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Ship Heading Control with Speed Keeping via a Nonlinear Disturbance Observer

Published online by Cambridge University Press:  22 January 2019

Zhiquan Liu Open the ORCID record for Zhiquan Liu [Opens in a new window] ,
Xiaoyang Lu  and
Diju Gao
Zhiquan Liu*
Affiliation:
(Key Laboratory of Marine Technology and Control Engineering Ministry of Communications, Shanghai Maritime University, Shanghai 201306China)
Xiaoyang Lu
Affiliation:
(Key Laboratory of Marine Technology and Control Engineering Ministry of Communications, Shanghai Maritime University, Shanghai 201306China)
Diju Gao
Affiliation:
(Key Laboratory of Marine Technology and Control Engineering Ministry of Communications, Shanghai Maritime University, Shanghai 201306China)
*
(E-mail: liuzhiquan215@sina.com)
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Abstract

The control problem for a ship steering system with speed loss is discussed in this paper. Two methods are proposed to deal with the unknown bounded disturbance for a sliding mode controller applied to a nonlinear surface vessel heading control system. The system uncertainties caused by speed changes are taken as internal disturbances, while the wave moments are considered as external disturbances. A feedback linearization method is adopted to simplify the nonlinear system. An adaptive method and a Nonlinear Disturbance Observer (NDO) are proposed for course keeping manoeuvres and speed keeping in vessel steering and provide robust performance for time varying wave disturbance and actuator dynamics. Furthermore, the overall stability conditions of the proposed controllers are analysed by Lyapunov's direct method. Finally, simulation results using the characteristics of a naval vessel illustrate the effectiveness of the presented control algorithms.

Keywords

Course keepingSpeed lossSliding mode controlNDO
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 4 , July 2019 , pp. 1035 - 1052
Copyright
Copyright © The Royal Institute of Navigation 2019 

