Properties of the Neural Network Internal Model Controller

Abstract

Nonlinear Internal Model Controller (IMC), realized with a multilayer perceptron neural network, is studied in this paper. The neural networks for the process model and the resulting controller are identified using the Recursive Prediction Error (RPE) method with the applied gradient calculation procedure. Novel stability analysis and stability projection methods are also introduced. Both simulation and laboratory processes are used in testing the control performance. Results indicate that the IMC control structure provides robust performance and is clearly a good alternative for controlling nonlinear plants. The real-time experiment addresses the very important question of implementing and gaining practical experience with neural network controllers.