Comparison of three neural network controllers

Abstract

The integration of neural networks in control theories has the potential to revolutionize modern control engineering by enhancing performance measures and increasing plant efficiency. This project aims to explore the performance of three neural network controllers: Neural Network Predictive controller , Neural Network Model Reference Controller, and the Nonlinear Auto-Regressive Moving Average Controller and evaluate the potential benefits and challenge sassociated with each type of controller. A system simulation was created on Simulink and Matlab scripts to compare a set of performance measures. Data were collected from several simulations and averaged to provide estimates. The finding sindicat etha tneura lnetwork sca nsignificant lyimpro v eaplant ’sresponse and accuracy,especially the Nonlinear Auto-Regressive Moving Average (NARMA-L2) and Neural Network Model Predictive Controllers. However, challenges such as controller training and computational power suggest a need to optimize the network algo- rithms. While neural network controllers provided enhanced responses with minimal informa- tion about the plants,each type studied has a specifi cfie ld ofapplicati ona ndlimitations under certain conditions.