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

Wang, Y, Chai, S ORCID: 0000-0001-5186-4456 and Nguyen, HD ORCID: 0000-0003-0118-8597 2019 , 'Unscented Kalman Filter trained neural network control design for ship autopilot with experimental and numerical approaches' , Applied Ocean Research, vol. 85 , pp. 162-172 , doi:

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In the recent decades, the application and research of unmanned surface vessels are experiencing considerablegrowth, 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 BasisFunction 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 weightstraining mechanism for the RBFNN based controller. The configurations of the newly developed free runningscaled 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 sailingconditions. The results indicated that the UKF RBFNN based autopilot satisfied the functionalities of coursekeeping, course changing and trajectory tracking only using the rudder as the actuator. It was concluded that thedeveloped control scheme was effective to track the desired states and robust against unpredictable externaldisturbances. 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 providingsmooth and effective control laws.

Item Type: Article
Authors/Creators:Wang, Y and Chai, S and Nguyen, HD
Keywords: intelligent autopilot, neural networks, Unscented Kalman Filter Training, free running model, experimental test
Journal or Publication Title: Applied Ocean Research
Publisher: Elsevier
ISSN: 0141-1187
DOI / ID Number:
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© 2019 Elsevier Ltd. All rights reserved.

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