Title :
Non-Linear Predictive Control using Optimisation Techniques
Author :
Willis, M.J. ; Montague, G.A. ; Di Massimo, C. ; Tham, M.T. ; Morris, A.J.
Author_Institution :
Department of Chemical and Process Engineering, University of Newcastle-upon-Tyne, U.K.
Abstract :
In this contribution a nonlinear multivariable predictive controller is proposed where the nominal model used for control law synthesis is a neural network. The technique makes use of an on-line optimisation routine which determines the future inputs that will minimise the deviations between the desired and predicted outputs. Control is implemented in a receding horizon fashion. The paper highlights the importance of selection of the network training philosophy by application of the predictive controller to a nonlinear distillation system. The enhanced performance using the neural network based control methodology is demonstrated.
Keywords :
Artificial neural networks; Chemical processes; Filters; Network synthesis; Network topology; Neural networks; Neurons; Nonlinear dynamical systems; Predictive control; Predictive models;
Conference_Titel :
American Control Conference, 1991
Conference_Location :
Boston, MA, USA
Print_ISBN :
0-87942-565-2