A nonlinear model predictive controller for remotely operated underwater vehicles with disturbance rejection

Yu Cao*, Boyang Li, Qian Li, Adam Stokes, David Ingram, Aristides Kiprakis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Remotely Operated underwater Vehicles (ROVs) are growing in importance in the ocean environment for observation and manipulation tasks, particularly when used to maintain offshore energy and offshore renewable energy assets. Many such tasks require the dynamic positioning of ROV in challenging sea conditions with multiple disturbances resulting from the effects of waves, currents and turbulence. This work presents a novel, nonlinear, model predictive dynamic positioning controller that accounts for such complex stochastic disturbances. These external disturbances are modelled as 6-degree of freedom forces and moments within the nonlinear ROV dynamic and propulsion model. A nonlinear model predictive dynamic positioning strategy based on the nonlinear model predictive control (NMPC) is proposed for the disturbance rejection in this work. A numerical water tank model is used to test the performance of the strategy using hardware in-the-loop simulation. The results of the simulation have been compared against baseline proportional-integral-derivative (PID) and linear quadratic regulator (LQR) controllers tested under wave and current conditions in the FloWave basin. A quantitative comparison of the controllers is presented. The resulting controller is shown to maintain a small root mean squared error (RMSE) in position when subjected to multiple directional disturbance, with minimal control effort. This study contributes an important insight on future theoretical design of model predictive disturbance rejection controllers and illustrates their practical implementation on real hardware.
Original languageEnglish
Pages (from-to)158622-158634
Number of pages13
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 31 Aug 2020

Keywords

  • Dynamic positioning
  • Offshore renewable energy
  • Nonlinear models
  • Model predictive control
  • Control design
  • Remotely operated vehicles
  • Underwater vehicles

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