Observer-Based Neural Formation Control of Leader–Follower AUVs With Input Saturation
Jinqiang Wang, Cong Wang, Yingjie Wei, Chengju Zhang
Abstract
This article investigates the three-dimensional formation tracking control problem of leader–follower autonomous underwater vehicles with input saturation under uncertain nonlinearities. For this purpose, a saturated formation tracking controller is developed by using the generalized saturation functions. This technique can effectively prevent actuators saturation and improve the transient performance of the presented control system. A nonlinear velocity observer is also designed to estimate the velocity signals of followers. Then, an estimation model based on the neural adaptive robust techniques is proposed to deal with the uncertain nonlinearities, such as unknown model dynamics, environmental disturbances, and approximation errors. A Lyapunov-based stability analysis is provided to guarantee that all signals of the closed-loop system are uniformly ultimately bounded. Finally, the reliability and robustness of the presented controller are demonstrated through simulations.