Ship Steering Control Based on Quantum Neural Network

Wei Guan,Chao Zhao,Zuojing Su,Haotian Zhou,Xianku Zhang

doi:10.1155/2019/3821048

Abstract

During the mission at sea, the ship steering control to yaw motions of the intelligent autonomous surface vessel (IASV) is a very challenging task. In this paper, a quantum neural network (QNN) which takes the advantages of learning capabilities and fast learning rate is proposed to act as the foundation feedback control hierarchy module of the IASV planning and control strategy. The numeric simulations had shown that the QNN steering controller could improve the learning rate performance significantly comparing with the conventional neural networks. Furthermore, the numeric and practical steering control experiment of the IASV BAICHUAN has shown a good control performance similar to the conventional PID steering controller and it confirms the feasibility of the QNN steering controller of IASV planning and control engineering applications in the future.

Highlights

In the past decade, the research on intelligent automatic surface ship (IASV) technology in academic and marine industries has continued to grow. ese developments have been fuelled by advanced sensing, communication, and computing technology together with the potentially transformative impact on automotive sea transportation and perceived social and economic bene ts [1,2,3,4,5]. e ship planning and control strategy for IASV, shown in Figure 1, which based on a module-based hierarchical structure, would be a good navigation strategy
To solve the issues and obtain better performance, various advanced control strategies have been proposed for the steering control of the ship in recent years, such as adaptive steering control strategy [6,7,8], steering control strategy based on fuzzy logic algorithm [9, 10], steering controller based on Backstepping controller design method [11,12,13], and adaptive backstepping method [14, 15]. e robust control schemes such as the sliding mode control method [16, 17] and H∞ robust control algorithm [18] are utilized in the ship steering control to achieve better ship course keeping and changing manoeuver
A three layer 2-5-1 quantum neural network (QNN) model was constructed. e structure of the QNN steering control system is shown in Figure 4. e two inputs of the QNN steering controller are the heading deviation Δψ(k) and yaw rate r(k), respectively, and the output is the command steering angle δQr NN(k). e difference between the QNN steering controller outputs and PID course keeping controller outputs is defined as the system error. e mean square of the system error (MSE) is defined as the performance evaluation function of the proposed QNN to evaluate the performance of the QNN learning performance and optimized targets

Summary

Introduction

The research on intelligent automatic surface ship (IASV) technology in academic and marine industries has continued to grow. ese developments have been fuelled by advanced sensing, communication, and computing technology together with the potentially transformative impact on automotive sea transportation and perceived social and economic bene ts [1,2,3,4,5]. e ship planning and control strategy for IASV, shown in Figure 1, which based on a module-based hierarchical structure, would be a good navigation strategy. A quantum neural network (QNN) for ship steering control is proposed to address the ship steering control problem based on the IASV planning and control concept. Hearn et al proposed an online course control neural network to improve the conventional PID steering control effects, but the slow convergence of the ship steering controller based on the neural network is still a big problem to be solved [20]. In order to overcome the shortcomings of the conventional neural network, a ship steering controller based on the QNN is proposed in this paper under the concept of quantum computing [21,22,23].

IASV Mathematical Model

QNN Steering Controller Design

Simulations and Analysis

Conclusions