Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/4111
Title: Adaptive RBFNN versus conventional self-tuning: comparison of two parametric model approaches for non-linear control
Authors: Pereira, C. 
Henriques, J. 
Dourado, A. 
Keywords: Adaptive control; Pole-placement; Non-linear control; Neural networks
Issue Date: 2000
Citation: Control Engineering Practice. 8:1 (2000) 3-12
Abstract: In this work a practical study evaluates two parametric modelling approaches -- linear and non-linear (neural) -- for automatic adaptive control. The neural adaptive control is based on a developed hybrid learning technique using an adaptive (on-line) learning rate for a Gaussian radial basis function neural network. The linear approach is used for a self-tuning pole-placement controller. A selective forgetting factor method is applied to both control schemes: in the neural case to estimate on-line the second-layer weights and in the linear case to estimate the parameters of the linear process model. These two techniques are applied to a laboratory-scaled bench plant with the possibility of dynamic changes and different types of disturbances. Experimental results show the superior performance of the neural approach particularly when there are dynamic changes in the process.
URI: http://hdl.handle.net/10316/4111
Rights: openAccess
Appears in Collections:FCTUC Eng.Informática - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat 
file26a60e346d7e4a88b574a1136d2346be.pdf345.67 kBAdobe PDFView/Open
Show full item record

Page view(s) 50

324
checked on Jun 12, 2019

Download(s)

29
checked on Jun 12, 2019

Google ScholarTM

Check


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