The modeling uncertainties and external unknown disturbances,among other factors,impose higher demands on the control methods for Autonomous Underwater Vehicle(AUV)in terms of trajectory tracking.The work aims to propose an AUV robust control method based on high-order recurrent neural networks to address it.High-order recurrent neural networks with simple structure but superior approximation performance were employed to estimate modeling uncertainties and exter-nal unknown disturbances,which were then compensated for in the input control law to enhance control performance.Subse-quently,the neural network weight adaptive update law and AUV adaptive control law were derived based on the HJI theory and Lyapunov stability analysis.Finally,a backstepping sliding mode method was designed as a comparative approach,and simula-tion experiments were conducted.The experimental results indicated that the proposed AUV robust control method based on high-order recurrent neural networks outperformed the backstepping sliding mode method in terms of tracking error,settling time,and other control metrics.Simulation experiments demonstrate that the proposed robust control method can effectively fa-cilitate precise target trajectory tracking by AUVs,while simultaneously exhibiting excellent control performance and robust-ness.This research provides an efficient and effective approach for AUV trajectory tracking control,with the potential for appli-cation in complex and uncertain underwater environments.
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