Implementation of intelligent controller using neural network state estimator

It is shown that the neural network can be used as a state estimator in a model-reference intelligent control system. Its learning capability and noise rejection characteristic are illustrated by the results of a simulation study. The implementation of the state estimator by a neural network was possible due to the development of a proper structure of the neural network which is capable of simulating the dynamic behavior of a linear or nonlinear system. This capability is achieved by use of a time-dependent learning process