Trajectory Tracking Control for Underactuated USV with Prescribed Performance and Input Quantization

This paper is devoted to the problem of prescribed performance trajectory tracking control for symmetrical underactuated unmanned surface vessels (USVs) in presence model uncertainties and input quantization. By combining backstepping filter mechanisms adaptive algorithms, two robust architectures are investigated surge motion yaw motion. To guarantee prespecified requirements position control, constrained error dynamics transformed unconstrained ones by virtue a tangent-type nonlinear mapping function. On other hand, inaccurate can be identified through radial basis neural networks (RBFNNs), where minimum learning parameter (MLP) algorithm employed with low computational complexity. Furthermore, quantization errors effectively reduced even when parameters quantizer remain unavailable designers. Finally, effectiveness proposed controllers verified via theoretical analyses numerical simulations.