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Unscented Kalman Filter trained neural network control design for ship autopilot with experimental and numerical approaches

Citation

Wang, Y and Chai, S and Nguyen, HD, Unscented Kalman Filter trained neural network control design for ship autopilot with experimental and numerical approaches, Applied Ocean Research, 85 pp. 162-172. ISSN 0141-1187 (2019) [Refereed Article]

Copyright Statement

2019 Elsevier Ltd. All rights reserved.

DOI: doi:10.1016/j.apor.2019.01.030

Abstract

In the recent decades, the application and research of unmanned surface vessels are experiencing considerable growth, which have caused the demands of intelligent autopilots to grow along with the ever-growing requirements. In this study, the design of an autopilot based on Unscented Kalman Filter (UKF) trained Radial Basis Function Neural Networks (RBFNN) was presented. In particular, in order to provide satisfactory control performance for surface vessels with random external disturbances, the modified UKF was utilised as the weights training mechanism for the RBFNN based controller. The configurations of the newly developed free running scaled model, as well as the online signal processing method, were introduced to enable the experimental studies. The experimental and numerical tests were carried out through using the physical scaled model and corresponding mathematical model to validate the capability of the designed control system under various sailing conditions. The results indicated that the UKF RBFNN based autopilot satisfied the functionalities of course keeping, course changing and trajectory tracking only using the rudder as the actuator. It was concluded that the developed control scheme was effective to track the desired states and robust against unpredictable external disturbances. Moreover, in comparison with Back-Propagation (BP) RBFNN and Proportional-Derivative (PD) based autopilots, the UKF RBFNN based autopilot has the comparable capability in the aspects of providing smooth and effective control laws.

Item Details

Item Type:Refereed Article
Keywords:intelligent autopilot, neural networks, Unscented Kalman Filter Training, free running model, experimental test
Research Division:Engineering
Research Group:Maritime Engineering
Research Field:Marine Engineering
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Engineering
UTAS Author:Wang, Y (Mr Yuanyuan Wang)
UTAS Author:Chai, S (Professor Shuhong Chai)
UTAS Author:Nguyen, HD (Dr Hung Nguyen)
ID Code:134565
Year Published:2019
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2019-08-20
Last Modified:2019-09-09
Downloads:0

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