Discerning Discretization for Unmanned Underwater Vehicles DC Motor Control

Abstract

Discretization is the process of converting a continuous function or model or equation into discrete steps. In this work, learning and adaptive techniques are implemented to control DC motors that are used for actuating control surfaces of unmanned underwater vehicles. Adaptive control is a strategy wherein the controller is designed to adapt the system with parameters that vary or are uncertain. Parameter estimation is the process of computing the parameters of a system using a model and measured data. Adaptive methods have been used in conjunction with different parameter estimation techniques. As opposed to the ubiquitous stochastic artificial intelligence approaches, very recently proposed deterministic artificial intelligence, a learning-based approach that uses the physics-defined process dynamics, 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 and learning techniques based on different discretization methods. The results are evaluated based on the absolute error mean between the output and 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 greater than sixty percent improvement over the results presented in the prequel literature.