###### Abstract  
Since AUV works in the complex marine environment without cable and unmanned, there will be a loss of contact when an accident occurs. It is necessary to carry out research on the drift track prediction of AUV for the sake of salvage and recovery of the AUV in time. It is worth noting that the volume of AUV is small, and the drift track changes significantly when it is affected by the marine environment. Consequently, when the AUV drifts to different ocean layers, there will be a feature drift problem which will lead to a significant drop in the prediction accuracy. In this paper, a new method of AUV drift track prediction is proposed. Inspired by the human emotion modulation mechanism in psychology, a modified neural network (ECRNet) is proposed to correct the prediction error in different ocean layers. Through experimental verification, the network reduces the prediction error and achieves a better prediction performance. emotional modulation mechanism; AUV drift track prediction; modified neural network; ECRNet 2022122169 122312169 122312169 122312169 122312169 122312169 1  
on a commercially available ROV, and the provided dataset can be used as a validation experiment as well as for testing and development of an algorithm for position control of comparable ROVs. [PERSON] et al. [9] studied the effect of cross section geometry on the magnitude and phase of wave-induced linear load experienced by AUVs when operating near the water surface. It is found that the AUV heave force is proportional to the plane area of the rectangle, and the shape of the cross section of the body of revolution changes to square, rectangular, or asymmetric shape, which affects the wave load. Besides, in terms of position control of underwater vehicles, there are also many articles which aim to improve the accuracy of AUV navigation and control system by estimating and effectively compensating for the disturbances caused by waves, etc. [PERSON] et al. [10] proposed a nonlinear disturbance estimator that can be perfectly integrated with the volume Kalman filter (CKF) for disturbance compensation in order to reduce the deviation caused by random environmental disturbances such as current and waves. Simulation experiments are carried out for the uncertainty of the dynamic model and the randomness of external disturbances such as current and wave, and it is proven that the method can effectively estimate disturbances and improve the accuracy of navigation. [PERSON] et al. [11] conducted experiments in a wave tank with ROV to verify the use of linear wave theory (LWT) to capture the time history of forces and moments caused by swell, heave, and pitch waves. The results show that compared with the standard PD controller, the force and torque generated by waves on ROV can be predicted more accurately and nearly in real time, which lays a foundation for developing model-based predictive control strategies that are more suitable for operation in harsh environments. [PERSON] et al. [12] studied how to improve the backstepping and nonlinear PD controller by introducing an adaptive law to automatically adjust the gain of the observer in the case of external disturbance and parameter uncertainty of unknown current and other factors. Real time experiments show that the algorithm is effective for trajectory tracking tasks in a variety of scenes. [PERSON] [13] proposed a nonsingular terminal sliding mode control method based on a finite time disturbance observer to improve the anti-interference ability of the system, aiming at the problem of accurate tracking control of underwater vehicle trajectory under the interference of complex marine environmental factors. The simulation experiment with MATLAB shows that the proposed method can achieve accurate trajectory tracking and provide solutions to related problems. [PERSON] et al. [14] point out that it is easy for the AUV to be interfered with by the external environment (waves, currents, etc. ), so a method based on active disturbance rejection control (ADRC) is proposed.