Abstract
SummaryThis technical note deals with a fault estimation (FE) problem for nonlinear systems with varying trial lengths subjected to specified constraints. An iterative learning observer is developed to achieve FE and state reconstruction simultaneously, which thus to consider the state error and fault estimating information from previous iteration to improve the FE performance in the current iteration. Compared with conventional observer‐based FE methods, the proposed method only requires the bounds of parameter matrices rather than the precise model of the system. To deal with the missing and redundancy problems caused by varying trial lengths, a truncation operator is presented to design the FE law. Different from existing iterative learning methods, the presented method aims at the nonlinear systems with specified constraints, such as filling systems. Furthermore, the λ‐norm method and mathematical induction are employed to obtain the solutions of iterative learning matrices and observer gain matrix. Finally, illustrative examples are introduced to demonstrate the validity and the effectiveness of the proposed FE approach.
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