This paper investigates the cooperative trajectory tracking (CTT) control problem of multiple autonomous underwater vehicles (AUVs). The multi‐AUV system is characterized by uncertain dynamics, being subjected to the impact about input saturation constraints and unmeasurable disturbances. First, a neural network‐based data‐driven control algorithm is proposed for the multi‐AUV system with unmeasurable disturbances and model parameters uncertain. The radial basis function neural network is employed to estimate the primary pseudo parameters of an equivalent data model, established through dynamic linearization methods. Subsequently, an iterative learning control approach based on adaptive gain is designed to act as a feedforward scheme along the iteration axis to enhance the tracking accuracy within a time constraint. Third, to prove that the resulting CTT control system fulfills the bounded stability under the proposed control approach, a formal stability analysis is provided. Finally, a simulation case study is conducted to illustrate the effectiveness of the proposed CTT control approach.

中文翻译：

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具有输入饱和约束的多个自主水下航行器的数据驱动迭代学习协作轨迹跟踪控制

本文研究了多个自主水下航行器（AUV）的协作轨迹跟踪（CTT）控制问题。多AUV系统具有动态不确定性的特点，受到输入饱和约束和不可测扰动的影响。首先，针对具有不可测扰动和模型参数不确定的多AUV系统，提出了一种基于神经网络的数据驱动控制算法。采用径向基函数神经网络来估计通过动态线性化方法建立的等效数据模型的主要伪参数。随后，设计了一种基于自适应增益的迭代学习控制方法，作为沿迭代轴的前馈方案，以提高时间约束内的跟踪精度。第三，为了证明所得到的 CTT 控制系统在所提出的控制方法下满足有限稳定性，提供了正式的稳定性分析。最后，通过仿真案例研究来说明所提出的 CTT 控制方法的有效性。