Thesis

Advanced model predictive control for three-dimensional motion control of autonomous underwater vehicles

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Awarding institution
  • University of Strathclyde
Date of award
  • 2025
Thesis identifier
  • T17284
Person Identifier (Local)
  • 202262421
Department, School or Faculty
Abstract
  • The increasing demand for ocean-related activities, driven by needs such as environmental preservation, offshore renewable energy deployment, border security and weather forecasting, highlights the importance of underwater operations. With minimal human intervention, autonomous underwater vehicles (AUVs) are increasingly employed to execute missions in water bodies. Improved AUV motion reliability requires advanced controllers to cope with challenges posed by nonlinear dynamics, coupled motion, actuator limits and environmental disturbances. This thesis aims to foster the use of Model Predictive Control (MPC) for AUV motion control, leveraging its capability to optimise the performance of both linear and nonlinear systems while accounting for system and operational constraints. Standard MPC uses the receding horizon strategy to offer inherent robustness under minor uncertainties. However, the effectiveness of AUV motion control in the marine environment can degrade under substantial ocean currents and wave disturbances. Moreover, the full-order nonlinear AUV model is complex, rendering it less appealing for MPC design due to the associated online computational cost. As a result, this thesis proposes formulating the full-order nonlinear AUV model as a linear parameter-varying (LPV) system. This makes obtaining a convex optimisation control problem possible, which can be efficiently solved using off-the-shelf solvers. Building on the overall research goals discussed in Chapter 1, this thesis introduces the mathematical model of an AUV in Chapter 2 and highlights issues impacting its use in motion control design. Chapter 3 provides a state-of-the-art review of advanced predictive control methods to underscore the significance of this work. This thesis proposes three main design approaches, leveraging the LPV model, to address the effects of disturbances across various motion control tasks. The first approach resulted in two novel MPC algorithms introduced in Chapter 4, both based on velocity models that use increment variables to counteract the effects of disturbances. The first controller, LPVMPC1, is designed for dynamic positioning, while the second controller, LPVMPC2, is developed for combined dynamic positioning and trajectory tracking control of AUVs. The LPVMPC2 integrates a planning scheme to facilitate a seamless transition from the trajectory tracking task to dynamic positioning. In the LPVMPC2 design, persistent AUV operation is ensured by maintaining continuous functionality even when reference signals include unreachable positions that violate the AUV workspace constraints. The second approach, presented in Chapter 5, utilises the tube-based method for a robust tube-based MPC (TMPC) design to achieve resilience against environmental disturbances. The TMPC employs a line-of-sight (LOS) local trajectory replanning strategy to mitigate input saturation effects, enabling the consideration of realistic magnitude and rate constraints on input signals. An optimal state dependent feedback controller is proposed to construct time-varying tubes to ensure the perturbed AUV system remains within a tube centred around the nominal trajectory. The TMPC framework is computationally tractable as it requires the online solution of a convex quadratically constrained quadratic problem. The third MPC approach is presented in Chapter 6, which introduces an enclosure based LOS guidance system and a robust min-max MPC (MM-MPC) for AUV path-following. By using the vehicle’s desired heading angle to generate reference linear and angular position coordinates, the need to formulate an AUV error model is bypassed. The simplicity of the LOS guidance system is then leveraged to develop a multi-objective LOS guidance system (MO-LOSGS) to ensure collision-free navigation amidst static obstacles. The MM-MPC is designed to stabilise the AUV speed for time- and energy-efficient navigation. The high computational cost that had limited the application of MM-MPC is mitigated by developing a duality-based transformation strategy to reformulate the problem into a quadratic minimisation control problem. All simulation validations of the developed controllers are performed using a realistic Naminow-D AUV manufactured by Mitsubishi Heavy Industries Ltd. The concluding chapter offers a summary of key research contributions to the development of advanced MPC techniques for AUV motion control and proposes potential avenues for future research. Key Words: Model Predictive Control; Dynamic Modelling; Autonomous Underwater Vehicles; Dynamic Positioning; Trajectory Tracking; Path-Following; Robust Control; Convex Optimisation.
Advisor / supervisor
  • Yue, Hong
  • Grimble, Michael J.
Resource Type
Date Created
  • 2024
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