【摘要】 针对无人船在风浪流干扰环境下航向控制精度降低的问题，提出一种融合扩展卡尔曼滤波器（EKF）的无人船航向自抗扰控制方法。首先，建立无人船与风浪流干扰数学模型；其次，设计级联结构的线性扩张状态观测器，以减小传统线性自抗扰中观测器对风浪流干扰的估计误差，从而提高系统对扰动的估计精度；进一步地，基于无人船运动数学模型设计相应的EKF，用于滤除无人船在水面航行过程中的环境噪声，改善风浪流干扰导致无人船航向控制器输出不稳定的问题；最后，为验证该方法性能，与三种不同控制方法进行了对比仿真实验。实验结果表明，该方法能够更加快速精准地跟踪到目标航向。此外，通过实船实验验证了该方法具有一定的工程实用价值。更多还原

【Abstract】 Since the course control accuracy of an unmanned surface vessel(USV) is decreased under the wind, wave and current disturbances, a USV course active disturbance rejection control method based on extended Kalman filter(EKF) is proposed. Firstly, a mathematical model of the USV and wind, wave, and current disturbances is established. Then, a linear extended state observer with hierarchical structure is designed to reduce the estimation error of the observer in the traditional linear active disturbance rejection control(LADRC) for wind, wave, and current disturbances, thereby improving the estimation accuracy of the disturbance. Furthermore, on the basis of the mathematical model of the USV motion, an EKF is designed to filter out the environmental noise of the USV during surface navigation and eliminate unstable course controller outputs caused by wind, wave, and current disturbances. Finally, three different control methods are compared and simulated to verify the performance of the proposed method. The experimental results show that the proposed method can track the target course more quickly and accurately. In addition, the proposed method is proved to have a certain engineering practical value by real ship experiments.更多还原