Diving dynamics identification and motion prediction for marine crafts using field data

Ensuring accurate parameter identification and diving motion prediction of marine crafts is essential for safe navigation, optimized operational efficiency, and the advancement of marine exploration. Addressing this, this paper proposes an instrumental variable-based least squares (IVLS) algorithm. Firstly, aiming to balance complexity with accuracy, a decoupled diving model is constructed, incorporating nonlinear actuator characteristics, inertia coefficients, and damping coefficients. Secondly, a discrete parameter identification matrix is designed based on this dedicated model, and then a IVLS algorithm is innovatively derived to reject measurement noise. Furthermore, the stability of the proposed algorithm is validated from a probabilistic point of view, providing a solid theoretical foundation. Finally, performance evaluation is conducted using four depth control datasets obtained from a piston-driven profiling float in Qiandao Lake, with desired depths of 30 m, 40 m, 50 m, and 60 m. Based on the diving dynamics identification results, the IVLS algorithm consistently shows superior performance when compared to recursive weighted least squares algorithm and least squares support vector machine algorithm across all depths, as evidenced by lower average absolute error (AVGAE), root mean square error (RMSE), and maximum absolute error values and higher determination coefficient ($R^2$). Specifically, for desired depth of 60 m, the IVLS algorithm achieved an AVGAE of 0.553 m and RMSE of 0.655 m, significantly outperforming LS-SVM with AVGAE and RMSE values of 8.782 m and 11.117 m, respectively. Moreover, the IVLS algorithm demonstrates a remarkable generalization capability with $R^2$ values consistently above 0.95, indicating its robustness in handling varied diving dynamics.