Dual closed-loop robust adaptive fast integral terminal sliding mode formation finite-time control for multi-underactuated AUV system in three dimensional space

被引:65
|
作者
Xia, Guoqing [1 ]
Zhang, Yu [1 ]
Zhang, Wei [1 ]
Chen, Ximing [1 ]
Yang, Haoyu [1 ]
机构
[1] Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin 150001, Peoples R China
基金
中国国家自然科学基金;
关键词
Multi-underactuated AUV system; Uncertainties; Finite-time control; Fast integral terminal sliding mode control; RBF neural Network; Conditional integrator; AUTONOMOUS UNDERWATER VEHICLES; FOLLOWER FORMATION CONTROL; TRAJECTORY TRACKING; DESIGN; ROBOTS; OUTPUT;
D O I
10.1016/j.oceaneng.2021.108903
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
This paper addresses a dual closed-loop fast integral terminal sliding mode control method of a multi-underactuated AUV formation system with uncertain model parameters and environmental disturbances. Different from the traditional sliding mode control method, this technique can not only keep the formation stable, but also significantly overcomes the problem that the formation tracking errors of the traditional method may not converge to zero in finite time. Then, an adaptive radial basis function (RBF) neural network controller is incorporated with a conditional integrator to deal with the uncertain model parameters, approximation errors and environmental disturbances in practical multi-AUV systems. And also the proposed controller is continuous with the property of chattering restraining. A virtual leader is adopted on the basis of the leader-following strategy to improve the robustness of the formation system and prevent the problem of formation collapse caused by the failure of the leader AUV. Moreover, the rigorous stability analysis based on Lyapunov method and numerical simulations demonstrate tracking errors converge to 0 in finite time. Finally, simulation results demonstrate the effectiveness of the proposed formation controller: the AUV formation can track the desired trajectory accurately with +/- 10% model parameter perturbation.
引用
收藏
页数:15
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