Adaptive prescribed performance tracking control for underactuated autonomous underwater vehicles with input quantization

This paper investigates the adaptive prescribed performance trajectory tracking control problem for underactuated underwater vehicles subjected to unmodeled hydrodynamics, ocean disturbances and input quantization. The controller is synchronized through the command filter-based backstepping design and minimum learning parameter algorithm, thus the adverse effect of explosion of complexity and computational complexity inherent in neural network is avoided. To endow tracking errors with prescribed performance guarantees, a mapping function is applied such that the constrained control problem could be transformed to the unconstrained one. By resorting to the hysteresis quantizer, the frequency of data transmission is considerably reduced and the quantization errors are effectively reduced under the proposed control scheme. Through Lyapunov stability analysis, it is verified that the proposed method is capable of ensuring asymptotic stability for tracking errors. Numerical simulation results reveal the advantage and effectiveness of this work.

Automated summary

This paper investigates the adaptive prescribed performance trajectory tracking control problem for underactuated underwater vehicles and proposes a control method based on command filter-based backstepping design and minimum learning parameter algorithm, effectively avoiding the complexity and computational complexity issues inherent in neural networks.