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Feed-forward Disturbance Compensation for Station Keeping in Wave-dominated Environments

Kyle L. Walker, Adam A. Stokes, Aristides Kiprakis, Francesco Giorgio-Serchi

Abstract

When deploying robots in shallow ocean waters, wave disturbances can be significant, highly dynamic and pose problems when operating near structures; this is a key limitation of current control strategies, restricting the range of conditions in which subsea vehicles can be deployed. To improve dynamic control and offer a higher level of robustness, this work proposes a Cascaded Proportional-Derivative (C-PD) with Feed-forward (FF) control scheme for disturbance mitigation, exploring the concept of explicitly using disturbance estimations to counteract state perturbations. Results demonstrate that the proposed controller is capable of higher performance in contrast to a standard C-PD controller, with an average reduction of ~48% witnessed across various sea states. Additional analysis also investigated performance when considering coarse estimations featuring inaccuracies; average improvements of ~17% demonstrate the effectiveness of the proposed strategy to handle these uncertainties. The proposal in this work shows promise for improved control without a drastic increase in required computing power; if coupled with sufficient sensors, state estimation techniques and prediction algorithms, utilising feed-forward compensating control actions offers a potential solution to improve vehicle control under wave-induced disturbances.

Feed-forward Disturbance Compensation for Station Keeping in Wave-dominated Environments

Abstract

When deploying robots in shallow ocean waters, wave disturbances can be significant, highly dynamic and pose problems when operating near structures; this is a key limitation of current control strategies, restricting the range of conditions in which subsea vehicles can be deployed. To improve dynamic control and offer a higher level of robustness, this work proposes a Cascaded Proportional-Derivative (C-PD) with Feed-forward (FF) control scheme for disturbance mitigation, exploring the concept of explicitly using disturbance estimations to counteract state perturbations. Results demonstrate that the proposed controller is capable of higher performance in contrast to a standard C-PD controller, with an average reduction of ~48% witnessed across various sea states. Additional analysis also investigated performance when considering coarse estimations featuring inaccuracies; average improvements of ~17% demonstrate the effectiveness of the proposed strategy to handle these uncertainties. The proposal in this work shows promise for improved control without a drastic increase in required computing power; if coupled with sufficient sensors, state estimation techniques and prediction algorithms, utilising feed-forward compensating control actions offers a potential solution to improve vehicle control under wave-induced disturbances.
Paper Structure (12 sections, 19 equations, 7 figures, 2 tables)

This paper contains 12 sections, 19 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Modelling
  3. Vehicle Rigid-Body Dynamics
  4. Wave-Induced Disturbances
  5. Control Methodology
  6. State Estimation
  7. Scenario Configuration
  8. Simulation Results
  9. Station Keeping Performance
  10. Sensitivity to Noise
  11. Station Keeping Efficiency
  12. Conclusions

Figures (7)

  • Figure 1: The two frames, earth-fixed and body-fixed, and the relating transformation for the BlueROV2 Heavy configuration.
  • Figure 2: Block diagram of the proposed feed-forward disturbance mitigation technique, with the grey blocks representing the generation of the additional compensating control actions.
  • Figure 3: Map showing the (a) various locations of buoys around Scotland, circling the buoy selected for this analysis and (b) an enlarged map of the Moray Firth buoy location.
  • Figure 4: Temporal segment of the (a) wave in case W3. showing the (b) surge position, (c) heave position and (d) pitch attitude.
  • Figure 5: Temporal segment for case W3 during noise analysis; here, SNR$_{0}$ is the encountered wave by the vehicle where as SNR$_{15}$ is the expected wave by the controller used to generate compensating control actions.
  • ...and 2 more figures