PROCESS CONTROL USING RECURRENT NEURAL NETWORKS

Abstract

Neural network based control schemes are generally designed by implementing feedforward neural network models in standard control engineering structures. Introduction of discrete time recurrent networks, which are inherently dynamic systems, into those schemes can simplify the design of neural controllers. In this paper we describe the concept of applying recurrent networks trained with the real-time recurrent learning algorithm in the indirect adaptive control schemes. A combined network consisting of the control network and the model network is constructed to allow the simple use of the real-time recurrent learning algorithm. To demonstrate the readability of the method two simulation examples are presented.