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References

REFERENCES

Alfardo-Cid, E., McGookin, E.W., Murray-Smith, D.J. and Fossen, T.I. (2005). Genetic algorithms optimization of decoupled sliding mode controllers: simulated and real results. Control Engineering Practice, 13(6), 739–748.10.1016/j.conengprac.2004.07.004Google Scholar
Arribas, F.P. (2007). Some methods to obtain the added resistance of a ship advancing in waves. Ocean Engineering, 34(7), 946–955.10.1016/j.oceaneng.2006.06.002Google Scholar
Armstrong, V.N. (2013). Vessel optimisation for low carbon shipping. Ocean Engineering, 73, 195–207.10.1016/j.oceaneng.2013.06.018Google Scholar
Banazadeh, A. and Ghorbani, M.T. (2013). Frequency domain identification of Nomoto model to facilitate Kalman filter estimation and PID heading control of a patrol vessel. Ocean Engineering, 72, 344–355.10.1016/j.oceaneng.2013.07.003Google Scholar
Bhattacharyya, R. (1978). Dynamics of marine vehicles. John Wiley & Sons, New York.Google Scholar
Borkowski, P. (2014). Ship course stabilization by feedback linearization with adaptive object model. Polish Maritime Research, 21(81), 14–19.10.2478/pomr-2014-0003Google Scholar
Burns, R.S. (1995). The use of artificial neural networks for the intelligent optimal control of surface ships. IEEE Journal of Oceanic Engineering, 20(1), 65–72.10.1109/48.380245Google Scholar
Chen, W.H. (2004). Disturbance observer-based control for nonlinear systems. IEEE Transactions on Mechatronics, 9(4), 706–710.10.1109/TMECH.2004.839034Google Scholar
Do, K.D. and Pan, J. (2006). Global robust adaptive path following of underactuated ships. Automatica, 42(10), 1713–1722.10.1016/j.automatica.2006.04.026Google Scholar
Do, K.D. (2015). Robust adaptive tracking control of underactuated ODINs under stochastic sea loads. Robotics and Autonomous System, 72, 152–163.10.1016/j.robot.2015.05.007Google Scholar
Du, J., Hu, X., Krstic, M. and Sun, Y. (2016). Robust dynamic positioning of ships with disturbances under input saturation. Automatica, 73, 207–214.10.1016/j.automatica.2016.06.020Google Scholar
Fang, M.C. and Luo, J.H. (2005). The nonlinear hydrodynamic model for simulating a ship steering in waves with autopilot system. Ocean Engineering, 32(11), 1486–1502.10.1016/j.oceaneng.2004.09.008Google Scholar
Fang, M.C. and Luo, J.H. (2006). A combined control system with roll reduction and track keeping for the ship moving in waves. Journal of Ship Research, 50(4), 344–354.Google Scholar
Fang, M.C. and Luo, J.H. (2007). On the tracking and roll reduction of the ship in random waves using different sliding mode controllers. Ocean Engineering, 34(3), 479–488.10.1016/j.oceaneng.2006.03.004Google Scholar
Fang, M.C., Lin, Y.H., and Wang, B.J. (2012). Applying the PD controller on the roll reduction and track keeping for the ship advancing in waves. Ocean Engineering, 54(4), 13–25.10.1016/j.oceaneng.2012.07.006Google Scholar
Fossen, T.I. (2011). Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley Press, West Sussex.10.1002/9781119994138Google Scholar
Grimble, M.J. and Katebi, M.R. (1986). LQG design of ship steering control systems. Signal Processing for Control, Lecture Notes in Control and Information Sciences, 79, 387–413.10.1007/BFb0008200Google Scholar
Harl, N. and Balakrishnan, S.N. (2012). Impact time angle guidance with sliding mode control. IEEE Transactions on Control System Technology, 20(6), 1436–1449.10.1109/TCST.2011.2169795Google Scholar
Healey, A.J. and Lienard, D. (1993). Multivariable sliding mode control for autonomous diving and steering of unmanned underwater vehicles. IEEE Journal of Oceanic Engineering. 18(3), 327–339.10.1109/JOE.1993.236372Google Scholar
Huang, Y.J., Kuo, T.C. and Chang, S.H. (2013). Adaptive sliding mode control for nonlinear systems with uncertain parameters. IEEE Transactions on Systems, Man and Cybernetics. B Cybernetics, 38(2), 534–539.10.1109/TSMCB.2007.910740Google Scholar
Kim, S.S., Kim, S.D., Kang, D., Lee, J., Lee, S.J. and Jung, K.H. (2015). Study on variation in ship's forward speed under regular waves depending on rudder controller. International Journal of Naval Architecture and Ocean Engineering, 7(2), 364–374.10.1515/ijnaoe-2015-0025Google Scholar
Li, Z. and Sun, J. (2012). Disturbance compensating model predictive control with application to ship heading control. IEEE Transactions on Control Systems Technology, 20(1), 257–265.Google Scholar
Lei, Z. and Guo, C. (2015). Disturbance rejection control solution for ship steering system with uncertain time delay. Ocean Engineering, 95, 78–83.10.1016/j.oceaneng.2014.12.001Google Scholar
Li, H., Liu, J., Hilton, C. and Liu, H. (2013). Adaptive sliding mode control for nonlinear active suspension vehicle systems using T-S fuzzy approach. IEEE Transactions on Industrial Electronics, 60(8), 3328–3338.10.1109/TIE.2012.2202354Google Scholar
Lin, C.M., Hsueh, C.S. and Chen, C.H. (2014). Robust adaptive backstepping control for a class of nonlinear systems using recurrent wavelet neural network. Neurocomputing, 142, 372–382.10.1016/j.neucom.2014.04.023Google Scholar
