Article
Version 1
Preserved in Portico This version is not peer-reviewed
Adaptive and Learning Methods for Drone Motor Control
and
*
Version 1
: Received: 27 January 2023 / Approved: 2 February 2023 / Online: 2 February 2023 (02:08:57 CET)
How to cite: Barnett, B.; Sands, T. Adaptive and Learning Methods for Drone Motor Control. Preprints 2023, 2023020022. https://doi.org/10.20944/preprints202302.0022.v1 Barnett, B.; Sands, T. Adaptive and Learning Methods for Drone Motor Control. Preprints 2023, 2023020022. https://doi.org/10.20944/preprints202302.0022.v1
Abstract
This manuscript explores unmanned aerial vehicle DC motor control performance efficacy of deterministic artificial intelligence in comparison to model-following adaptation, particularly a direct self-tuner with filtering. The deterministic artificial intelligence model made use of self-awareness statements to overcome error in response to permutations of the multi-duty cycle square wave that served as the system input. It can be seen that (despite equivalently powerful estimation techniques) deterministic artificial intelligence provided far superior results: a reduction in peak initial transient error of 55%, and a mean error reduction over 81% with over 65% reduction in error standard deviation compared to a state-of-the-art nonlinear adaptive control method. Deterministic artificial intelligence also was able to very closely track at the switching of the input control, while the benchmark nonlinear adaptive control failed to respond as quickly at these points.
Keywords
drones; artificial intelligence for control; autonomous unmanned aerial vehicles (UAVs); design and modeling; antenna; nonlinear adaptive control; self-tuning regulators
Subject
Engineering, Control and Systems Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
