Trajectory tracking control of underactuated ship based on adaptive iterative sliding mode

SHEN Zhi-peng; DAI Chang-sheng; ZHANG Ning

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SHEN Zhi-peng, DAI Chang-sheng, ZHANG Ning. Trajectory tracking control of underactuated ship based on adaptive iterative sliding mode[J]. Journal of Traffic and Transportation Engineering, 2017, 17(6): 125-134.

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SHEN Zhi-peng, DAI Chang-sheng, ZHANG Ning. Trajectory tracking control of underactuated ship based on adaptive iterative sliding mode[J]. Journal of Traffic and Transportation Engineering, 2017, 17(6): 125-134.

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Trajectory tracking control of underactuated ship based on adaptive iterative sliding mode

School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China

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Author Bio:
SHEN Zhi-peng(1977-), male, professor, PhD, shenbert@dlmu.edu.cn
Received Date: 2017-07-21
Publish Date: 2017-12-25

Abstract

Abstract

Aiming at the trajectory tracking control problem of underactuated ship, the unknown parameters and external disturbances of ship system were considered, and a control method with reinforcement learning based on neural network adaptive iterative sliding mode was put forward.The nonlinear iterative sliding mode functions were constructed based on the horizontal and vertical deviations of tracking trajectory, and the neural network iterative sliding mode controllers of diesel engine speed and rudder angle were designed, respectively.According to the real-time measurement values of diesel engine speed and rudder angle, the reinforcement learning signals reflecting the chattering states of control quantities were calculated, and the neural networks' constructions and parameters were optimized online to restrain control the chattering states and enhance the control system's adaptability.The mathematical model of 5446 TEU container ship was established, and the trajectory tracking controls of circular and sinusoidal trajectories werecarried out, respectively.Simulation result shows that when the circular trajectory is tracked under the disturbances of wind and sea wave, the tracking time of target trajectory is about 250 s with the proposed control strategy, and the tracking speed is about 1 time higher than the value with iterative sliding mode control strategy.The maximum tracking yaw distance is 250 m, and the error reduces by about 30%.The control rudder angle is basically stable after 400 s, and its chattering amplitude is about 2°.The chattering amplitudes of rudder angle and diesel engine speed reduce by more than 50%.The control parameters of diesel engine speed and rudder angle are adaptively adjusted between 38-45 and 3.3-3.9, respectively.When the sinusoidal trajectory is tracked, the proposed control strategy is compared with the fuzzy iterative sliding mode control strategy, and the average vertical tracking error is less than 20 mand reduces by more than 50%.The average chattering amplitude of rudder angle is less than 10°and reduces by more than 60%.The control parameters of diesel engine speed and rudder angle are adaptively adjusted between5.7-5.8 and 0.8-2.5, respectively.
- underactuated ship,
- trajectory tracking,
- diesel engine speed,
- ship rudder angle,
- adaptive iterative sliding mode,
- neural network,
- reinforcement learning

