Neural adaptive sliding mode controller for unmanned surface vehicle steering system

Lili Wan,Binghua Shi,Yixin Su,Huajun Zhang,Yongchuan Tang

doi:10.1177/1687814018795523

Abstract

Unmanned surface vehicle has the properties such as complexity, nonlinearity, time variability, and uncertainty, which lead to the difficulty of obtaining a precise kinematics model. A neural adaptive sliding mode controller for the unmanned surface vehicle steering system is developed based on the sliding mode control technique and the radial basis function neural network. In the new approach, two parallel radial basis function neural networks are used to reduce the influence of the system uncertainties and eliminate the dependency of the controller on the precise kinematics model of the system. Among these two radial basis function neural networks, one is used to approximate the unknown nonlinear yaw dynamics and the other is used to adjust the control gain as well as realize the variable gain sliding mode control. The weights of the two neural networks are trained online using the sliding surface variable and the control, where the Lyapunov method is used to derive the adaptive laws to ensure the stability of the whole closed-loop system. The proposed adaptive controller is suitable for the steering control at different cruising speeds with bounded external disturbances. The simulation results show that the proposed controller has a good control performance regarding the smooth control, fast response, and high accuracy.

Highlights

Unmanned surface vehicles (USVs) attract the attention of many countries and become a hot research spot in the field of marine equipment because of its low labor cost and strong maneuverability
In order to develop an efficient steering system for the nonlinear course control problem of USV with parameter uncertainties and external disturbances, a neural adaptive Sliding mode controller (SMC) based on radial basis function (RBF) neural networks (NNs) is designed in this article
For the characteristics of nonlinearity, uncertainty, and complexity of USV, a variable gain adaptive SMC based on RBF NN is designed in this article

Summary

Introduction

Unmanned surface vehicles (USVs) attract the attention of many countries and become a hot research spot in the field of marine equipment because of its low labor cost and strong maneuverability. Sliding mode controller (SMC) has the outstanding advantages in robustness, anti-disturbance, and response speed.[30] It is used in the autopilot design of USV.[31,32,33] An adaptive sliding mode control algorithm and nonlinear disturbance observer method were developed for course-keeping maneuvers in vessel steering by Liu.[31] It provides a robust performance against the environmental disturbance and the uncertainty in the rudder dynamics. The NN which can effectively solve the problems with regard to unknown nonlinear systems attracts many concerns from many researchers.[17,18,19,20,21,22,23,24,39,40,41,42] Using the universal approximation property of the NNs, the influence of the external disturbance and uncertainty can be reduced, and the controller becomes independent of the precise system model information.[39] The neural adaptive SMCs are designed to improve the robustness of the system and reduce the system chattering.[40,41,42]

Results

Conclusion