Learning-based adaptive control with an accelerated iterative adaptive law

Zhongjiao Shi, Liangyu Zhao*

*Corresponding author for this work

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10 Citations (Scopus)
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Abstract

A novel adaptive control framework equipped with an accelerated iterative learning update mechanism is developed to handle time-varying uncertainties, based on the combination of standard adaptive control architecture and Heavy-ball optimization algorithm. The stability analysis shows that the tracking error and the estimated weight error are both bounded, and the closed-loop system is exponentially stable. The momentum term, introduced in the accelerated iterative adaptive law, makes the proposed learning-based adaptive control possess a faster convergence rate. The proposed learning-based adaptive control is applied to aircraft control to show that the proposed framework can handle time-varying uncertain parameters.

Original languageEnglish
Pages (from-to)5831-5851
Number of pages21
JournalJournal of the Franklin Institute
Volume357
Issue number10
DOIs
Publication statusPublished - Jul 2020

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Shi, Z., & Zhao, L. (2020). Learning-based adaptive control with an accelerated iterative adaptive law. Journal of the Franklin Institute, 357(10), 5831-5851. https://doi.org/10.1016/j.jfranklin.2020.03.018