AUV hydrodynamic coefficient offline identification based on deep reinforcement learning

System identification (SI) is a research focus in the field of autonomous underwater vehicles (AUV) which can be considered as the estimation to the hydrodynamic coefficients (HCs) in nonlinear dynamic equations of the AUV. This paper presents an SI method based on deep reinforcement learning (DRL) to estimate the HCs of AUVs only through the utilization of position and altitude information of the AUV. Compared with most existing methods, fewer design parameters are required in the proposed SI method, thereby making the SI method simpler to perform. First, a sensitivity analysis of the HCs is performed, and then the action and reward mechanism in the origin deep deterministic policy gradient (DDPG) algorithm is improved according to the results of sensitivity analysis. The simulation results show that the HCs can be estimated with a low error level and greater accuracy than the other methods. Therefore, it can be concluded that the DRL SI method possesses effective performance in HC estimation and the improved-DDPG algorithm is more suitable for SI than the original one.