|Table of Contents|

 Yuanyuan Wang,Hung Duc Nguyen.Rudder Roll Damping Autopilot Using Dual Extended Kalman Filter-Trained Neural Networks for Ships in Waves[J].Journal of Marine Science and Application,2019,(4):510-521.[doi:10.1007/s11804-019-00111-8]
Click and Copy

Rudder Roll Damping Autopilot Using Dual Extended Kalman Filter-Trained Neural Networks for Ships in Waves


Rudder Roll Damping Autopilot Using Dual Extended Kalman Filter-Trained Neural Networks for Ships in Waves
Yuanyuan Wang Hung Duc Nguyen
Yuanyuan Wang Hung Duc Nguyen
National Center for Maritime Engineering and Hydrodynamics, Australian Maritime College, University of Tasmania, Launceston 7250, Australia
Rudder roll dampingAutopilotRadial basis functionNeural networksDual extended Kalman filter trainingIntelligent controlPath followingAdvancing in waves
The roll motions of ships advancing in heavy seas have severe impacts on the safety of crews, vessels, and cargoes; thus, it must be damped. This study presents the design of a rudder roll damping autopilot by utilizing the dual extended Kalman filter (DEKF)-trained radial basis function neural networks (RBFNN) for the surface vessels. The autopilot system constitutes the roll reduction controller and the yaw motion controller implemented in parallel. After analyzing the advantages of the DEKFtrained RBFNN control method theoretically, the ship’s nonlinear model with environmental disturbances was employed to verify the performance of the proposed stabilization system. Different sailing scenarios were conducted to investigate the motion responses of the ship in waves. The results demonstrate that the DEKF RBFNN-based control system is efficient and practical in reducing roll motions and following the path for the ship sailing in waves only through rudder actions.


