Stability Analysis of A Neuro-Identification Scheme with Asymptotic Convergence

This paper focuses on the stability and convergence analysis of a neuro-identification scheme for uncertain nonlinear systems. Based on linearly parameterized neural networks and the previous knowledge of upper bounds for the approximation error and disturbances, a robust modification of the descent gradient algorithm is proposed to make the overall identification process stable, and, in addition, the on-line residual prediction error asymptotically null, despite the presence of approximation error and disturbances. A simulation study to show the application and comparative performance of the proposed algorithm is presented.

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