Padmanabhan, R and Makam, R and George, K (2024) Multiple estimation models for discrete-time adaptive iterative learning control. In: International Journal of Systems Science, 55 . pp. 2154-2171.
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Abstract
This article focuses on making discrete-time Adaptive Iterative Learning Control more effective using multiple estimation models. Existing strategies use the tracking error to adjust the parametric estimates. Our strategy uses the last component of the identification error to tune these estimates of the model parameters. We prove that this strategy results in bounded estimates of the parameters, and bounded and convergent identification and tracking errors. We emphasise that the proof does not use the Key Technical Lemma. Rather, it uses the properties of square-summable sequences. We extend this strategy to include multiple estimation models and show that all the signals are bounded, and the errors converge. It is also shown that this works whether we switch between the models at every instant and every iteration or at the end of every iteration. Simulation results demonstrate the efficacy of the proposed method with a faster convergence using multiple estimation models. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
| Item Type: | Journal Article |
|---|---|
| Publication: | International Journal of Systems Science |
| Publisher: | Taylor and Francis Ltd. |
| Additional Information: | The copyright for this article belongs to authors. |
| Keywords: | Iterative methods; Learning algorithms; Learning systems; Parameter estimation; Two term control systems, Adaptive iterative learning control; Discrete time; Estimation models; Identification errors; Modeling parameters; Multiple estimation; Property; Strategy use; Technical lemmas; Tracking errors, Errors |
| Department/Centre: | Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering) |
| Date Deposited: | 29 Aug 2024 10:12 |
| Last Modified: | 29 Aug 2024 10:12 |
| URI: | http://eprints.iisc.ac.in/id/eprint/84863 |
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