Hybrid Neural Adaptive Control for Practical Tracking of Markovian Switching 
Networks

Hu, Bin; Yu, Xinghuo; Guan, Zhi-Hong; Kurths, Jürgen; Chen, Guanrong

doi:10.1109/TNNLS.2020.3001009

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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;

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引用

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. doi:10.1109/TNNLS.2020.3001009.

引用: https://publications.pik-potsdam.de/pubman/item/item_25811

要旨

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.