Indirect Adaptive Control of Nonlinear System Using Recurrent Neural Network

Abstract

In this article, a locally recurrent neural network with input feed through (LRNNIFT) is presented for control of nonlinear dynamical systems. The rationale of using LRNNIFT is due to its modest structure and mathematical model, which gives it an edge over the existing Elman neural networks (ENN) and feed forward neural networks (FFNN). Results from simulation showed that LRNNIFT-based controller is able to achieve adaptive control in a nonlinear system. It is also tested and observed to counterbalance the effects of disturbances. A comparative analysis is presented with the help of simulation, and it is deduced that overall performance of LRNNIFT controller is better than that of FFNN and ENN controllers.