Adaptive Global Integral Neuro-sliding Mode Control for a Class of Nonlinear System

Authors:
Yuelong Hao;Jinggang Zhang;Zhimei Chen
Affiliations:
Institute of Electronic Information Engineering, Taiyuan University of Science and Technology, 030024, China;Institute of Electronic Information Engineering, Taiyuan University of Science and Technology, 030024, China;Institute of Electronic Information Engineering, Taiyuan University of Science and Technology, 030024, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

Citing 6
Cited 0

Adaptive fuzzy sliding mode control with GA-based reaching laws

Fuzzy Sets and Systems
Adaptive Control

Adaptive Control
Fuzzy adaptive sliding-mode control for MIMO nonlinear systems

IEEE Transactions on Fuzzy Systems
Brief Fuzzy moving sliding mode control with application to robotic manipulators

Automatica (Journal of IFAC)
Decentralized sliding mode adaptive controller design based on fuzzy neural networks for interconnected uncertain nonlinear systems

IEEE Transactions on Neural Networks
Neuro-sliding mode control with its applications to seesaw systems

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

An scheme of composite sliding control is proposed for a class of uncertainty nonlinear system, which is based on fuzzy neural networks (FNN) and simple neural networks (SNN). The SNN is uniquely determined by the design of the global integral sliding mode surface, the output of which replaces the corrective control, and FNN is applied to mimic the equivalent control. In this scheme, the bounds of the uncertainties and the extern disturbance are not required to be known in advance, and the stability of systems is analyzed based on Lyapunov function. Simulation results are given to demonstrate the effectiveness of this scheme.