Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/16828
Title: Adaptive neural control of nonlinear systems with hysteresis
Authors: REN BEIBEI
Keywords: Adaptive control, nonlinear control, neural networks, hysteresis
Issue Date: 24-Jul-2009
Source: REN BEIBEI (2009-07-24). Adaptive neural control of nonlinear systems with hysteresis. ScholarBank@NUS Repository.
Abstract: Control of nonlinear systems preceded by unknown hysteresis nonlinearities has received increasing attention in recent years with growing industrial demands involving varied applications. The most common approach is to construct an inverse operator, which, however, has its limits due to the complexity of the hysteresis characteristics. The main focus of the thesis is to explore new avenues to fuse several different hysteresis models with the available control techniques to achieve the stable output tracking performance without constructing a hysteresis inverse. By investigating the characteristics of these hysteresis models, neural network (NN) based control approaches fused with these hysteresis models are developed for the concerned uncertain nonlinear systems, in the presence of unmodelled dynamics and uncertain hysteresis models. The results presented in this thesis can be considered as a stepping stone to be used toward the development of a general control framework for the systems with hysteretic behavior. Analytical and simulation results are presented to show the effectiveness of the proposed schemes.
URI: http://scholarbank.nus.edu.sg/handle/10635/16828
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
RenB.PDF1.05 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

398
checked on Dec 11, 2017

Download(s)

328
checked on Dec 11, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.