A model predictive control approach is proposed for path following of underactuated surface ships with input saturation, parameters uncertainties, and environmental disturbances. An Euler iterative algorithm is used to reduce the calculation amount of model predictive control. The matter of input saturation is addressed naturally and flexibly by taking advantage of model predictive control. The mathematical model group (MMG) model as the internal model improves the control accuracy. A radial basis function neural network is also applied to compensate the total unknowns including parameters uncertainties and environmental disturbances. The numerical simulation results show that the designed controller can force an underactuated ship to follow the desired path accurately in the case of input saturation and time-varying environmental disturbances including wind, current, and wave.

中文翻译：

---

基于神经网络模型预测控制的欠驱动水面舰船路径跟踪

针对输入饱和、参数不确定和环境扰动的欠驱动水面舰船的路径跟踪，提出了一种模型预测控制方法。采用欧拉迭代算法，减少模型预测控制的计算量。通过利用模型预测控制，可以自然而灵活地解决输入饱和问题。数学模型组（MMG）模型作为内模，提高了控制精度。还应用径向基函数神经网络来补偿包括参数不确定性和环境干扰在内的总未知数。