Alar??n F (2007) Internal model control using neural network for ship roll stabilization. J Mar Sci Technol 15(2):141-147. https://doi.org/10.6119/JMST
Broomhead DS, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks. Royal Signals and Radar Establishment Malvern, London, 39
Choi J, Lima ACC, Haykin S (2005) Kalman filter-trained recurrent neural equalizers for time-varying channels. IEEE Trans Commun 53(3):472-480. https://doi.org/10.1109/TCOMM.2005.843416
De JRJ, Yu W (2007) Nonlinear system identification with recurrent neural networks and dead-zone Kalman filter algorithm.Neurocomputing 70(13-15):2460-2466. https://doi.org/10.1016/j.neucom.2006.09.004
Fang MC, Luo JH (2006) A combined control system with roll reduction and track keeping for the ship moving in waves. J Ship Res 50(4):344-354
Fang MC, Luo JH (2007) On the track keeping and roll reduction of the ship in random waves using different sliding mode controllers.Ocean Eng 34(3):479-488. https://doi.org/10.1016/j.oceaneng.2006.03.004
Fang MC, Zhuo YZ, Lee ZY (2010) The application of the self-tuning neural network PID controller on the ship roll reduction in random waves. Ocean Eng 37(7):529-538. https://doi.org/10.1016/j.oceaneng.2010.02.013
Fang MC, Lin YH, Wang BJ (2012) Applying the PD controller on the roll reduction and track keeping for the ship advancing in waves. Ocean Eng 54:13-25. https://doi.org/10.1016/j.oceaneng.2012.07.006
Fossen TI (1994) Guidance and control of ocean vehicles. Wiley, New York, 48, 302, 440
Fossen TI (2011) Handbook of marine craft hydrodynamics and motion control, vol 243. Wiley, New York, 433
Ge SS, Hang CC, Tao Z (1999) A direct method for robust adaptive nonlinear control with guaranteed transient performance. Syst Control Lett 37(5):275-284. https://doi.org/10.1016/S0167-6911(99)00032-8
Ge SS, Hang CC, Lee TH, Tao Z (2010) Stable adaptive neural network control. Springer, New York, 44
Goh SL, Mandic DP (2007) An augmented extended Kalman filter algorithm for complex-valued recurrent neural networks. Neural Comput 19(4):1039-1055. https://doi.org/10.1162/neco.2007.19.4.1039
Ko CN, Lee CM (2013) Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy 49:413-422. https://doi.org/10.1016/j.energy.2012.11.015
Li H, Guo C, Li X (2010) Ship roll stabilization using supervision control based on inverse model wavelet neural network. Proceedings of the 8th World Congress on Intelligent Control and Automation, Jinan, China, 4829-4833. https://doi.org/10.1109/WCICA.2010.5554721
Liu J (2013) Radial basis function (RBF) neural network control for mechanical systems:design, analysis and Matlab simulation.Springer, New York, 58-60
McGookin EW, Murray-Smith DJ, Li Y, Fossen TI (2000) Ship steering control system optimisation using genetic algorithms. Control Eng Pract 8(4):429-443. https://doi.org/10.1016/S0967-0661(99)00159-8
Medagam PV, Pourboghrat, F (2009) Optimal control of nonlinear systems using RBF neural network and adaptive extended Kalman filter. Proceedings of the American Control Conference 2009, Hyatt Regency Riverfront, USA, 355-360. https://doi.org/10.1109/ACC.2009.5160105
Nejim S (2000) Rudder roll damping system for ships using fuzzy logic control. Proceedings of the OCEANS 2000 MTS/IEEE Conference and Exhibition, Providence, USA, 1137-1143. https://doi.org/10.1109/OCEANS.2000.881755
Nouri K, Dhaouadi R, Braiek NB (2008) Adaptive control of a nonlinear dc motor drive using recurrent neural networks. Appl Soft Comput 8(1):371-382. https://doi.org/10.1016/j.asoc.2007.03.002
Oda H, Ohtsu K, Sato H, Kanehiro K (2008) Designing advanced rudder roll stabilization system. Proceedings of the 7th JFPS International Symposium on Fluid Power, Toyama, Japan, 169-174
Perez T, Blanke M (2012) Ship roll damping control. Annu Rev Control 36(1):129-147. https://doi.org/10.1016/j.arcontrol.2012.03.010
Sanchez EN, Alanís AY, Loukianov AG (2008) Discrete-time high order neural control:trained with Kalman filtering, vol 112. Springer, New York, 332
Sun B, Zhu D, Yang SX (2014) A bioinspired filtered backstepping tracking control of 7000-m manned submarine vehicle. IEEE Trans Ind Electron 61(7):3682-3693. https://doi.org/10.1109/TIE.2013.2267698
Treakle TW, Mook DT, Liapis SI, Nayfeh AH (2000) A time-domain method to evaluate the use of moving weights to reduce the roll motion of a ship. Ocean Eng 27(12):1321-1343. https://doi.org/10.1016/S0029-8018(99)00051-7
Van AJ, Van NLH (1978) Optimum steering of ships with an adaptive autopilot. Proceedings of the Fifth Ship Control Systems Symposium, Annapolis, USA, 8-17
Wang X, Huang Y (2011) Convergence study in extended Kalman filterbased training of recurrent neural networks. IEEE Trans Neural Netw 22(4):588-600. https://doi.org/10.1109/TNN.2011.2109737
Wang Y, Chai S, Khan F, Nguyen HD (2015) Radial basis function neural network based rudder roll stabilization for ship sailing in waves.Proceedings of the 5th Australian Control Conference, Gold coast, Australia, 256-261
Wang Y, Chai S, Khan F, Nguyen HD (2017a) Unscented Kalman filter trained neural networks based rudder roll stabilization system for ship in waves. Appl Ocean Res 68:26-38. https://doi.org/10.1016/j.apor.2017.08.007
Wang Y, Chai S, Nguyen HD (2017b) Modelling of a surface vessel from free running test using low cost sensors. Proceedings of the 3rd International Conference on Control, Automation and Robotics, Nagoya, Japan, 299-303. https://doi.org/10.1109/ICCAR.2017.7942707
Zhang XK, Jin YC, Yang C, Zhang L (2006) A kind of robust rudder rolldamping system. Paper presented at the Systems and Control in Aerospace and Astronautics. Proceedings of the 1st International Symposium on Systems and Control in Aerospace and Astronautics, Harbin, China, 1151-1154. https://doi.org/10.1109/ISSCAA.2006.1627570
Zhao J, Zhu X, Wang W, Liu Y (2013) Extended Kalman filter-based Elman networks for industrial time series prediction with GPU acceleration. Neurocomputing 118:215-224. https://doi.org/10.1016/j.neucom.2013.02.031


Received date:2018-06-05;Accepted date:2019-03-22。
Foundation item:This research is a part of the project titled ‘Intelligent Control for Surface Vessels Based on Kalman Filter Variants Trained Radial Basis Function Neural Networks’ partially funded by the Institutional Grants Scheme (TGRS 060515) of Tasmania, Australia.
Corresponding author:Yuanyuan Wang,Yuanyuan.Wang@utas.edu.au
Last Update: 2020-02-04