针对无人艇在风浪流等高海况环境下易受干扰的问题，开展了无人艇自适应航行控制方法研究：提出了基于滑模神经网络（Sliding Mode Control-Radial Basis Function Neural Networks，SMC-RBFNN）的航速控制算法，具有更快的收敛速度，提高了无人艇航速控制的鲁棒性和抗干扰性；提出了基于改进无模型自适应迭代学习（Model Free Adaptive Iterative Learning Control，MFAILC）的航向控制算法，提高了模型变化、不确定性干扰条件下的航向控制精度和收敛速度；提出了一种基于改进积分视线（Integral Line of Sight，ILOS）算法的航路跟踪技术，提高了复杂海洋环境下的航路跟踪能力。最后，通过仿真和航行试验对算法的可行性进行验证。结果表明，航路平均跟踪误差在2 m以内，相对传统控制方法，所提出的自适应航行控制方法抗干扰能力强、收敛速度快、跟踪精度高。
Aiming at the problem that unmanned surface vehicle (USV) is susceptible to interference in high sea conditions such as wind, wave and current, the adaptive navigation control method of USV is studied: A speed Control algorithm based on Sliding Mode control-radial Basis Function Neural Networks (SMC-RBFNN) is proposed, which has a faster convergence rate and improves the robustness and anti-interference of speed control of USV. A heading Control algorithm based on Model Free Adaptive Iterative Learning Control (MFAILC) is proposed to improve the accuracy and convergence speed of heading control under the conditions of model changes and uncertain interference. A new route tracking technique based on improved Integral Line of Sight (ILOS) algorithm is proposed to improve the route tracking capability in complex Marine environment. Finally, the feasibility of the algorithm is verified by simulation and sailing test. The results show that the average tracking error of the route is less than 2 m. Compared with the traditional control method, the proposed adaptive navigation control method has strong anti-interference ability, high tracking accuracy and fast convergence speed.
2025,47(18): 68-74 收稿日期：2024-12-3
DOI：10.3404/j.issn.1672-7649.2025.18.012
分类号：U664.82；TP273
作者简介：王术龙（1982 – ），男，学士，高级工程师，研究方向为系统总体
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