McCullough, Daniel Roger ORCID: https://orcid.org/0000-0002-1666-0965 (2021) Nonlinear Model Predictive Control of Autonomous Surface Vehicle in Rough Seas. PhD thesis, University of Sheffield.
Abstract
Autonomous Surface Vehicles (ASVs) offer the potential of performing dirty, dull, and dangerous
missions at sea, in an automated fashion. However, without consideration of the ocean environment
and the risk of wave-induced damage, the use of conventional ASVs is restricted to relatively calm
sea-states. Assuming a conventional underactuated ASV with only throttle and rudder inputs, this
thesis addresses the problem of controlling an ASV in an optimal fashion, while maintaining headway
towards a desired destination in any sea and with any sea state. This is a challenging problem
owing to the coupled, nonlinear nature of the vessel-wave dynamics, along with multiple competing
performance objectives, such as excessive motions, and actuation effort. The thesis shows that the
optimal solution, obtained via nonlinear model predictive control (NMPC), involves tacking at two
different timescales. This subsequently motivates the design of a two-degree of freedom controller,
consisting of a tacking planner that generates a long-term, optimal, heading and velocity reference,
and a feedback regulator that produce the optimal throttle and rudder commands to maintain this
reference, whilst minimizing wave-induced effects with smaller tacks. This thesis represents the first
work to formulate and solve the optimal control problem of navigating in rough seas, based upon a
coupled dynamic model of a 6-DOF vessel excited by waves. Closed-loop simulation results from a
high fidelity ocean-vessel model demonstrate significant reductions in excessive vessel motions and
forces, compared to a conventional path following PID controller for head and beam seas. Further
examinations show the superiority of the NMPC in a full sea state. The impact of the prediction
horizon parameters on the performance is investigated with a view towards an adaptive prediction
horizon. Lastly, the ability of the NMPC to use one set of Response Amplitude Operators (RAOs)
in other wave conditions without loss of performance is shown.
Metadata
Supervisors: | Rossiter, Anthony |
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Keywords: | Autonomous Surface Vehicle; Model Predictive Control; Rough Seas; Optimal Control |
Awarding institution: | University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Automatic Control and Systems Engineering (Sheffield) |
Identification Number/EthosID: | uk.bl.ethos.855690 |
Depositing User: | Mr. Daniel Roger McCullough |
Date Deposited: | 09 May 2022 10:08 |
Last Modified: | 01 Jun 2023 09:53 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:30698 |
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