Finite-time Projective Synchronization of Hyperjerk Systems Modeled With Fuzzy Recurrent Neural Networks
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 4482-4495 |
| Number of pages | 14 |
| Journal / Publication | IEEE Transactions on Fuzzy Systems |
| Volume | 32 |
| Issue number | 8 |
| Online published | 15 May 2024 |
| Publication status | Published - Aug 2024 |
Link(s)
Abstract
In this article, we present a terminal sliding-mode control method for the projective synchronization of unmodeled hyperjerk systems subject to parameter perturbation and external disturbances. We leverage fuzzy recurrent neural networks to identify unknown hyperjerk systems. We propose a control law for projective synchronization via the adaptive estimation of the unknown bounds of parameter perturbation and external disturbances. We theoretically prove that the proposed control law is able to achieve chattering-free projective synchronization in finite time. Finally, we elaborate on the simulation results to demonstrate the efficacy of the methods. © 2024 IEEE.
Research Area(s)
- Adaptive systems, Backstepping, fuzzy recurrent neural networks, Hyperjerk systems, Mathematical models, projective synchronization, Recurrent neural networks, Sliding mode control, Synchronization, terminal sliding-mode control, Uncertainty, Fuzzy recurrent neural networks (FRNNs), terminal slidingmode control (TSMC)
Citation Format(s)
Finite-time Projective Synchronization of Hyperjerk Systems Modeled With Fuzzy Recurrent Neural Networks. / Zhang, Baojie; Wang, Jun; Feng, Yuming et al.
In: IEEE Transactions on Fuzzy Systems, Vol. 32, No. 8, 08.2024, p. 4482-4495.
In: IEEE Transactions on Fuzzy Systems, Vol. 32, No. 8, 08.2024, p. 4482-4495.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
