Fast modelling and control of unknown nonlinear systems via neural networks

Abstract

This paper presents a unified framework for the use of neural networks for the modelling and control of unknown nonlinear systems. The systems are assumed to be expressed by a unknown NARMA model including a set of unknown parameters. At first, it is assumed that the nominal (initial) values of these parameters are known during an initial operation period of the system. By incorporating the nominal parameter set into the structure of neural network, a neural network model for the system can be established via online training of the weights during the initial operation period. Using the trained neural network, the estimation of the parameters is achieved by incorporating the estimated parameters into the neural network model and by constructing a gradient descent based estimation algorithm. The design of the controllers for the unknown nonlinear systems have been discussed and desired results have been obtained via comparing existing neural network based modelling approaches.