In this paper, a neural network-based robust anti-sway control is proposed for a crane system transporting an underwater object. A dynamic model of the crane system is developed by incorporating hoisting dynamics, hydrodynamic forces, and external disturbances. Considering the various uncertain factors that interfere with accurate payload positioning in water, neural networks are designed to compensate for unknown parameters and unmodeled dynamics in the formulated problem. The neural network-based estimators are embedded in the anti-sway control algorithm, which improves the control performance against uncertainties. A sliding mode control with an exponential reaching law is developed to suppress the sway motions during underwater transportation. The asymptotic stability of the sliding manifold is proved via Lyapunov analysis. The embedded estimator prevents the conservative gain selection of the sliding mode control, thus reducing the chattering phenomena. Simulation results are provided to verify the effectiveness and robustness of the proposed control method.

中文翻译：

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基于神经网络的工业起重机在起重动力学和不确定水动力作用下的鲁棒防摇控制

在本文中，针对运输水下物体的起重机系统提出了一种基于神经网络的鲁棒防摇控制。起重机系统的动力学模型是通过结合起重动力学、水动力和外部干扰来开发的。考虑到干扰水中有效载荷准确定位的各种不确定因素，设计神经网络以补偿公式化问题中的未知参数和未建模的动力学。基于神经网络的估计器嵌入在抗摇摆控制算法中，提高了对不确定性的控制性能。开发了一种具有指数到达定律的滑模控制来抑制水下运输过程中的摇摆运动。通过李雅普诺夫分析证明了滑动流形的渐近稳定性。嵌入式估计器防止了滑模控制的保守增益选择，从而减少了抖动现象。仿真结果验证了所提出的控制方法的有效性和鲁棒性。