Estimation of states of a nonlinear plant using dynamic neural network

The purpose of this paper is to design a dynamic neural network that can effectively estimate all the states of single input non linear plants. Lyapunov's stability theory along with solution of full form Ricatti equation is used to guarantee that the tracking errors are uniformly bounded. No a priori knowledge on the bounds of weights and errors are required. The nonlinear plant and the dynamic neural network models have been simulated by the same input to illustrate the validity of theoretical results.