Use of Stochastic Neural Networks for Process Control

Abstract Process control engineers now have a powerful tool, in the form of Artificial Neural Networks. These networks can be made to model arbitrary non-linear functions. This capability of modelling non-linear mappings is of immensense value to the process engineer in as much that he now has a tool which is capable of reflecting most, if not all, of the complexities of chemical processes. Most neural networks utilise sigmoid type activation functions. The use of these activation functions, leads to complicated learning rules like the back propagation algorithm. However, if Gaussian (stochastic) activation functions are utilised the learning algorithms are greatly simplified. In this paper networks with Guassian activation functions are used to identify tnot only he input-output relationship of a CSTR, but also the inverse relationship. Identification of the inverse relationship helps in formulating an IMC type controller. The performance of the neural based IMC controller is compared to a neural based predictive controller.

[1]  Carlos E. Garcia,et al.  Internal model control. A unifying review and some new results , 1982 .

[2]  A. J. Morris,et al.  Artificial neural networks in process engineering , 1991 .

[3]  C. R. Cutler,et al.  Dynamic matrix control¿A computer control algorithm , 1979 .

[4]  M. Morari,et al.  Internal Model Control: extension to nonlinear system , 1986 .

[5]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[6]  Daniel Sbarbaro,et al.  Neural Networks for Nonlinear Internal Model Control , 1991 .

[7]  David W. Clarke,et al.  Generalized predictive control - Part I. The basic algorithm , 1987, Autom..

[8]  David Clarke,et al.  Self-tuning control , 1979 .

[9]  Daniel Sbarbaro,et al.  Connectionist representations and control structures , 1991 .

[10]  J. Duane Morningred,et al.  An Adaptive Nonlinear Predictive Controller , 1990, 1990 American Control Conference.

[11]  M. T. Tham,et al.  An evaluation of nonlinear control strategies , 1991 .

[12]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[13]  M. Morari,et al.  Internal model control. VI: Extension to nonlinear systems , 1986 .

[14]  Václav Peterka,et al.  Predictor-based self-tuning control , 1982, Autom..

[15]  Stephen A. Billings,et al.  Non-linear system identification using neural networks , 1990 .

[16]  S. A. Billings,et al.  Non-Linear Systems Identification Using Neural Networks , 1989 .

[17]  K S Narendra,et al.  IDENTIFICATION AND CONTROL OF DYNAMIC SYSTEMS USING NEURAL NETWORKS , 1990 .