Data-driven iterative learning cooperative trajectory tracking control for multiple autonomous underwater vehicles with input saturation constraints

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.