Continuous Recurrent Neural Network Identifier Based On Exponential Barrier Lyapunov Functions

This paper describes an identifier for a class of nonlinear systems based on continuous recurrent neural networks (CRNN). The identifier is proposed considering the approximation properties of artificial neural networks. The learning or adaptive laws are obtained using Barrier Lyapunov functions with an exponentially decreasing barrier. The application of such a function results in a bounded identification error with an exponential convergence and predefined decay. Additionally, it ensures the convergence of the weights for the activation functions to the fitting values. The proposed identifier was used to identify a virtual Cartesian robot with two degrees of freedom. The results showed the performance of the identification error, which does not violate the imposed exponential barrier. Moreover, the effect of the predefined convergence parameter was observed in the identification error evolution without the need for the change of any other parameter in the CRNN.