Chaos synchronization via adaptive recurrent neural control

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal

12 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)3536-3539
Journal / PublicationProceedings of the IEEE Conference on Decision and Control
Volume4
Publication statusPublished - 2001
Externally publishedYes

Conference

Title40th IEEE Conference on Decision and Control (CDC)
PlaceUnited States
CityOrlando
Period4 - 7 December 2001

Abstract

This paper proposes a new adaptive control structure, based on a dynamic neural network, for trajectory tracking of unknown nonlinear plants. The main components of this structure include a neural identifier and a control law, which together guarantee the desired trajectory tracking performance. Stability of the tracking control is analyzed by using the Lyapunov function method, and the structure is tested by simulations on an example of complex dynamical systems: chaos synchronization.

Research Area(s)

  • Adaptive control, Chaos production, Chaos synchronization, Dynamic neural networks, Lyapunov function, Stability

Citation Format(s)

Chaos synchronization via adaptive recurrent neural control. / Sanchez, Edgar N.; Perez, Jose P.; Ricalde, Luis J.; Chen, Guanrong.

In: Proceedings of the IEEE Conference on Decision and Control, Vol. 4, 2001, p. 3536-3539.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal