Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach
Source abstract
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.
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Research context
OpenAlex topics: Chaos control and synchronization · Neural Networks Stability and Synchronization · Neural Networks and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Communication University of Shanxi Department of Media Management, , Jinzhong 030619, China
Huainan Normal University Department of Applied Mathematics, , Huainan 232038, China
Funding
National Natural Science Foundation of China
References (47)
Show source-provided references
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