Abstract

To solve the problem of speed and robustness of small Unmanned Surface Vehicle (USV) heading control in the inland water navigation environment, the paper designed a pod propulsion USV heading control system based bipolar fuzzy controller. The system comprises of heading controller, steering mechanism, electronic compass and rudder angle measuring instrument. Heading controller includes two fuzzy controllers, which can meet the demands of large-angle steering control and small-angle course keeping. The simulation results show that the control system is more to meet the conditions of inland navigation than fuzzy PID autopilot.

Introduction

Inland USV is mainly used in search and rescue, emergency, cruise, measurement etc. Due to the large density of ship navigation, channel bending, shallow narrow leg more, meteorology, hydrology and other environmental complexity of inland waters, so the requirements of USV heading control flexibility and robustness are higher.

In the USV heading control, there is reported and literatures at home and abroad. The PID controller's structure is simple, easy to use, and applied on ships heading control at first, but the lack of ability to adapt to vessels working conditions and environment, and its performance is not satisfactory (Cheng and Huang, 1997). Yang and Yu (1999) designed a robust PID control law autopilot, and it has robustness to some extent in ship speed-changing and with disturbance. Based on generalized predictive theory, Peng, Wu and Liu (2014) designed a GPC-PID cascade controller with good control accuracy and robustness. It was noted (Healey and Lienard, 1993; Song, Li and Chen, 2003;) sliding mode controller to achieve the control of the ship's heading or track, but it didn't solve the high frequency chattering phenomenon. Zhang, Lv and Guo (2007) combined the neural network structure and the closed loop gain shaping algorithm to form a closed loop control system, with strong robustness, and the algorithm was simple and has clear physical meaning. Using the method of adaptive Backstepping, Godhavn, Fossen and Berge (1998) designed a nonlinear controller for keeping ship's course system with a good control effect. And an integral term was added to improve the course-keeping performance of the control system (Fossen and Storand, 1999). The neural network approach was combined with an adaptive Backstepping method (Xu, Li and Li, 2012).

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