欠驱动船舶动力布局不均匀、船舶转向能力和速度调节能力较差,其靠泊受外界风力、水流等影响控制难度过高。为解决这一问题,本文提出一种基于模糊自适应PID的欠驱动船舶靠泊自动控制方法。首先依据欠驱动船舶的运动学与动力学模型,利用视线角导航算法,确定欠驱动船舶靠泊航行轨迹;然后利用模糊自适应PID控制算法,通过船舶靠泊的首向控制器与速度控制器,利用模糊控制规则,实时推理获取最佳的PID控制参数,控制船舶靠泊的航向角与速度趋于期望航向与期望航速;最后将模糊自适应PID控制算法的控制输出传送至船舶的执行机构,使船舶依据规划的靠泊路径航行,实现欠驱动船舶靠泊自动控制。实验结果表明,该方法可有效控制欠驱动船舶靠泊,低速航行时,仍能保持较低的船舶航向角与航行速度控制误差,应用效果不佳。
Underactuated ships have uneven power layout, poor turning and speed regulation capabilities, and their berthing is difficult to control due to external wind, water flow, and other factors. To address this issue, this study proposes a fuzzy adaptive PID based automatic control method for underactuated ship berthing. This time, based on the kinematic and dynamic models of underactuated ships, the line of sight navigation algorithm is used to determine the berthing and navigation trajectory of underactuated ships. Then, using the fuzzy adaptive PID control algorithm, the optimal PID control parameters are obtained through real-time inference using fuzzy control rules through the bow controller and speed controller of the ship berthing, to control the heading angle and speed of the ship berthing towards the desired heading and speed. Finally, the control output of the fuzzy adaptive PID control algorithm is transmitted to the execution mechanism of the ship, allowing the ship to navigate according to the planned berthing path, achieving automatic control of underactuated ship berthing. The experimental results show that this method can effectively control the berthing of underactuated ships, and can still maintain low ship heading angle and navigation speed control errors at low speeds, but the application effect is not satisfactory.
2024,46(11): 70-74 收稿日期:2024-02-28
DOI:10.3404/j.issn.1672-7649.2024.11.013
分类号:TP273
作者简介:范凌云(2001-),男,研究方向为智能控制
参考文献:
[1] 杜朋柱, 张砚北, 隆武强, 等. 基于非线性模型预测的欠驱动船舶轨迹跟踪控制研究[J]. 大连理工大学学报, 2023, 63(5): 494-500.
DU Pengzhu, ZHANG Yanbei, LONG Wuqiang, et al. Research on trajectory tracking control of underactuated ship based on nonlinear model prediction[J]. Journal of Dalian University of Technology, 2023, 63(5): 494-500.
[2] 马天珩, 宁杨阳. 基于非线性模型预测控制的无人船航迹跟踪控制方法[J]. 船舶工程, 2023, 45(2): 123-130+166.
MA Tianheng, NING Yangyang. Trajectory tracking control method of unmanned surface vehicles based on nonlinear model predictive control[J]. Ship Engineering, 2023, 45(2): 123-130+166.
[3] 刘佳仑, 谢玲利, 李诗杰, 等. 面向船舶智能航行测试的变稳船控制系统设计[J]. 中国舰船研究, 2023, 18(3): 38-47+74.
LIU Jialun, XIE Lingli, LI Shijie, et al. Design of variable stability ship control system for ship intelligent navigation test[J]. Chinese Journal of Ship Research, 2023, 18(3): 38-47+74.
[4] 赵永生, 吴韬, 白一鸣. 未知时变扰动及模型参数动态不确定下船舶自动靠泊控制[J]. 上海海事大学学报, 2022, 43(1): 8-13.
ZHAO Yongsheng, WU Tao, BAI Yiming. Automatic berthing control of ships with unknown time-varying disturbance and dynamic uncertainty of model parameters[J]. Journal of Shanghai Maritime University, 2022, 43(1): 8-13.
[5] 黄立文, 刘进来, 贺益雄, 等. 考虑舵机延时的船舶最优航向控制器设计[J]. 武汉理工大学学报, 2023, 45(8): 60-67.
HUANG Liwen, LIU Jinlai, HE Yixiong, et al. Design of ship optimalcourse controller considering the delay of steering gear[J]. Journal of Wuhan University of Technology, 2023, 45(8): 60-67.
[6] 彭斌, 王文奎, 马军祥, 等. 基于改进前馈补偿模糊PID的随动特性分析[J]. 计算机仿真, 2022, 39(3): 72-78.
PENG Bin, WANG Wenkui, MA Junxiang, et al. Analysis of follow-up characteristic based on improved feedforward compensation fuzzy PID[J]. Computer Simulation, 2022, 39(3): 72-78.