Robust Fixed-Time 3D Trajectory Tracking Control for Underactuated AUVs Based on a Fixed-Time SSNN Disturbance Observer

Abstract

To address the problem of accurate tracking control for underactuated autonomous underwater vehicles (AUVs), a robust fixed-time control strategy is proposed to make AUVs converge to the desired reference trajectory in a fixed time. First, a new asymmetric barrier function is proposed to improve the tracking accuracy by constraining the errors to a defined range. Second, a fixed-time self-structuring neural network disturbance observer is proposed to estimate the external disturbances and guarantee that the observer errors are fixed-time convergent. The number of neurons can be optimized to reduce the computational burden. Third, a nonlinear first-order filter is employed to solve the effect of “complexity explosion” in the backstepping control method. Finally, the input quantizer is employed to reduce chattering and updating frequency on the premise of ensuring control accuracy. It is proven that the tracking error of the AUV can converge to the residual set in a fixed time, and the simulation results not only verify the validity of the method but also demonstrate the superiority of the method.

Keywords

• Fixed-time control
• self-structuring neural network disturbance observer
• input quantization
• underactuated autonomous underwater vehicles
• 3D trajectory tracking