Abstract:

For reducing the dependence of nonlinear observer on the precision model, a non conventional NN(Neural Network) observer for nonlinear system is proposed. The neuro-observer is a three-layer feedforward neural network, which is trained extensively with the error backpropagation learning algorithm including a correction term to guarantee good tracking and bounded NN weights. Designing the neural network observer is using artificial neural network to identify the nonlinear parts of the system and using a Luenberger observer to reconstruct the states of the system. The Lyapunov direct method is used in order to ensure the stability of the proposed non-conventional observer. The proposed observer is applied to 2 degrees of freedom horizontal manipulator to evaluate its performance. The simulation results show that the state observation of uncertain systems can be solved by the method and it is suitable for the low precision model of the nonlinear system.

Key words: neural network observer, nonlinear system, manipulator