针对船舶自动化研究领域中的路径规划和路径跟踪2个问题，提出一种基于改进蚁群算法的船舶路径规划方法。为了更好解决传统蚁群算法存在的问题，使用人工势场法改进蚁群算法信息素更新策略，统筹船舶路径规划与船舶路径跟踪控制问题，通过引入神经网络对船舶动力学子系统的未知不确定项进行逼近，设计一种基于径向基神经网络算法的滑模控制器，仿真实验验证了本文所提出算法的有效性。
Aiming at the two problems of path planning and path tracking in the field of ship automation research, proposes a ship path planning method based on improved ant colony algorithm. In order to better solve the problems of the traditional ant colony algorithm, the artificial potential field method is used to improve the pheromone update strategy of the ant colony algorithm, and the ship path planning and ship path tracking control issues are coordinated, and the unknown uncertainty of the ship dynamics subsystem is introduced by the neural network. The items are approximated, and a sliding mode controller based on radial basis function neural network algorithm is designed,Simulation experiments verify the effectiveness of the algorithm proposed in this paper.
2022,44(7): 68-73 收稿日期：2021-08-30
DOI：10.3404/j.issn.1672-7649.2022.07.013
分类号：U675.96
基金项目：国家自然科学基金重点项目（51939001），国家自然科学基金面上项目（61976033），国家自然科学基金青年基金项目（61803064）；辽宁省自然科学基金项目（20170540098）；中央高校基本科研业务费项目（3132021144, 3132021135）
作者简介：李伟(1968-),男,博士,教授,研究方向为船舶智能控制、船舶自动避碰、海上安全评估以及船舶操纵性能
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