Data-Driven Coefficient Estimation for Autonomous Underwater Vehicle Depth Subsystem

Autonomous Underwater Vehicles (AUVs) play a key role in modern marine exploration. The effective deployment of AUVs relies on accurately determining their dynamics, which can be modeled using system identification approaches. Data-driven approaches for system identification of AUV sub-systems is an open research area. In this paper, we present a data-driven approach for system identification of the depth subsystem of AUVs using Physics Informed Neural Networks (PINNs) and Sparse Identification of Non-Linear Dynamics (SINDy). The AUV depth subsystem is excited with two different input profiles and the resulting states are used for system identification. Both SINDy and PINN show high accuracy in the estimated coefficient values with noisy measurements. The root mean square error (RMSE) and the mean absolute error (MAE) quantify the closeness between the actual states and those obtained from the system using the estimated coefficient values. In addition, the percentage error in the identified coefficient values is reported. The results highlight that PINNs obtain the most remarkable performance with multi-step inputs, whereas SINDy excels with sine inputs. For multi-step input, PINN yields MSE and RMSE values of the order 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> for states, whereas SINDy achieves values around of the order 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> for sinusoidal input.