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Intelligent control for surface vessels based on Kalman filter variants trained radial basis function neural networks

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Wang, Y ORCID: 0000-0002-6571-7825 2018 , 'Intelligent control for surface vessels based on Kalman filter variants trained radial basis function neural networks', PhD thesis, University of Tasmania.

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Abstract

For decades, there has been a significant increase in the demand of using a ship’s autopilot for complicated manoeuvres, such as maritime underway replenishment and sailing in constrained waters. In order to achieve these applications even in the presence of severe sea conditions, new control algorithms are required for the autopilots to control the underactuated ships. The study detailed in this thesis investigates the development of Radial Basis Function Neural Networks (RBFNN) based autopilot to satisfy the functionalities of course keeping, rudder roll damping, and path tracking. Two novel Kalman Filter Variants (KFV) based training algorithms, namely Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), were proposed to improve the performance of the autopilot in the aspects of compensating the effects of system nonlinearity and unpredictable external disturbances.
The primary emphasis of this study is in the design of autopilots, analysis of their performances, verification and validation through the experimental and numerical investigations. Considering the better generalisation ability and faster converge performance, modified EKF and UKF were proposed as the alternatives of the Back-Propagation (BP) training method for RBFNN controller to approximate the control law of the ship’s motions. The research splits into four phases. In first two phases, the capabilities of the proposed controllers, i.e., course keeping and path tracking controllers incorporating with roll damping controllers, were validated by adopting the mathematical model of a full scale ship with environmental disturbances. In order to enable both the experimental and numerical studies of proposed autopilots, the third phase focused on the modelling of the free running scaled model ‘Hoorn’, which was newly developed by utilising the embedded open-source hardware and low-cost sensors. In the last phase, the performances of course keeping and path tracking were investigated by conducting experiments using the physical model on Trevallyn Lake (Tasmania, Australia) and simulations using the developed mathematical model.
The simulation results of the full scale ship showed that both EKF RBFNN and UKF RBFNN based control schemes were feasible to maintain the ship advancing on desired course and trajectory while reducing the roll damping only use the rudder as the actuator. The free running tests and system identification were successfully implemented to develop the four Degree of Freedom mathematical model of ‘Hoorn’, which has been verified by the comparison between experimental data and simulation results. The following experimental and numerical studies showed that the presented signal processing methods were effectively employed to provide acceptable states estimation, while the KFV trained neural network controllers were adequately making the ship to follow the desired states in the presence of variable external disturbances. Consequently, the ship’s robustness and controllability in counteracting environmental disturbances were corroborated.
Based on the above-mentioned investigations, it is concluded that the developed control schemes could effectively determine the deflections of rudder to fulfil the proposed functionalities. The experiment results also demonstrated that the developed autopilots were assisted in effectively tracking desired states and enhancing the ship’s controllability with unpredictable disturbances. Moreover, in comparison with the EKF RBFNN based autopilot, the advantages of UKF RBFNN based autopilot consisted in the fast learning rate and smooth control law output while making the ship to meet the predefined requirements. Additionally, the experimental and simulated results have indicated that the developed control schemes have a great potential to be utilised commercially on marine vehicles, while the presented methods in developing free running model supplied a low-cost but efficient way to investigate the ship’s hydrodynamic characteristics and intelligent autopilot experimentally.

Item Type: Thesis - PhD
Authors/Creators:Wang, Y
Keywords: Neural Network Control, Kalman Filter Variants, Intelligent Autopilot System, Experimental Study, Modeling of Surface Vessel, Free Running Model Scaled Ship
Copyright Information:

Copyright 2017 the author

Additional Information:

Chapter 2 appears to be the equivalent of a post-print version of an article published as: Wang, Y., Nguyen, H. D., Chai, S., Khan, F., 2015. Radial basis function neural network based rudder roll stabilisation for ship sailing in waves, In 2015 5th Australian Control Conference (AUCC), 158-163, IEEE. © 2015 Engineers Australia. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Chapter 3B appears to be the equivalent of a post-print version of an article published as: Wang, Y., Chai, S., Khan, F., Nguyen, H. D., 2016. Unscented Kalman filter trained neural networks based rudder roll stabilisation system for ship in waves, Applied ocean research, 68, 26-38

Chapter 4 appears to be the equivalent of a post-print version of an article published as: Wang, Y., Chai, S. Nguyen, H. D., 2017. Modelling of a surface vessel from free running test using low-cost sensors, 3rd International Conference on Control, Automation and Robotics (ICCAR), 2017. IEEE © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Chapter 5B appears to be the equivalent of a post-print version of an article published as: Wang, Y., Nguyen, H. D., Chai, S., Khan, F., 2015. Unscented Kalman filter trained neural network control design for ship autopilot with experimental and numerical approaches. Applied ocean research, 85, 162-172

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