Zeroing Neural Network‐Based Controller Design and Robustness Analysis for Underwater Manipulator Systems

This paper addresses the challenge of precise trajectory tracking and disturbance rejection for underwater manipulators operating in complex environments. To this end, a robust tracking control framework based on a zeroing neural network (ZNN) is proposed. Systematically, the ZNN‐based architecture is designed with the system error, enabling direct compensation of nonlinear dynamics, such as Coriolis, centrifugal, and damping forces, as well as external disturbances. Theoretically, rigorous stability and robustness analysis are given to guarantee the asymptotic convergence for the underwater manipulator system. Finally, the framework is validated through comprehensive numerical simulations under composite reference trajectories and perturbation scenarios. Results confirm that the proposed controller achieves superior tracking accuracy, faster convergence, and stronger robustness, thereby enhancing the reliability and effectiveness of the underwater manipulator system in demanding applications such as deep‐sea exploration and intervention.