Modeling and Control of a Remotely Operated Underwater Vehicle: a Nonlinear Model Predictive Control Approach

Draghetti, Alessandro (2023) Modeling and Control of a Remotely Operated Underwater Vehicle: a Nonlinear Model Predictive Control Approach. [Laurea magistrale], Università di Bologna, Corso di Studio in Automation engineering / ingegneria dell’automazione [LM-DM270], Documento full-text non disponibile
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The primary goal of this thesis is to design a mathematical model for the Centauro Remotely Operated Underwater Vehicle and to develop a control system for path following. In this work, a six degrees of freedom mathematical model of the ROV is presented, with a specific emphasis on the thrusters and hydrodynamic coefficients. This model is essential for analyzing the dynamic behavior of the ROV and for tuning the controller that handles the ROV's motion. The main contributions are the controllers that address path following objectives: the Nonlinear Model Predictive Control (NMPC) and the Nonlinear Model Predictive Contour Control (NMPCC). The NMPC is a model-based nonlinear optimal control technique used to solve trajectory tracking tasks. To handle path following problems, the NMPC is combined with a guidance system that calculates the progress speed based on the distance between the ROV and the path. This information allows the guidance system to provide the reference trajectory for the NMPC. In contrast, the NMPCC merges trajectory planning and tracking into a single optimization problem. This controller computes the optimal inputs that maximize the progress along the path while minimizing the contour error. To demonstrate the effectiveness and robustness of the two controllers, a comparison is made with a classic control technique, Sliding Mode Control (SMC) in the Super Twisting version. Simulations on Simulink show the significant performance degradation of the SMC when actuator saturations and constraints occur, as it cannot explicitly handle them. In contrast, the NMPC and NMPCC demonstrate greater effectiveness in solving path following problems and handling actuator saturations, constraints, and measured disturbances.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Draghetti, Alessandro
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
ROV Model,Nonlinear Model Predictive Control,Model Predictive Control,Nonlinear Model Predictive Contour Control,Model Predictive Contour Control,MPC,MPCC,Sliding Mode Control,Super Twisting Algorithm,Remotely Operated Vehicle
Data di discussione della Tesi
22 Marzo 2023

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