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Finite-time extended state observer-based iterative learning control for nonrepeatable nonlinear systems
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AI
DOI：10.1007/s11071-025-11016-3.png)
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Abstract
En 中文
This paper focuses on the high-precision tracking control problem of nonlinear systems subject to nonrepeatable disturbances. In order to handle the nonrepeatable disturbances properly, a finite-time extended state observer (FTESO) is firstly developed, based on which a novel robust iterative learning control (ILC) scheme is proposed. In such a case, the ILC approach is endowed with the ability to handle iteration-varying disturbances, which thus widens its application scope. The convergence of the proposed FTESO-based ILC algorithm is analyzed rigorously under the framework of composite energy function methodology. Finally, the effectiveness of the proposed method is validated by implementing on a permanent magnet synchronous motor system.
Keywords:
Iterative learning control (ILC)
Robustness
Finite time extended state observer (FTESO)
Nonrepeatable disturbances