Article
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Preserved in Portico This version is not peer-reviewed
Discerning Discretization for UUV DC Motor Control
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Version 1
: Received: 23 December 2022 / Approved: 27 December 2022 / Online: 27 December 2022 (10:35:46 CET)
A peer-reviewed article of this Preprint also exists.
Menezes, J.; Sands, T. Discerning Discretization for Unmanned Underwater Vehicles DC Motor Control. J. Mar. Sci. Eng. 2023, 11, 436. Menezes, J.; Sands, T. Discerning Discretization for Unmanned Underwater Vehicles DC Motor Control. J. Mar. Sci. Eng. 2023, 11, 436.
Abstract
Discretization is the process of converting a continuous function or model or equation into discrete steps. In this work, adaptive and learning methods are implemented to control DC motors that are used for actuating control surfaces of unmanned underwater vehicles. Adaptive control is a method in which the controller is designed to adapt the system with parameters which vary or are uncertain. Parameter estimation is the process of computing the parameters of a system using a model & measured data. Adaptive methods have been used in conjunction with different parameter estimation techniques. Deterministic artificial intelligence, a learning-based approach that uses the process dynamics defined by physics, is also applied to control the output of the DC motor to track a specified trajectory. This work goes further to evaluate the performance of the adaptive & learning techniques based on different discretization methods. The results are evaluated based on the absolute error mean between the output & the reference trajectory and the standard deviation of the error. The first order-hold method of discretization and surprisingly large sample time of seven tenths of a second yields over sixty percent improvement over the results presented in the prequel literature.
Keywords
Discretization; DC motors; deterministic artificial intelligence; adaptive control; learning control; proportional derivative; estimation; least squares; modeling
Subject
Engineering, Marine 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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