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Critical Commentary on Deterministic Artificial Intelligence Applied to Oscillatory Circuits

With heritage in nonlinear adaptive control (as proposed by Slotine) and physics-based control (as proposed by Lorenz), recently proposed methods referred to as deterministic artificial intelligence (D.A.I.) claim slight performance improvement over the parent methods. This brief communication firstly validates claims of slight improvement, but furthermore highlights a key feature: indications that improvements in observer implementations are the proper path for subsequent development in the field. The manuscript validates the recently published 97% performance improvement over classical methods using nonlinear adaptive methods, with an addition 0.23% performance improvement using D.A.I. compared to nonlinear adaptive control. Furthermore, the work also identifies strong correlation between system performance and observer performance, which is significant since D.A.I. eliminates controller tuning. Thus, observer improvement is recommended for future developments. The recently published 2-norm optimal learning scheme (of Smeresky) is recommended as the next step in the lineage of research in the discipline assuming augmentation with nonlinear state observers.

Deterministic Artificial Intelligence, D.A.I., Van Der Pol, Adaptive Control, Physics-Based Controls, State Observers, Luenberger Observers

APA Style

Eric Miller, Timothy Sands. (2021). Critical Commentary on Deterministic Artificial Intelligence Applied to Oscillatory Circuits. Control Science and Engineering, 5(1), 13-19. https://doi.org/10.11648/j.cse.20210501.12

ACS Style

Eric Miller; Timothy Sands. Critical Commentary on Deterministic Artificial Intelligence Applied to Oscillatory Circuits. Control Sci. Eng. 2021, 5(1), 13-19. doi: 10.11648/j.cse.20210501.12

AMA Style

Eric Miller, Timothy Sands. Critical Commentary on Deterministic Artificial Intelligence Applied to Oscillatory Circuits. Control Sci Eng. 2021;5(1):13-19. doi: 10.11648/j.cse.20210501.12

Copyright © 2021 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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