English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

Hybrid Neural Adaptive Control for Practical Tracking of Markovian Switching Networks

Authors

Hu,  Bin
External Organizations;

Yu,  Xinghuo
External Organizations;

Guan,  Zhi-Hong
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Chen,  Guanrong
External Organizations;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PIKpublic
Supplementary Material (public)
There is no public supplementary material available
Citation

Hu, B., Yu, X., Guan, Z.-H., Kurths, J., Chen, G. (2021): Hybrid Neural Adaptive Control for Practical Tracking of Markovian Switching Networks. - IEEE Transactions on Neural Networks and Learning Systems, 32, 5, 2157-2168.
https://doi.org/10.1109/TNNLS.2020.3001009


Cite as: https://publications.pik-potsdam.de/pubman/item/item_25811
Abstract
While neural adaptive control is widely used for dealing with continuous- or discrete-time dynamical systems, less is known about its mechanism and performance in hybrid dynamical systems. This article develops analytical tools to investigate the neural adaptive tracking control of the hybrid Markovian switching networks with heterogeneous nonlinear dynamics and randomly switched topologies. A gradient-descent adaptation law built on neural networks (NNs) is presented for efficient distributed adaptive control. It is shown that the proposed control scheme can guarantee a stable closed-loop error system for any positive control gain and tuning gain. The tracking error is demonstrated to be practically uniformly exponentially stable with a threshold in the mean-square sense. This study further reveals how the topological structure affects the NN function, by measuring the influence of the switched topologies on the learning performance.