Trajectory tracking control of a Remotely Operated Underwater Vehicle based on Fuzzy Disturbance Adaptation and Controller Parameter Optimization

TL;DR

This paper presents a novel control framework for a remotely operated underwater vehicle that combines fuzzy disturbance adaptation and particle swarm optimization to achieve precise trajectory tracking in challenging under-ice environments.

Contribution

It introduces a MIMO nonlinear control system with Lyapunov stability, fuzzy logic-based adaptation, and PSO-tuned parameters, advancing autonomous underwater exploration technology.

Findings

Simulation results show improved trajectory tracking accuracy.

Fuzzy adaptation enhances disturbance rejection in nonlinear dynamics.

PSO tuning optimizes controller performance.

Abstract

The exploration of under-ice environments presents unique challenges due to limited access for scientific research. This report investigates the potential of deploying a fully actuated Remotely Operated Vehicle (ROV) for shallow area exploration beneath ice sheets. Leveraging advancements in marine robotics technology, ROVs offer a promising solution for extending human presence into remote underwater locations. To enable successful under-ice exploration, the ROV must follow precise trajectories for effective localization signal reception. This study develops a multi-input-multi-output (MIMO) nonlinear system controller, incorporating a Lyapunov-based stability guarantee and an adaptation law to mitigate unknown environmental disturbances. Fuzzy logic is employed to dynamically adjust adaptation rates, enhancing performance in highly nonlinear ROV dynamic systems. Additionally, a Particle Swarm Optimization (PSO) algorithm automates the tuning of controller parameters for optimal trajectory tracking. The report details the ROV dynamic model, the proposed control framework, and the PSO-based tuning process. Simulation-based experiments validate the efficacy of the methodology, with experimental results demonstrating superior trajectory tracking performance compared to baseline controllers. This work contributes to the advancement of under-ice exploration capabilities and sets the stage for future research in marine robotics and autonomous underwater systems.