Article
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Preserved in Portico This version is not peer-reviewed
Autonomous Drone Electronics Amplified With Pontryagin–Based Optimization
Version 1
: Received: 2 April 2023 / Approved: 3 April 2023 / Online: 3 April 2023 (05:31:58 CEST)
A peer-reviewed article of this Preprint also exists.
Xu, J.; Sands, T. Autonomous Drone Electronics Amplified with Pontryagin-Based Optimization. Electronics 2023, 12, 2541. Xu, J.; Sands, T. Autonomous Drone Electronics Amplified with Pontryagin-Based Optimization. Electronics 2023, 12, 2541.
Abstract
In the era of electrification and artificial intelligence, direct current motors are widely utilized with numerous innovative adaptive and learning methods. Traditional methods utilize model-based algebraic techniques with system identification, such as recursive least squares, extended least squares, and autoregressive moving averages. The new method known as deterministic artificial intelligence asserts physical-based process dynamics to achieve target trajectory tracking. There are two common autonomous trajectory generation algorithms: sinusoidal function and Pontryagin's based generation algorithm. This thesis aims to simulate model-following and deterministic artificial intelligence methods using sinusoidal and Pontryagin's methods and compare their performance difference when following challenging step function slew maneuver.
Keywords
Systems Engineering; Path Planning; deterministic artificial intelligence; System Identification; DC-motor; Least Square; Autonomous Trajectory Generation
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.
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