Liu, W., Sui, Q., Xiao, H. and Zhou., F. (2011). Sliding backstepping control for ship course with nonlinear disturbance observer. Journal of Information and Computation Science, 8(14), 3809–3817.Google Scholar
Liu, Z. and Jin, H. (2013). Extended radiated energy method and its application to a ship roll stabilisation control system. Ocean Engineering, 72(7), 25–30.10.1016/j.oceaneng.2013.06.009Google Scholar
Liu, Z., Jin, H., Grimble, M.J. and Katebi, R. (2014). Ship roll stabilization control with low speed loss. MTS/IEEE Oceans'14, Taipei, Taiwan.Google Scholar
Liu, C., Sun, J. and Zou, Z. (2015). Integrated line of sight and model predictive control for path following and roll motion control using rudder. Journal of Ship Research, 59(2), 99–112.10.5957/JOSR.59.2.140057Google Scholar
Liu, Y.C., Liu, S.Y. and Wang, N. (2016). Fully tuned fuzzy neural network robust adaptive tracking control of unmanned under water vehicle with thruster dynamics. Neurocomputing, 196, 1–13.10.1016/j.neucom.2016.02.042Google Scholar
Liu, Z., Jin, H., Grimble, M.J. and Katebi, R. (2016). Ship forward speed loss minimization using nonlinear course keeping and roll motion controllers. Ocean Engineering, 113, 201–207.10.1016/j.oceaneng.2015.11.010Google Scholar
Loukakis, T.A. and Sclavounos, P.D. (1978). Some extensions of the classical approach to strip theory of ship motions, including the calculation of mean added forces and moments. Journal of Ship Research, 22, 1–19.Google Scholar
MARIN (2014). http://www.marin.nl/web/Research-Topics/Offshore-operations/Motion-monitoring.htm.Google Scholar
Miloh, T. and Pachter, M. (1989). Ship collision-avoidance and pursuit-evasion differential games with speed-loss in a turn. Computers Mathematics with Application, 18(1), 77–100.10.1016/0898-1221(89)90126-0Google Scholar
Moreira, L., Fossen, T.I. and Soares, C.G. (2007). Path following control system for a tanker ship model. Ocean Engineering, 34(12), 2074–2085.10.1016/j.oceaneng.2007.02.005Google Scholar
Peng, Z., Wang, D., Chen, Z., Hu, X. and Lan, W. (2013). Adaptive dynamic surface control for formations of autonomous surface vehicles with uncertain dynamics. IEEE Transactions on Control System Technology, 21(2), 513–520.10.1109/TCST.2011.2181513Google Scholar
Perez, T. (2005). Ship Motion Control: Course Keeping and Roll Reduction Using Rudder and Fins, Springer Press, London.Google Scholar
Perera, L. and Soares, C.G. (2012). Pre-filtered sliding mode control for nonlinear ship steering associated with disturbances. Ocean Engineering, 51(3), 49–62.10.1016/j.oceaneng.2012.04.014Google Scholar
Perera, L.P. and Soares, C.G. (2013). Lyapunov and Hurwitz based controls for input output linearization applied to nonlinear vessel steering. Ocean Engineering, 66, 58–68.10.1016/j.oceaneng.2013.04.002Google Scholar
Prpic-Orsic, J. and Faltinsen, O.M. (2012). Estimation of ship speed loss and associated CO2 emissions in a sea way. Ocean Engineering, 44(1), 1–10.10.1016/j.oceaneng.2012.01.028Google Scholar
Qin, z., Lin, Z., Sun, H. and Yang, D. (2016). Sliding mode control of path following for underactuated ships based on high gain observer. Journal of Central South University, 23(10), 3356–3364.10.1007/s11771-016-3401-9Google Scholar
Roberts, G.N. (2008). Trends in marine control systems. Annual Review in Control, 32(2), 263–269.10.1016/j.arcontrol.2008.08.002Google Scholar
Saari, H. and Djemai, M. (2012). Ship motion control using multi-controller structure. Ocean Engineering, 55(4), 184–190.10.1016/j.oceaneng.2012.07.028Google Scholar
Shojaei, K. (2015). Neural adaptive robust control of underactuated marine surface vehicles with input saturation. Applied Ocean Research, 53, 267–278.10.1016/j.apor.2015.09.010Google Scholar
Tzeng, C.Y. (1999). An internal model control approach to the design of yaw rate control ship steering autopilot. IEEE Journal of Oceanic Engineering, 24(4), 507–513.10.1109/48.809275Google Scholar
Xu, D., Sun, Y., Du, J. and Hu, X. (2016). Disturbance observer based sliding mode controller design fir heave motion of surface effects ships. Journal of Donghua University, 33(5), 759–763.Google Scholar
Yang, J., Li, S. and Yu, X. (2013). Sliding mode control for systems with mismatched uncertainties via a disturbance observer. IEEE Transactions on Industrial Electronics, 60(1), 160–169.10.1109/TIE.2012.2183841Google Scholar
Zhang, R., Chen, Y. and Sun, Z. (2000). Path control of a surface ship in restricted waters using sliding mode. IEEE Transactions on Control System Technology, 80(4), 722–732.10.1109/87.852916Google Scholar
Zhang, G., Zhang, X. and Zheng, Y. (2015). Adaptive neural path following control for underactuated ships in fields of marine practice. Ocean Engineering, 104, 558–567.10.1016/j.oceaneng.2015.05.013Google Scholar
Zhang, X. and Zhang, G. (2016). Design of a ship course keeping autopilot using a sine function based nonlinear feedback technique. The Journal of Navigation, 69(2), 246–256.10.1017/S0373463315000612Google Scholar