A new adaptive neural network based observer for robotic manipulators

In this paper, a new neural network based observer is proposed for a class of nonlinear systems. The proposed observer can applied to estimate nonlinear systems with a high nonlinearity without any prior knowledge about system. This features help the proposed neuro-observer for real implementation and to use it in practice. The Lyapunov's direct method employed to show the stability and estimating performance of the proposed scheme. Simulation results on a two DOF robot manipulator are presented to show the efficiency of the proposed neural network based observer.