Adaptive Neural Network-Based Prescribed Performance Control of AUVs with Input Saturation

In view of system uncertainty and input saturation of autonomous undersea vehicles(AUVs), an improved adaptive neural network-based prescribed performance control strategy was proposed to track the desired trajectory. Firstly, the nonlinear transformation was introduced to ensure that the position error remained within the preset time-varying range, improving control accuracy. Based on backstepping and Lyapunov functions, a virtual control law for the system was designed. Then, the neural network technology was used to process the unknown parameters of the system model, and the real control law of the system was reconstructed, which simplified the traditional backstepping control strategy and effectively reduced the computational complexity. Then, based on the Lyapunov stability theory, all the error signals of the AUV system were confirmed to be bounded. Finally, compared with traditional dynamic surface control methods, the simulation results show that the proposed control strategy has better control performance and can effectively overcome the impact of uncertainty on system performance by considering input saturation, effectively tracking target trajectories.