Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

Abstract

In this study, an adaptive neural network synchronization (NNS) approach, capable of guaranteeing prescribed performance (PP), is designed for non-identical fractional-order chaotic systems (FOCSs). For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.

Observer-based sliding mode synchronization for a class of fractional-order chaotic neural networks

“…Observer design for nonlinear dynamic systems is a significant and interesting research area, and it has a lot of potential applications in control engineering, fault reconstruction, state estimation, and signal tracking [27][28][29][30][31][32][33][34][35][36][37][38][39]. Because the fractional derivative of a compound function has a very complicated form, most of the current observers which were designed for classical nonlinear systems cannot be used to fractional ones directly.…”