no code implementations • 5 Feb 2024 • Xiaobing Dai, Zewen Yang, Mengtian Xu, Fangzhou Liu, Georges Hattab, Sandra Hirche
Gaussian processes are harnessed to compensate for the unknown components of the multi-agent system.
no code implementations • 5 Feb 2024 • Zewen Yang, Songbo Dong, Armin Lederer, Xiaobing Dai, Siyu Chen, Stefan Sosnowski, Georges Hattab, Sandra Hirche
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.
no code implementations • 5 Feb 2024 • Zewen Yang, Xiaobing Dai, Akshat Dubey, Sandra Hirche, Georges Hattab
This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs).
no code implementations • 26 Jul 2023 • Zhenxiao Yin, Xiaobing Dai, Zewen Yang, Yang shen, Georges Hattab, Hang Zhao
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).
no code implementations • 14 May 2023 • Xiaobing Dai, Armin Lederer, Zewen Yang, Sandra Hirche
When the dynamics of systems are unknown, supervised machine learning techniques are commonly employed to infer models from data.
no code implementations • 11 Apr 2023 • Xiaobing Dai, Zewen Yang, Sandra Hirche
In the realm of the cooperative control of multi-agent systems (MASs) with unknown dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to its modeling flexibility of nonlinear functions and the existence of a theoretical prediction error bound.
no code implementations • 29 Mar 2021 • Zewen Yang, Stefan Sosnowski, Qingchen Liu, Junjie Jiao, Armin Lederer, Sandra Hirche
In this paper, a distributed learning leader-follower consensus protocol based on Gaussian process regression for a class of nonlinear multi-agent systems with unknown dynamics is designed.