Nonlinear internal model control and model predictive control using neural networks

Abstract

The ramifications of incorporating neural networks into two model-based control architectures, namely internal model control (IMC) and model predictive control (MPC), are considered. The development of a neural network analog to the conventional IMC design is described, the controller behavior is discussed, and control architectures necessary to improve controller performance are presented. The performance of the neural network controller under less restrictive assumptions is examined, and a neural network analog to the conventional MPC design is developed and tested. An IMC-type neural network controller in which the process model replaced by a neural network gives very good performance, even when only partial state data are available, also gives excellent performance. These results indicate that neural networks can learn accurate models and give good nonlinear control when model equations are not known. Suggestions for improving performance are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>