Adaptive Trajectory Tracking Optimization for ROVs Based on RLS Online Identification Under Varying Water Depth Conditions

Remotely operated vehicles (ROVs) suffer from severe navigation trajectory optimization problems in variable water-depth environments, as near-wall hydrodynamic effects cause synchronous scaling drift of added mass and damping coefficients. This parameter variation leads to obvious model mismatch in traditional fixed-gain controllers and seriously deteriorates ROV trajectory tracking accuracy. To address the scale-type parameter mismatch issue, this paper proposes an adaptive trajectory tracking control strategy combining forgetting-factor recursive least squares (RLS) online identification and periodic linear quadratic regulator (LQR) gain scheduling. A closed-loop coupling framework is established to estimate the discrete state-space matrices of ROVs via the RLS algorithm, and the optimal feedback gains are updated every 50 sampling steps to adapt to time-varying hydrodynamic characteristics. Three typical water-depth scenarios with different parameter mismatch degrees are set up for sinusoidal trajectory tracking simulations, adopting PID and fixed-parameter MPC as comparison methods. The results indicate that the proposed method maintains comparable steady-state performance with fixed-parameter MPC under nominal conditions, and reduces the two-dimensional trajectory RMSE by 8.4% and 57.4% under moderate and severe parameter mismatch conditions, respectively. A critical mismatch threshold of fixed-parameter MPC compensation capability is also determined. This study provides a feasible technical reference for high-precision adaptive motion control of ROVs in variable-depth water environments.