Gomm, J.B. and Doherty, Sean and Williams, D. (1996) Control of pH in-line using a neural predictive strategy. In: UKACC International Conference on Control '96 (Conf. Publ. No. 427). IET, pp. 1058-1063. ISBN 0852966687

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Abstract

Control of an experimental in-line pH process exhibiting varying nonlinearity and deadtime is described. A radial basis function (RBF) artificial neural network is used to model the nonlinear dynamics of the process. Accommodation of the varying process deadtime in the neural model is achieved by the generation of a feed-forward signal, for input to the neural network, from a downstream pH measurement. The feedforward signal is derived from a variable delay model based on process knowledge and a flow measurement. The neural model is then used to realise a predictive control scheme for the process. Development of the neural process model is described and results are presented to illustrate the performance of the neural predictive control scheme which is tested as a regulator at different setpoints.

Item Type:	Book Section
Keywords:	pH control; process control; predictive control; neurocontrollers; nonlinear control systems; feedforward neural nets; delays; control system synthesis;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	9685
Identification Number:	https://doi.org/10.1049/cp:19960699
Depositing User:	Seán Doherty
Date Deposited:	17 Jul 2018 16:10
Publisher:	IET
Refereed:	Yes
URI:	https://ieeexplore.ieee.org
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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