí odkaz:
http://arxiv.org/abs/2403.08054
Consensus control in multi-agent systems has received significant attention and practical implementation across various domains. However, managing consensus control under unknown dynamics remains a significant challenge for control design due to syst
Externí odkaz:
http://arxiv.org/abs/2402.03174
Autor:
Yang, Zewen, Dong, Songbo, Lederer, Armin, Dai, Xiaobing, Chen, Siyu, Sosnowski, Stefan, Hattab, Georges, Hirche, Sandra
This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies. The approach leverages a correla
Externí odkaz:
http://arxiv.org/abs/2402.03048
This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs). The key contribution of this work is the development of an elective learning algorithm, nam
Externí odkaz:
http://arxiv.org/abs/2402.03014
The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the
Externí odkaz:
http://arxiv.org/abs/2307.13945
To reach carbon neutrality in the middle of this century, smart controls for building energy systems are urgently required. Model predictive control (MPC) demonstrates great potential in improving the performance of heating ventilation and air-condit
Externí odkaz:
http://arxiv.org/abs/2307.00638
When the dynamics of systems are unknown, supervised machine learning techniques are commonly employed to infer models from data. Gaussian process (GP) regression is a particularly popular learning method for this purpose due to the existence of pred
Externí odkaz:
http://arxiv.org/abs/2305.08169