Predictive control of a mixing tank using radial basis function networks

Author

Mason, Julian ; Kambhampati, C.

Author_Institution

Dept. of Cybern., Reading Univ., UK

Volume

1

fYear

1996

fDate

11-13 Dec 1996

Firstpage

478

Abstract

This paper presents a predictive control strategy employing a radial basis function network (RBFN) for the process model. Similarly to other neural network architectures, the RBFN can model any continuous bounded nonlinear mapping, but has the advantage of faster training. This facilitates its use in adaptive situations. The effectiveness of the controller described here is demonstrated using a mixing tank application. In addition proofs are stated regarding the robustness and stability of the closed-loop system

Keywords

adaptive control; closed loop systems; feedforward neural nets; neurocontrollers; nonlinear control systems; predictive control; robust control; CSTR; RBFN; adaptive situations; closed-loop system; continuous bounded nonlinear mapping; mixing tank; neural network architectures; predictive control; radial basis function networks; robustness; stability; Control systems; Cybernetics; Inductors; Neural networks; Optimal control; Predictive control; Predictive models; Radial basis function networks; Robust control; Robust stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1996., Proceedings of the 35th IEEE Conference on

Conference_Location

Kobe

ISSN

0191-2216

Print_ISBN

0-7803-3590-2

Type

conf

DOI

10.1109/CDC.1996.574359

Filename

574359