针对船舶航向非线性运动数学模型存在不确定性误差的情况下，提出一种新颖的动态面二阶滑模智能控制方法。首先采用动态面控制（DSC）技术,以消除传统Backstepping方法中存在的“计算爆炸”问题。为了削弱滑模控制中固有的抖振效应，提高系统的鲁棒性，引用了一种新颖的二阶滑模控制方法。然后直接利用径向基神经网络技术逼近模型误差，同时采用最少学习参数（MLP）技术，以减少控制器的计算负担，所设计的控制器可以保证闭环系统中所有信号一致最终有界，并使跟踪误差任意小，最后通过仿真验证所提算法的有效性。
In this paper, a novel dynamic surface second order sliding model control method is proposed for course-keeping control of ship in the presence of uncertain errors. The controller is constructed by " dynamic surface control" technique to solve the problems of " explosion of complexity" in the traditional Lyapunov stability theory. A novel second order sliding model control method is proposed in this paper, which is not only capable of strengthening robustness of the system, but also attenuating inherent chattering of classical sliding mode control method effectively. And then the radial basis function neural network approximation technique is used for approximating modeling errors, meanwhile the " minimum learning parameter" technique is used to reduce the computational burden of the algorithm. The controller guarantees that all the close-loop signals are uniform ultimate bounded (UUB) and that the tracking er-rors converge to a small neighborhood of the desired trajectory. Finally, simulation results are given to illustrate the effectiveness of the proposed algorithm.
2017,39(10): 66-69 收稿日期：2016-09-17
DOI：10.3404/j.issn.1672-7649.2017.10.012
分类号：V249.122
基金项目：国家自然科学基金资助项目（51179019,61374114）；辽宁省教育厅重点实验室基础项资助项目（LZ2015006）；中央高校基本科研业务费资助项目（3132016313）
作者简介：张凯(1993-),男,硕士研究生,研究方向为船舶运动控制、水下机器人控制算法
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