Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Evaluation of Adaptive and Learning in Unmanned Systems

Version 1 : Received: 26 February 2022 / Approved: 1 March 2022 / Online: 1 March 2022 (11:15:37 CET)

A peer-reviewed article of this Preprint also exists.

Koo, S.M.; Travis, H.; Sands, T. Impacts of Discretization and Numerical Propagation on the Ability to Follow Challenging Square Wave Commands. J. Mar. Sci. Eng. 2022, 10, 419. Koo, S.M.; Travis, H.; Sands, T. Impacts of Discretization and Numerical Propagation on the Ability to Follow Challenging Square Wave Commands. J. Mar. Sci. Eng. 2022, 10, 419.

Abstract

This study determines the threshold for the computational rate of actuator motor controllers for unmanned underwater vehicles necessary to accurately follow discontinuous square wave commands. Motors must track challenging square-wave inputs, and identification of key computational rates permit application of deterministic artificial intelligence (D.A.I.) to achieve tracking to a machine-precision degree of accuracy in direct comparison to other state-of-art approaches. All modeling approaches are validated in MATLAB simulations where the motor process is discretized at varying step-sizes (inversely proportional to computational rate). At a large step-size (fast computational rate), discrete D.A.I. shows a mean error more than three times larger than that of a ubiquitous model-following approach. Yet, at a smaller step size (slower computational rate), the mean error decreases by a factor of 10, only three percent larger than that of continuous D.A.I. Hence, the performance of discrete D.A.I. is critically affected by the sampling period for discretization of the system equations and computational rate. Discrete D.A.I. should be avoided when small step-size discretization is unavailable. In fact, continuous D.A.I. has surpassed all modeling approaches which makes it the safest and most viable solution to future commercial applications in unmanned underwater vehicles.

Keywords

Autonomous surface vehicles (ASV); autonomous underwater vehicle (AUV); Control and guidance; nonlinear control; deterministic artificial intelligence (D.A.I.); model-following; R.L.S.; marine actuators

Subject

Engineering, Control and Systems Engineering

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