(2003) “Nonlinear Model Predictive Control of a Multivariable Hammerstein Model Using Genetic Algorithms”. Ichem Transactions.

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Abstract

Model predictive control or MPC can provide robust control for processes with variable gain and dynamics, multivariable interaction, measured loads and unmeasured disturbances. In this paper a novel approach for the implementation of nonlinear MPC is proposed using genetic algorithms (GAs). The proposed method formulates the MPC as an optimization problem and genetic algorithms are used in the optimization process. Application to two types of nonlinear models namely Hammerstein and Wiener Models is studied and the simulation results are shown for the case of two chemical processes to demonstrate the performance of the proposed scheme

Item Type:	Article
Subjects:	Electrical
Department:	College of Engineering and Physics > Electrical Engineering
Depositing User:	AYHAM ZAZA
Date Deposited:	24 Mar 2008 13:36
Last Modified:	01 Nov 2019 13:24
URI:	http://eprints.kfupm.edu.sa/id/eprint/697