Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Autopilot System for Depth and Pitch Control in Underwater Vehicles: Navigating Near-Surface Waves and Disturbances

Vladimir Petrov, Gage MacLin, Venanzio Cichella

TL;DR

The paper tackles depth and pitch control of autonomous underwater vehicles (AUVs) in near-surface, wave-affected environments. It fuses an LQR autopilot with band-pass wave augmentation and an $\mathcal{L}_1$ adaptive augmentation to cancel wave disturbances and mitigate low-frequency uncertainties, validated on the Joubert BB2 reduced-order model. The approach yields robust tracking of depth commands $z_{\text{cmd}}(t)$ and pitch commands $\theta_{\text{cmd}}(t)$ while distributing control effort across actuators to minimize wear. Numerical simulations across two speed regimes and three control configurations demonstrate that the combined augmentation (Case 3) consistently improves depth convergence and reduces actuator loads, especially near-surface suction effects. The framework has practical implications for improved safety and navigation accuracy in marine applications such as geoscience, environmental monitoring, and security missions.

Abstract

This paper introduces a framework for depth and pitch control of underwater vehicles in near-surface wave conditions. By effectively managing tail, sail plane angles and hover tank operations utilizing a Linear Quadratic Regulator controller and L1 Adaptive Autopilot augmentation, the system ensures balanced control input distribution and significantly attenuates wave disturbances. This development in underwater vehicle control systems offers potential for improved functionality across a range of marine applications. The proposed framework is demonstrated to be robust in a variety of wave conditions, enabling more precise navigation and improved safety in operational scenarios. The effectiveness of this control strategy is validated through extensive simulations using the Joubert BB2 model.

Autopilot System for Depth and Pitch Control in Underwater Vehicles: Navigating Near-Surface Waves and Disturbances

TL;DR

The paper tackles depth and pitch control of autonomous underwater vehicles (AUVs) in near-surface, wave-affected environments. It fuses an LQR autopilot with band-pass wave augmentation and an adaptive augmentation to cancel wave disturbances and mitigate low-frequency uncertainties, validated on the Joubert BB2 reduced-order model. The approach yields robust tracking of depth commands and pitch commands while distributing control effort across actuators to minimize wear. Numerical simulations across two speed regimes and three control configurations demonstrate that the combined augmentation (Case 3) consistently improves depth convergence and reduces actuator loads, especially near-surface suction effects. The framework has practical implications for improved safety and navigation accuracy in marine applications such as geoscience, environmental monitoring, and security missions.

Abstract

This paper introduces a framework for depth and pitch control of underwater vehicles in near-surface wave conditions. By effectively managing tail, sail plane angles and hover tank operations utilizing a Linear Quadratic Regulator controller and L1 Adaptive Autopilot augmentation, the system ensures balanced control input distribution and significantly attenuates wave disturbances. This development in underwater vehicle control systems offers potential for improved functionality across a range of marine applications. The proposed framework is demonstrated to be robust in a variety of wave conditions, enabling more precise navigation and improved safety in operational scenarios. The effectiveness of this control strategy is validated through extensive simulations using the Joubert BB2 model.
Paper Structure (10 sections, 54 equations, 9 figures)

This paper contains 10 sections, 54 equations, 9 figures.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. LQR Autopilot
  4. $\mathcal{L}_1$ Augmentation
  5. Control law
  6. Controller performance bounds
  7. Numerical results
  8. Scenario 1 - Low Speed
  9. Scenario 2 - High Speed
  10. Conclusions

Figures (9)

  • Figure 1: The axes and variables used in the BB2 model
  • Figure 2: Controller’s Architecture
  • Figure 3: Scenario 1, Cases 1,2,3. Depth change. a - depth change for whole scenario; b - depth change for the specified location.
  • Figure 4: Scenario 1, Cases 1,2,3. $\theta$ change. a - $\theta$ change for whole scenario; b - $\theta$ change for the specified location.
  • Figure 5: Scenario 1, Cases 1,2,3. $\delta_{v}$, $\delta_{m}$ change for the specified location. a - $\delta_{v}$; b - $\delta_{m}$.
  • ...and 4 more figures