Robust Adaptive Position Tracking Control of Underactuated Unmanned Surface Vehicle

doi:10.3969/j.issn.1000-1093.2020.07.017

Abstract

Abstract: An neural network adaptive position tracking control strategy for underactuated unmanned surface vehicle (USV) is proposed to solve the problem of horizontal position tracking of underactuated USV with unknown uncertainties. A nonlinear controller is designed by using the backstepping method. The dynamic surface method is used to obtain the derivative of dummy variable, which reduces the complexity of direct derivation of dummy variables. The uncertain function of USV system is approximated by employing the neural network adaptive method, which overcomes parameter uncertainty problem. An exponentially convergent current observer is designed to effectively estimate the constant current velocity. The stability of the closed-loop control system is proved by using Lyapunov stability theory. Finally, the effectiveness and robustness of control strategy are verified by simulation experiments. Key

Key words: underactuatedunmannedsurfacevehicle, neuralnetwork, dynamicsurfacecontrol, positiontracking, robust

CLC Number:

TP273⁺.2

ZHANG Chengju， WANG Cong, WANG Jinqiang, LI Conghui. Robust Adaptive Position Tracking Control of Underactuated Unmanned Surface Vehicle[J]. Acta Armamentarii, 2020, 41(7): 1393-1400.

References

［1］ XIE W J, MA B L, HUANG W, et al. Global trajectory tracking control of underactuated surface vesseles with non-diagonal inertial and damping matrices[J]. Nonlinear Dynamics, 2018, 92(3): 1481-1492.
[2] 王岩, 朱齐丹, 孟浩, 等. 欠驱动船舶航迹跟踪指令滤波滑模鲁棒控制[J].电机与控制学报, 2013, 17(5): 110-118.
WANG Y, ZHU Q D, MENG H, et al. Command filter sliding mode robust tracking control of underactuated surface vessels[J]. Electric Machines and Control, 2013, 17(5): 110-118.(in Chinese)
[3] 王金强, 王聪, 魏英杰, 等. 未知海流干扰下自主水下航行器位置跟踪控制策略研究[J]. 兵工学报, 2019, 40(3):583-591.
WANG J Q, WANG C, WEI Y J, et al. Position tracking control of autonomous underwater vehicles in the disturbance of unknown ocean currents[J]. Acta Armamentarii, 2019, 40(3):583-591.(in Chinese)
[4] 周佳加, 严浙平, 贾鹤鸣, 等. 改进规则下自适应神经网络的UUV水平面路径跟随控制[J]. 中南大学学报(自然科学版), 2014, 45(9):3021-3028.
ZHOU J J, YAN Z P, JIA H M, et al. Improved path-following in horizontal plane for UUV using adaptive neural network controller[J]. Journal of Central South University (Science and Technology), 2014, 45(9):3021-3028.(in Chinese)
[5] 王宏健, 陈子印, 贾鹤鸣, 等. 基于滤波反步法的欠驱动AUV三维路径跟踪控制[J].自动化学报, 2015, 41(3): 631-645.
WANG H J, CHEN Z Y, JIA H M, et al. Three-dimensional path-following control of underactuated autonomous underwater vehicle with command filtered backstepping[J]. Acta Automatica Sinica, 2015, 41(3): 631-645.(in Chinese)
[6] DONG Z P, WAN L, LI Y M, et al.Trajectory tracking control of underactuated USV based on modified backstepping approach[J]. International Journal of Naval Architecture & Ocean Engineering, 2015, 7(5): 817-832.
[7] SONNENBURG C, WOOLSEY C A. An experimental comparison of two USV trajectory tracking control laws[C]∥Proceedings of IEEE International Conference on OCEANS. Hampton Roads, VA, US: IEEE, 2012.
[8] WANG J Q, WANG C, WEI Y J, et al. Three-dimensional path following of an underactuated AUV based on neuro-adaptive command filtered backstepping control[J]. IEEE Access, 2018, 6:74355-74365.
[9] 张伟, 滕延斌, 魏世琳, 等. 欠驱动UUV自适应RBF神经网络反步跟踪控制[J]. 哈尔滨工程大学学报, 2018, 39(1):93-99.
ZHANG W, TENG Y B, WEI S L, 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)
[10] 徐健, 汪慢, 乔磊, 等. 欠驱动UUV三维轨迹跟踪的反步动态滑模控制[J]. 华中科技大学学报(自然科学版), 2015, 43(8):107-113.
XU J, WANG M, QIAO L, 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)
[11] YIPP C P. Robust and adaptive nonlinear control using dynamic surface controller with applications to intelligent vehicle highway systems[D]. Berkeley, CA, US:University of California, 1997:70-90.
[12] ELMOKADEM T , ZRIBI M , YOUCEF-TOUMI K. Trajectory tracking sliding mode control of underactuated AUVs[J]. Nonlinear Dynamics, 2016, 84(2):1079-1091.
[13] PAN C Z, LAI X Z, SIMON X Y, et al. A bioinspired neural dynamics-based approach to tracking control of autonomous surface vehicles subject to unknown ocean currents[J]. Neural Computing & Application, 2015,26: 1929-1938.
[14] SHOJAEI K. Line-of-sight target tracking control of underactuated autonomous underwater vehicles[J]. Ocean Engineering, 2017, 133: 244-252.
[15] 毕凤阳. 欠驱动自主水下航行器的非线性鲁棒控制策略研究［D］. 哈尔滨:哈尔滨工业大学, 2010.
BI F Y. Research on nonlinear robust control strategies of underactuated autonomous underwater vehicle［D］. Harbin: Harbin Institute of Technology, 2010.（in Chinese）

[16] DO K D, PAN J. Robust path-following of underactuated ships: theory and experiments on a model ship[J]. Ocean Engineering, 2006, 33(10): 1354-1372.

第41卷第7期2020 年7月
兵工学报ACTA ARMAMENTARII
Vol.41No.7Jul.2020

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