Neural network control for fractional-order nonlinear systems based on observer and event-triggered strategy
YOU Xing-xing
TAO Xu
GUO Bin
XIANG Guo-fei
LIU Kai
DIAN Song-yi
Abstract：In this paper,an adaptive neural network event-triggered control scheme is proposed for tracking control of a class of fractional-order nonlinear systems.Firstly,radial basis function neural networks are used to approximate the unknown nonlinear functions,and a neural networks-based state observer is constructed to estimate the state of original system.Then,the event-triggered strategy is presented in the design of controller,and the stability of closed-loop system is analyzed by using the Lyapunovmethod.In addition,a new condition is developed for estimating the time interval lower bound of the event-triggered condition of fractional-order nonlinear systems in this paper,thus the Zenophenomenon can be avoided.Theoretical analysis shows that the proposed control scheme can not only ensure that the tracking error converges to the neighborhood near the origin,but also can guarantee the boundedness of all signals in the closed-loop system.Finally,simulation of fractional-order interconnected power systems demonstrates the effectiveness of scheme.
Keywords：fractional-order nonlinear systemsobserverneural networks controlevent-triggered controldynamic surface control
Publication Date：2024-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 1735-1744 )