Finite-time observer-based trajectory tracking control of underactuated USVs using hierarchical non-singular terminal sliding mode

被引:3
作者
Zhu F. [1 ]
Peng Y. [1 ]
Cheng M. [1 ]
Luo J. [1 ]
Wang Y. [1 ]
机构
[1] School of Mechatronic Engineering and Automation, Shanghai University, Shanghai
关键词
dynamic surface control; finite-time disturbance observer; Non-singular terminal sliding mode control; underactuated unmanned surface vehicles;
D O I
10.1080/23335777.2021.1921851
中图分类号
学科分类号
摘要
In this paper, a finite-time control method has been proposed for underactuated unmanned surface vehicles (USVs) with external disturbances in order to implement trajectory tracking control. Considering the complexity of the marine environment and the high-accuracy and rapidity required by USVs to complete complex marine missions, such as water quality detection, underwater pipe-laying, rescue operations and so on, a novel hierarchical sliding mode base on non-singular terminal sliding mode control (NTSMC) method is designed for underactuated USVs to ensure that all tracking error can faster converge to a neighbourhood around zero within finite time and address effectively the singularity problem which always exist in terminal sliding mode control (TSMC). The underactuated problem is addressed by hierarchical sliding mode technique. Meanwhile, the dynamic surface control (DSC) is employed to address the explosion problem of computational complexity in traditional method. Further, a novel finite-time disturbance observer (FDO) is devised to estimate accurately the unknown environmental disturbances and for practicality, a saturation constraint function is used to limit the input of the controller. Finally, the effectiveness and stability of the proposed method are validated by simulations and comparisons. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
引用
收藏
页码:263 / 285
页数:22
相关论文
共 29 条
[11]  
Nie L., Guan J., Lu C., Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network, IET Intelligent Transport Systems, 12, 6, pp. 485-494, (2018)
[12]  
Chen Z., Li Z., 28, 6, pp. 1-13, (2016)
[13]  
(2016)
[14]  
Wang J., Wang C., Wei Y., Sliding mode based neural adaptive formation control of underactuated AUVs with leader-follower strategy, Applied Ocean Research, 94, (2020)
[15]  
Chen L., Cui R., Yang C., (2019)
[16]  
Dong Z., Bao T., Zheng M., Heading Control of Unmanned Marine Vehicles based on an Improved Robust Adaptive Fuzzy Neural Network Control Algorithm, IEEE Access, 7, (2019)
[17]  
Chang H., Jian C., Xin G., Fractional-order sliding mode control of uncertain QUAVs with time-varying state constraints, Nonlinear Dynamics, 95, 2, pp. 1347-1360, (2019)
[18]  
Aguilar-Ibanez C., Suarez-Castanon M.S., A trajectory planning based controller to regulate an uncertain 3d overhead crane system, Int J App Mathe Comput, 29, 4, pp. 693-702, (2019)
[19]  
Zhang Y., Chen Z., Nie Y., pp. 1298-1303, (2020)
[20]  
Yan Z., Yu H., Zhang W., pp. 1):132-146, (2015)