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References

[1]	GUO Chen, WANG Yang, SUN Fu-chun, et al. Survey for motion control of underactuated surface vessels[J]. Control and Decision, 2009, 24 (3): 321-329. (in Chinese). doi: 10.3321/j.issn:1001-0920.2009.03.001
[2]	YANG Yang, DU Jia-lu, LIU Hong-bo, et al. A trajectory tracking robust controller of surface vessels with disturbance uncertainties[J]. IEEE Transactions on Control Systems Technology, 2014, 22 (4): 1511-1518. doi: 10.1109/TCST.2013.2281936
[3]	DUAN Hai-qing, ZHU Qi-dan. Trajectory tracking control of ships based on an adaptive backstepping neural network[J]. CAAI Transactions on Intelligent Systems, 2012, 7 (3): 259-264. (in Chinese). doi: 10.3969/j.issn.1673-4785.201205056
[4]	ZHANG Wei, TENG Yan-bin, WEI Shi-lin, et al. Underactuated UUV tracking control of adaptive RBF neural network and backstepping method[J]. Journal of Harbin Engineering University, 2018, 39 (1): 93-99. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG201801015.htm
[5]	WONDERGEN M, LEFEBER E, PETTERSEN K, et al. Output feedback tracking of ships[J]. IEEE Transactions on Control Systems Technology, 2011, 19 (2): 442-448. doi: 10.1109/TCST.2010.2045654
[6]	ASHRAFIUON H, MUSKE K R, MCNINCH L C, et al. Sliding-mode tracking control of surface vessels[J]. IEEE Transactions on Industrial Electronics, 2008, 55 (11): 4004-4012. doi: 10.1109/TIE.2008.2005933
[7]	YU Rui-ting, ZHU Qi-dan, XIA Gui-lin, et al. Sliding mode tracking control of an underactuated surface vessel[J]. IET Control Theory and Applications, 2012, 6 (3): 461-466. doi: 10.1049/iet-cta.2011.0176
[8]	JIA He-ming, CHENG Xiang-qin, ZHANG Li-jun, et al. Three-dimensional path tracking control for underactuated AUV based on adaptive backstepping[J]. Control and Decision, 2012, 27 (5): 652-657, 664. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201205005.htm
[9]	LAO Yu-lei, ZHUANG Jia-yuan, LI Ye, et al. Sliding-mode trajectory tacking control for underactuated autonomous surface vehicle[J]. Journal of Applied Sciences, 2011, 29 (4): 428-434. (in Chinese). doi: 10.3969/j.issn.0255-8297.2011.04.016
[10]	XING Dao-qi, ZHANG Liang-xin. Sliding-model control for trajectory tracking of surface vessels[J]. Ship and Boat, 2011, 22 (5): 10-14. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CBZZ201105005.htm
[11]	XU Jian, WANG Man, QIAO Lei, et al. Backstepping dynamical sliding mode controller for three-dimensional trajectory tracking of underactuated UUV[J]. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2015, 43 (8): 107-113. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG201508023.htm
[12]	LIAO Yu-lei, WAN Lei, ZHUANG Jia-yuan. Backstepping adaptive dynamical sliding mode control method for path following of underactuated surface vessel[J]. Journal of Central South University: Science and Technology, 2012, 43 (7): 2655-2661. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201207029.htm
[13]	ELMOKADEM T, ZRIBI M, YOUCEF-TOUMI K. Trajectory tracking sliding mode control of underactuated AUVs[J]. Nonlinear Dynamics, 2016, 84 (2): 1079-1091.
[14]	HWANG C L, CHIANG C C, YEH Y W. Adaptive fuzzy hierarchical sliding-mode control for the trajectory tracking of uncertain underactuated nonlinear dynamic systems[J]. IEEE Transactions on Fuzzy Systems, 2014, 22 (2): 286-299.
[15]	RAYGOSA-BARHONA R, PARRA-VEGA V, OLGUIN-DIAZ E, et al. A model-freebackstepping with integral sliding mode control for underactuated ROVs[C]//IEEE. 8th International Conference on Electrical Engineering, Computing Science and Automatic Control. New York: IEEE, 2011: 1-7.
[16]	LIU Chun-mei, YEH Chih-ping, CHEN Wen. Robust iterative learning control for output tracking via chatteringfree sliding mode control technique[C]//IEEE. 8th IEEE International Conference on Control and Automation. New York: IEEE, 2010: 241-246.
[17]	ZHAO Guo-liang, ZHAO Can, WANG De-gang. Tensor product model transformation based integral sliding mode control with reinforcement learning strategy[C]//IEEE. Proceedings of the 33rd Chinese Control Conference. New York: IEEE, 2014: 77-82.
[18]	HE Xiong-xiong, ZHUANG Hua-liang, ZHUANG Duan, et al. Pulse neural network-based adaptive iterative learning control for uncertain robots[J]. Neural Computing and Applications, 2013, 23 (7/8): 1885-1890.
[19]	HUANG Zheng-yu, EDWARDS R M, LEE K Y. Fuzzyadapted recursive sliding-mode controller design for a nuclear power plant control[J]. IEEE Transactions on Nuclear Science, 2004, 51 (1): 256-266.
[20]	JIA He-ming, ZHANG Li-jun, CHENG Xiang-qin, et al. Three-dimensional path following control for an underactuated UUV based on nonlinear iterative sliding mode[J]. Acta Automatica Sinica, 2012, 38 (2): 308-314. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201202019.htm
[21]	BU Ren-xiang, LIU Zheng-jiang, LI Tie-shan. Iterative sliding mode based increment feedback control and its application to ship autopilot[J]. Journal of Harbin Engineering University, 2007, 28 (3): 268-272. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG200703004.htm
[22]	BIAN Xin-qian, CHENG Xiang-qin, JIA He-ming, et al. A bottom-following controller for underactuated AUV based on iterative sliding and increment feedback[J]. Control and Decision, 2011, 26 (2): 289-292, 296. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201102025.htm
[23]	SHEN Zhi-peng, JIANG Zhong-hao, WANG Guo-feng, et al. Fuzzy-adapted iterative sliding mode control for sail-assisted ship motion[J]. Journal of Harbin Engineering University, 2016, 37 (5): 634-639. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG201605002.htm
[24]	SHEN Zhi-peng, DAI Chang-sheng. Iterative sliding mode control based on reinforced learning and used for path tracking of under-actuated ship[J]. Journal of Harbin Engineering University, 2017, 38 (5): 697-704. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG201705007.htm
[25]	SHEN Zhi-peng, GUO Chen, ZHANG Ning. A general fuzzified CMAC based reinforcement learning control for ship steering using recursive least-squares algorithm[J]. Neurocomputing, 2010, 73 (4-6): 700-706.