Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/16047
Title: Data-based PID controller designs for nonlinear systems
Authors: IMMA NUELLA
Keywords: data-based, PID, nonlinear, fuzzy neural network, JITL
Issue Date: 13-Jun-2008
Source: IMMA NUELLA (2008-06-13). Data-based PID controller designs for nonlinear systems. ScholarBank@NUS Repository.
Abstract: In this thesis, the data-based controller designs for nonlinear process are developed. Firstly, an adaptive PID control scheme is proposed in the fuzzy neural network modeling framework. By utilizing Lyapunov method, an updating algorithm is derived to adjust the PID parameters to guarantee the convergence of the predicted tracking error. Next, a self-tuning PID controller design is designed based on the JITL modeling technique. This proposed design method exploit the current process information from controller database and modeling database to realize on-line tuning of PID parameters. The controller database is constructed to store the PID parameters with their corresponding information vector, and the modeling database is employed for the standard use by JITL for the modeling purpose. The PID parameters are obtained from controller database and can be updated during on-line implementation.Simulation results are presented to demonstrate that the proposed control strategies give better performances than their conventional counterpart.
URI: http://scholarbank.nus.edu.sg/handle/10635/16047
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Imma - Thesis.pdf1.36 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

484
checked on Dec 11, 2017

Download(s)

323
checked on Dec 11, 2017

Google ScholarTM

Check


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