Adaptive Neural Network Control of Underactuated Surface Vessels With Guaranteed Transient Performance: Theory and Experimental Results

Lepeng Chen,Weisheng Yan,Rongxin Cui,Chenguang Yang

doi:10.1109/tie.2019.2914631

Abstract

In this paper, an adaptive trajectory tracking control algorithm for underactuated unmanned surface vessels (USVs) with guaranteed transient performance is proposed. To meet the realistic dynamical model of USVs, we consider that the mass and damping matrices are not diagonal and the input saturation problem. Neural networks (NNs) are employed to approximate the unknown external disturbances and uncertain hydrodynamics of USVs. Moreover, both full-state feedback control and output feedback control are presented, and the unmeasurable velocities of the output feedback controller are estimated via high-gain observer. Unlike the conventional control methods, we employ the error transformation function to guarantee the transient tracking performance. Both simulation and experimental results are carried out to validate the superior performance via comparing with traditional potential integral control approaches.

Highlights

In the last decades, unmanned surface vessels (USVs) plays an important role in monitoring, exploration, surveillance and military applications
Several control approaches have been proposed to relieve the effect of unknown disturbances and model uncertainties, such as sliding mode control [1]–[3], adaptive backstepping control [4]–[9], Neural Networks (NNs)-based control [10]–[13], and neural learning control [14]–[16], model predictive control [17], [18], data-driven based control [19]–[21]
The adaptive NN control is one of the most promising tools to improve the tracking performance of USVs that affected by the model uncertainties and disturbances

Summary

Introduction

USV plays an important role in monitoring, exploration, surveillance and military applications. In [10], both full-state and output feedback adaptive neural control were proposed for USVs, and asymmetric barrier Lyapunov function was used to achieve output constraint. Based on the previous work, [14] presented an radial basis function (RBF) neural learning output feedback controller to steer an USV without velocity measurements. The above-mentioned control schemes achieve good performance to address the problem of model uncertainties, external disturbances, unavailable velocity measurements, etc. Among these control techniques, the adaptive NN control is one of the most promising tools to improve the tracking performance of USVs that affected by the model uncertainties and disturbances

Objectives

Methods

Results

Conclusion