Double-Loop Integral Terminal Sliding Mode Tracking Control for UUVs With Adaptive Dynamic Compensation of Uncertainties and Disturbances
Double-Loop Integral Terminal Sliding Mode Tracking Control for UUVs With Adaptive Dynamic Compensation of Uncertainties and Disturbances
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DOI：
10.1109/joe.2017.2777638
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发表时间：
2019-01-01
影响因子：
4.1
通讯作者：
Zhang, Weidong
中科院分区：
文献类型：
--
作者：
Qiao, Lei;Zhang, Weidong
本文重点研究存在动态不确定性和时变外部干扰的情况下无人水下航行器（UUV）的轨迹跟踪控制。针对基于积分终端滑模（ITSM）和快速ITSM（FITSM）的UUV，提出了两种自适应积分终端滑模控制方案，即自适应积分终端滑模控制（AITSMC）方案和自适应快速积分终端滑模控制（AFITSMC）方案。每个控制方案都是双环的：由运动控制器和动态控制器组成。首先，为两种控制方案设计运动控制器。这两个运动控制器分别基于ITSM和FITSM。这些运动控制器使位置跟踪误差局部有限时间收敛到零，同时避免了传统终端滑模控制（TSMC）中的奇点问题。然后，使用运动控制器的输出作为参考速度命令，为两种控制方案中的每一种开发动态控制器。这两个动态控制器也分别基于ITSM和FITSM。引入自适应机制来估计由动态不确定性和时变外部干扰组成的集总系统不确定性上限的未知参数，从而不需要集总系统不确定性上限的先验知识。然后将估计的参数用作控制器参数，以消除集总系统不确定性的影响。研究了积分终端滑动变量向量的收敛速度，得到了ITSM或FITSM中速度跟踪误差局部有限时间收敛到零的结果。最后，基于设计的运动学和动态控制器，显示了全闭环级联系统的有限时间稳定性。所提出的两种控制方案比现有的全局有限时间稳定跟踪控制（GFTSTC）和自适应非奇异TSMC方案提高了跟踪精度，并增强了GFTSTC方案对参数不确定性和外部干扰的鲁棒性。与传统的自适应积分滑模控制（AISMC）方案相比，由于涉及分数积分器，所提出的两种控制方案为UUV的轨迹跟踪控制提供了更快的收敛速度和更强的针对动态不确定性和时变外部干扰的鲁棒性。对全向智能导航无人潜航器的动态模型进行了两种轨迹跟踪情况的对比数值模拟。收敛速度和对不确定性和干扰的鲁棒性分别被量化为稳态位置和速度跟踪误差的收敛时间和界限。结果表明，与AISMC方案相比，所提出的两种控制方案的收敛速度提高了至少20s，位置跟踪鲁棒性提高了约2%，速度跟踪鲁棒性提高了20%。
This paper focuses on the trajectory tracking control of unmanned underwater vehicles (UUVs) in the presence of dynamic uncertainties and time-varying external disturbances. Two adaptive integral terminal sliding mode control schemes, namely, adaptive integral terminal slidingmode control (AITSMC) scheme and adaptive fast integral terminal sliding mode control (AFITSMC) scheme are proposed for UUVs based on integral terminal sliding mode (ITSM) and fast ITSM (FITSM), respectively. Each control scheme is double-looped: composed of a kinematic controller and a dynamic controller. First, a kinematic controller is designed for each of the two control schemes. The two kinematic controllers are based on ITSM and FITSM, respectively. These kinematic controllers yield local finite-time convergence of the position tracking errors to zero meanwhile avoid the singularity problem in the conventional terminal sliding mode control (TSMC). Then, using the output of the kinematic controller as a reference velocity command, a dynamic controller is developed for each of the two control schemes. The two dynamic controllers are also based on ITSM and FITSM, respectively. An adaptive mechanism is introduced to estimate the unknown parameters of the upper bound of the lumped system uncertainty which consists of dynamic uncertainties and time-varying external disturbances so that the prior knowledge of the upper bound of the lumped system uncertainty is not required. The estimated parameters are then used as controller parameters to eliminate the effects of the lumped system uncertainty. The convergence rate of the integral terminal sliding variable vector is investigated and the local finitetime convergence of the velocity tracking errors to zero in the ITSM or FITSM is obtained. Finally, based on the designed kinematic and dynamic controllers, the finite-time stability of the full closed-loop cascaded system is shown. The two proposed control schemes improve the tracking accuracy over the existing globally finite-time stable tracking control (GFTSTC) and adaptive nonsingular TSMC schemes, and enhance the robustness against parameter uncertainties and external disturbances over the GFTSTC scheme. Compared with the conventional adaptive integral sliding mode control (AISMC) scheme, the two proposed control schemes offer faster convergence rate and stronger robustness against dynamic uncertainties and time-varying external disturbances for the trajectory tracking control of UUVs due to involving the fractional integrator. Comparative numerical simulations are performed on the dynamic model of the Omni Directional Intelligent Navigator UUV for two trajectory tracking cases. The convergence rate and robustness to uncertainties and disturbances are quantified as the convergent time and bounds of the steady-state position and velocity tracking errors, respectively. The results show that the two proposed control schemes improve at least 20s in convergence rate and enhance about 2% robustness in position tracking and 20% robustness in velocity tracking over the AISMC scheme.