@inproceedings{09b0d2144f9a4a6896b9e72a372f6e7f,
title = "Memory-Augmented Adaptive Control With Parameter Convergence Guarantee",
abstract = "A modified concurrent learning adaptive control, named memory-augmented adaptive control, is developed in this paper, which guarantees the convergence of parameter estimates without the requirement of persistent exciting condition and state derivative estimation. A stable low-pass filter is introduced into the error dynamics to filter out the state derivatives, circumventing the estimation of the unmeasurable state derivatives. And, the filtered basis matrix is also used in this adaptive control law to achieve the exponential convergence of the estimation error. Numerical simulations show that the proposed memory-augmented adaptive control performs better in tracking and estimation error convergence, compared to the standard adaptive control.",
keywords = "adaptive control, concurrent learning, exponential convergence, finite excitation",
author = "Huajie Zhu and Zhongjiao Shi and Liangyu Zhao",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 China Automation Congress, CAC 2021 ; Conference date: 22-10-2021 Through 24-10-2021",
year = "2021",
doi = "10.1109/CAC53003.2021.9727684",
language = "English",
series = "Proceeding - 2021 China Automation Congress, CAC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "340--345",
booktitle = "Proceeding - 2021 China Automation Congress, CAC 2021",
address = "United States",
}