Recurrent Neural Networks (RNNs) Controller For Dynamical System

Abstract

The ability of Artificial Neural Network (ANN) for controlling dynamical systems is presented. The structure of a neural controller based on model reference adaptive control (MRAC) is available. The Levenberg Marquardt back propagation (LMBP) is used in the learning of Locally Recurrent Neural Networks (LRNNs) to identify the plant and as a neural controller. This method has the ability to capture the nonlinearity and overcome the problems of dynamic systems. The parameters of the neural controller are adjusted with time by using the error signal between the output of the model reference and output of the system through the adjusting mechanism based on LMBP algorithm. A simple airplane channel at a certain freezing point is used as an example in this paper. More satisfactory results are obtained, with the locally recurrent neural network (LRNN) controller when compared with a classical controller for dynamical system.