Modeling and Control of Unmanned Surface Vehicles: An Integrated Approach

This paper presents a comprehensive approach to augment the control performance of unmanned surface vehicles (USVs), addressing two core issues: dynamics modeling and control of USVs. To bolster the precision of dynamics modeling, the paper introduces a parameter identification algorithm based on the nonlinear multi-innovation least-squares method (NMILS). NMILS helps mitigate the noise influence and enhances the precision of the dynamics modeling. To further reinforce control performance, finite-time sliding mode control (FTSMC) is employed. FTSMC effectively counteracts the influence of identification errors, offering enhanced robustness against uncertainties and disturbances. The proposed techniques are validated on the Cybership I model. Simulation results revealed highly accurate parameter identification, with identified values for key parameters m
11
, m
22
, and m
33
closely matching the true values. Moreover, motion prediction with these identified parameters yielded minor errors, the largest spread being in e
u
with a maximum value of 0.047m/s. The effectiveness of the FTSMC control strategy was demonstrated through a path-following simulation. Notably, the maximum errors for x
e
and y
e
did not exceed 0.006m and 0.15m respectively, reinforcing the precision of the proposed approach.