Iterative learning fault estimation design for nonlinear system with random trial length
Summary: An iterative learning scheme-based fault estimation observer is designed for a class of nonlinear systems with randomly changed trial length. This is achieved by presenting a state observer for monitoring the system state and an iterative learning law for fault estimation in the presence of imprecise system model. An average factor is defined to deal with the lack and redundancy in tracking information caused by random trial length. Via the convergence analysis, sufficient design conditions are developed for estimation of fault signal. The observer gains and iterative learning law indexes are computed by solving the proposed conditions under \(\lambda\)-\textit{norm} constraints. Numerical examples are presented to demonstrate the validity, the effectiveness, and the superiority of this method.
iterative learning fault estimation design
nonlinear system with random trial length
scientific article; zbMATH DE number 6833370
scientific article