NARX modeling and simulation of heave dynamics with application of robust control of an underactuated underwater vehicle

This article, which is an extension of the previous work of the authors (Tanveer and Ahmad (2022)) on yaw dynamics, investigates the modeling and control of heave degree-of-freedom of a compact custom designed ROV. Wherein, nonlinear data-driven modeling strategy is adopted to develop a high-fidelity heave dynamic model. The proposed modeling approach uses open-loop real-time experimental data to derive a high-fidelity NARX model of the vehicle. The resulting model accommodates the dynamics of the system in addition to the tether dynamics. The advantage of this approach is its ability to eliminate the need for intricate controller tuning. The identified model consistently demonstrated fitness scores ranging from 82% to 92% in both self-validation and cross-validation tests conducted on distinct datasets. This relative advantage is exemplified in real-time through the testing of a Genetic Algorithm Proportional-Integral (GAPI) controller. The performance of GAPI is subsequently compared with the relatively recent Marine Predators Algorithm (MPA) and the more conventional root-locus tuned PI controllers. The experimental results demonstrate that GAPI provides the most favorable response, achieving a 35%, 76%, and 44% improvement in rise time, percent overshoot and peak time, respectively. Furthermore, the controller effort required by GAPI running on an ATmega328 chipset is 22% less than its counterparts.