Dynamic event‐triggered fault identification for nonlinear systems via deterministic learning

In this paper, a fault identification strategy for nonlinear systems is proposed by combining the deterministic learning (DL)‐based adaptive high‐gain observer (AHGO) with a dynamic event‐triggered mechanism (DETM). The DL theory is utilized to satisfy the partial persistent excitation condition, while the AHGO is employed to estimate the system states and fault functions simultaneously. Two DETMs are adopted to reduce data transmission and computational burden. The inter‐event intervals of the considered event‐triggered mechanisms are proven to be positive, thus excluding the Zeno phenomenon. The novelty of this paper lies in that, through the special design of AHGO and event‐triggered conditions, the estimation errors can converge to zero with arbitrary precision. Meanwhile, by incorporating the estimated output error into the DETM design, it is demonstrated that the number of events can be adaptively adjusted based on the fault signal. Furthermore, the relationship between the observer gain and system performance, as well as the inter‐event interval, is revealed (The event‐triggered mechanisms design method that ensures exponential convergence of the observer). Finally, the effectiveness of the developed strategy is verified through a simulation example.