Exponential Convergence Analysis of Fault Identification for Nonlinear Sampled-data Systems Using Deterministic Learning

In this paper, a sampled-data fault identification (SDFI) scheme for nonlinear uncertain systems is proposed based on the deterministic learning, and the exponential convergence of the SDFI algorithm is analyzed. First, a sampled-data (SD) Linear Time-Varying (LTV) system is employed to describe the learning systems. Then, the uniformed completely observability (UCO) property and the Lyapunov theorem are employed to analyze exponential convergence of the SD LTV system. Based on this, the detailed analysis of the convergence property is derived, which is related to learning speed and learning accuracy.