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Iterative learning scheme‐based fault estimation design for nonlinear systems with varying trial lengths and specified constraints
Author(s) -
Feng Li,
Xu Shuiqing,
Chai Yi,
Zhang Ke
Publication year - 2018
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4287
Subject(s) - iterative learning control , iterative method , nonlinear system , observer (physics) , redundancy (engineering) , computer science , control theory (sociology) , mathematical optimization , mathematics , algorithm , artificial intelligence , physics , control (management) , quantum mechanics , operating system
Summary This 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 ‐ n o r m 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.