Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929235032014848 |
|---|---|
| author | Yang, Zewen Dong, Songbo Lederer, Armin Dai, Xiaobing Chen, Siyu Sosnowski, Stefan Hattab, Georges Hirche, Sandra |
| author_facet | Yang, Zewen Dong, Songbo Lederer, Armin Dai, Xiaobing Chen, Siyu Sosnowski, Stefan Hattab, Georges Hirche, Sandra |
| contents | 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 correlation-aware cooperative algorithm framework built upon Gaussian process regression, which adeptly captures inter-agent correlations for uncertainty predictions. A standout feature is its exceptional efficiency in deriving the aggregation weights achieved by circumventing the computationally intensive posterior variance calculations. Through Lyapunov stability analysis, the distributed control law ensures bounded tracking errors with high probability. Simulation experiments validate the protocol's efficacy in effectively managing complex scenarios, establishing it as a promising solution for robust tracking control in multi-agent systems characterized by uncertain dynamics and dynamic communication structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03048 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies Yang, Zewen Dong, Songbo Lederer, Armin Dai, Xiaobing Chen, Siyu Sosnowski, Stefan Hattab, Georges Hirche, Sandra Multiagent Systems Machine Learning Systems and Control 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 correlation-aware cooperative algorithm framework built upon Gaussian process regression, which adeptly captures inter-agent correlations for uncertainty predictions. A standout feature is its exceptional efficiency in deriving the aggregation weights achieved by circumventing the computationally intensive posterior variance calculations. Through Lyapunov stability analysis, the distributed control law ensures bounded tracking errors with high probability. Simulation experiments validate the protocol's efficacy in effectively managing complex scenarios, establishing it as a promising solution for robust tracking control in multi-agent systems characterized by uncertain dynamics and dynamic communication structures. |
| title | Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies |
| topic | Multiagent Systems Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2402.03048 |