Improved Integral LOS Guidance and Path-Following Control for an Unmanned Robot Sailboat via the Robust Neural Damping Technique

摘要

This paper introduces a scheme for waypoint-based path-following control for an Unmanned Robot Sailboat (URS) in the presence of actuator gain uncertainty and unknown environment disturbances. The proposed scheme has two components: intelligent guidance and an adaptive neural controller. Considering upwind and downwind navigation, an improved version of the integral Line-Of-Sight (LOS) guidance principle is developed to generate the appropriate heading reference for a URS. Associated with the integral LOS guidance law, a robust adaptive algorithm is proposed for a URS using Radial Basic Function Neural Networks (RBF-NNs) and a robust neural damping technique. In order to achieve a robust neural damping technique, one single adaptive parameter must be updated online to stabilise the effect of the gain uncertainty and the external disturbance. To ensure Semi-Global Uniform Ultimate Bounded (SGUUB) stability, the Lyapunov theory has been employed. Two simulated experiments have been conducted to illustrate that the control effects can achieve a satisfactory performance.