Iterative learning based fault estimation for stochastic systems with variable pass lengths and data dropouts

This paper addresses the fault estimation (FE) problem in stochastic linear discrete-time varying systems with data dropouts and variable pass lengths. To comprehensively characterize the randomness of pass lengths and data dropouts, we utilize recursive Gaussian and Bernoulli distributions. We design a novel switch-type modified weighted iterative learning observer to achieve accurate FE. The fault estimation strategy of this observer integrates a modified weighted averaging operator with an intermittent update strategy (IUS) to address information loss and redundancy caused by variable pass lengths and data dropouts. Convergence conditions are established using the $\lambda$-norm method and recursive analysis. Additionally, the proposed iterative learning (IL) method effectively ensures the boundedness of FE errors. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed FE method.