Hydrodynamics Model Identification and Model-Based Control Application of a New Type of AUV

The hydrodynamic coefficients of underwater robots can be used to evaluate their maneuverability and motion stability and to design motion controllers, thereby reducing experimental time and costs. In this paper, an Autonomous Underwater Vehicle (AUV) with a negative-lift profile is designed. The spatially constrained motion method, combined with neural networks, is utilized to identify all the hydrodynamic coefficients in the standard hydrodynamic equations of the AUV. Subsequently, based on the goodness-of-fit, the significance of the hydrodynamic coefficients is evaluated to yield a simplified hydrodynamic equation. Given the cost constraints, it was not feasible to obtain precise experimental data on hydrodynamic coefficients to validate the accuracy of the CFD calculation method. Therefore, the hydrodynamic coefficients were used to construct a dynamic model for the AUV, and an MPC controller was designed based on this model. Finally, simulations and pool tests were conducted on the AUV, and a comparative analysis of the simulation results with the pool test results revealed that although there were certain errors in the calculation of the hydrodynamic coefficients, the controller constructed within this margin of error was still capable of effectively controlling the AUV. This fully demonstrates the feasibility and applicability of using CFD methods to calculate hydrodynamic coefficients and establishing model predictive control methods based on these coefficients in practical applications.