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