针对欠驱动船舶在轨迹跟踪控制中遭遇的外部未知环境强扰动和控制器频繁更新问题，本文提出一种基于事件触发的自适应滑模控制器。控制器利用反步法设计虚拟控制律实现轨迹跟踪，并采用径向基（RBF）神经网络逼近船舶模型不确定部分，设计参数自适应律以估计外部干扰的上界。引入事件触发机制，显著降低控制器更新频率。通过构建Lyapunov函数，证明了闭环系统的稳定性，确保事件触发间隔大于零，有效预防Zeno现象。仿真结果显示，控制器能够保持精确轨迹跟踪，并且更新次数减少约5000次，显著提高船舶控制效率。该控制器设计简洁，易于实现，有助于降低船舶能耗，提高船舶的操作性能，可进一步应用到实际工程实践中。
Aiming at the problem of strong disturbance of unknown external environment and frequent controller update encountered in trajectory tracking control of underactuated ships, this paper proposes an event-triggering adaptive sliding mode controller. The controller designs a virtual control law to track the trajectory, and uses radial basis (RBF) neural network to approach the uncertain part of the ship model. The parameter adaptive law is designed to estimate the upper bound of external interference. The introduction of event-triggering mechanism significantly reduces the controller update frequency. By constructing a Lyapunov function, the stability of the closed-loop system is proven, ensuring that the event triggering interval is greater than zero and effectively preventing Zeno phenomena. The simulation results show that the controller can maintain precise trajectory tracking, and the number of updates is reduced by about 5,000 times, significantly improving the efficiency of ship control. The design of the controller is simple and easy to implement, which is helpful to reduce the energy consumption of the ship and improve the operation performance of the ship, and can be further applied to practical engineering practice.
2025,47(1): 70-75 收稿日期：2024-2-29
DOI：10.3404/j.issn.1672-7649.2025.01.013
分类号：U675.91
基金项目：辽宁省教育厅科学研究资助项目（LJKZ0726）；辽宁省教育厅2023年度高校基本科研项目（JYTQN2023131）
作者简介：白春鹏(1998-),男,硕士研究生,研究方向为船舶运动控制
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