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Robust adaptive sliding mode control of underactuated autonomous underwater vehicles with uncertain dynamics

机译:具有不确定动力学的欠驱动自动驾驶水下航行器的鲁棒自适应滑模控制

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摘要

This paper focuses on adaptive integral sliding mode control for a class of underactuated autonomous underwater vehicles (AUVs) with uncertain dynamics, where the vehicles moving in three-dimensional (3-D) space have only three available control inputs provided by the stem propellers, steering and diving rudders but five degrees of freedom to be controlled. Different from the traditional sliding mode control, the proposed dual closed-loop integral sliding mode control design can be described as comprising two distinct phases: 1) in outer-loop, the virtual velocity commands are determined for the following work; 2) in inner-loop, the actual control inputs are designed to achieve the trajectory tracking. Moreover, the practical situations that there exist systematic parametric uncertainties and external disturbances are also considered, and a novel direct adaptive neural network controller combined with a conditional integrator is presented, which provides the robustness and adaptation for the vehicle. In addition, the rigorous stability analysis based Lyapunov's method demonstrates the uniform ultimate boundedness of all the tracking errors in the closed-loop system. Finally, simulation results are shown the effectiveness of the proposed controllers.
机译:本文针对一类动力学不确定的欠驱动自动水下航行器(AUV)进行自适应积分滑模控制,其中在三维(3-D)空间中行驶的车辆只有主螺旋桨提供了三个可用的控制输入,操舵和潜水舵,但要控制五个自由度。与传统的滑模控制不同,所提出的双闭环整体滑模控制设计可描述为包括两个不同的阶段:1)在外环中,确定虚拟速度指令用于以下工作; 2)在内部回路中,实际控制输入旨在实现轨迹跟踪。此外,还考虑了存在系统参数不确定性和外部干扰的实际情况,并提出了一种新型的带有条件积分器的直接自适应神经网络控制器,为车辆提供了鲁棒性和自适应性。此外,基于李雅普诺夫方法的严格稳定性分析证明了闭环系统中所有跟踪误差的一致最终有界性。最后,仿真结果表明了所提出控制器的有效性。

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