Critical Commentary on Deterministic Artificial Intelligence Applied to Oscillatory Circuits

Eric Miller; Timothy Sands

doi:doi:10.11648/j.cse.20210501.12

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Critical Commentary on Deterministic Artificial Intelligence Applied to Oscillatory Circuits

Eric Miller, Timothy Sands

Published in Control Science and Engineering (Volume 5, Issue 1)

Received: 12 August 2021 Accepted: 21 August 2021 Published: 31 August 2021

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Abstract

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.

Published in	Control Science and Engineering (Volume 5, Issue 1)
DOI	10.11648/j.cse.20210501.12
Page(s)	13-19
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

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

References

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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

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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

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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

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@article{10.11648/j.cse.20210501.12,
  author = {Eric Miller and Timothy Sands},
  title = {Critical Commentary on Deterministic Artificial Intelligence Applied to Oscillatory Circuits},
  journal = {Control Science and Engineering},
  volume = {5},
  number = {1},
  pages = {13-19},
  doi = {10.11648/j.cse.20210501.12},
  url = {https://doi.org/10.11648/j.cse.20210501.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cse.20210501.12},
  abstract = {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.},
 year = {2021}
}

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TY  - JOUR
T1  - Critical Commentary on Deterministic Artificial Intelligence Applied to Oscillatory Circuits
AU  - Eric Miller
AU  - Timothy Sands
Y1  - 2021/08/31
PY  - 2021
N1  - https://doi.org/10.11648/j.cse.20210501.12
DO  - 10.11648/j.cse.20210501.12
T2  - Control Science and Engineering
JF  - Control Science and Engineering
JO  - Control Science and Engineering
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EP  - 19
PB  - Science Publishing Group
SN  - 2994-7421
UR  - https://doi.org/10.11648/j.cse.20210501.12
AB  - 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.
VL  - 5
IS  - 1
ER  -

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Author Information

Eric Miller

Systems Engineering Program, Cornell University, Ithaca, USA
Timothy Sands

Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, USA

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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

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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

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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

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@article{10.11648/j.cse.20210501.12,
  author = {Eric Miller and Timothy Sands},
  title = {Critical Commentary on Deterministic Artificial Intelligence Applied to Oscillatory Circuits},
  journal = {Control Science and Engineering},
  volume = {5},
  number = {1},
  pages = {13-19},
  doi = {10.11648/j.cse.20210501.12},
  url = {https://doi.org/10.11648/j.cse.20210501.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cse.20210501.12},
  abstract = {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.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Critical Commentary on Deterministic Artificial Intelligence Applied to Oscillatory Circuits
AU  - Eric Miller
AU  - Timothy Sands
Y1  - 2021/08/31
PY  - 2021
N1  - https://doi.org/10.11648/j.cse.20210501.12
DO  - 10.11648/j.cse.20210501.12
T2  - Control Science and Engineering
JF  - Control Science and Engineering
JO  - Control Science and Engineering
SP  - 13
EP  - 19
PB  - Science Publishing Group
SN  - 2994-7421
UR  - https://doi.org/10.11648/j.cse.20210501.12
AB  - 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.
VL  - 5
IS  - 1
ER  -

Copy | Download