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Nonlinear iterative learning control for discriminating between disturbances
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DOI：10.1016/j.automatica.2024.111902.png)
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Abstract
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Disturbances in iterative learning control (ILC) may be amplified if these vary from one iteration to the next, and reducing this amplification typically reduces the convergence speed. The aim of this paper is to resolve this trade-off and achieve fast convergence, robustness and small converged errors in ILC. A nonlinear learning approach is presented that uses the difference in amplitude characteristics of repeating and varying disturbances to adapt the learning gain. Monotonic convergence of the nonlinear ILC algorithm is established, resulting in a systematic design procedure. Application of the proposed algorithm demonstrates both fast convergence and small errors. (c) 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords:
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Iterative learning control
Nonlinear control
Feedforward control
Stability of nonlinear systems
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