Path following control of underactuated autonomous vessels remains a challenging issue in recent years due to its inherent underactuation and nonlinearities as well as the widely existing disturbances in the marine environment. In order to accommodate all the difficulties simultaneously, a novel extended state Kalman filter, which adopts the idea of extended state observer in estimating and compensating system lumped disturbance and optimizes the filter gain in a real-time fashion using Kalman filter technique, is constructed to estimate system states and disturbances in the presence of model uncertainties and measurement noise. Based on the estimated states and disturbances, an enhanced model predictive controller is proposed to steer the underactuated autonomous vessels along a predefined path at a desired speed after considering system state and input constraints. Simulation results have proved the superiority of extended state Kalman filter over traditional extended state observer and extended Kalman filter under various disturbance and noise scenarios. Moreover, the comparison results with conventional proportion-integration-differentiation controller have demonstrated the feasibility and efficacy of the proposed extended state Kalman filter-based model predictive controller in both set-point tracking and disturbance rejection.

近年来，由于其固有的欠驱动和非线性以及海洋环境中广泛存在的干扰，对欠驱动的自主船舶进行路径跟踪控制仍然是一个具有挑战性的问题。为了同时解决所有困难，构造了一种新颖的扩展状态卡尔曼滤波器，它采用扩展状态观测器的思想来估计和补偿系统的集总干扰，并使用卡尔曼滤波器技术实时优化滤波器增益。在存在模型不确定性和测量噪声的情况下估计系统状态和干扰。根据估计的状态和干扰，在考虑系统状态和输入约束之后，提出了一种增强的模型预测控制器，以期望的速度沿预定路径操纵欠驱动的自主船。仿真结果证明了在各种干扰和噪声情况下，扩展状态卡尔曼滤波器优于传统的扩展状态观测器和扩展卡尔曼滤波器。此外，与常规比例积分微分控制器的比较结果证明了所提出的基于扩展状态卡尔曼滤波器的模型预测控制器在设定点跟踪和干扰抑制方面的可行性和有效性。仿真结果证明了在各种干扰和噪声情况下，扩展状态卡尔曼滤波器优于传统的扩展状态观测器和扩展卡尔曼滤波器。此外，与常规比例积分微分控制器的比较结果证明了所提出的基于扩展状态卡尔曼滤波器的模型预测控制器在设定点跟踪和干扰抑制方面的可行性和有效性。仿真结果证明了在各种干扰和噪声情况下，扩展状态卡尔曼滤波器优于传统的扩展状态观测器和扩展卡尔曼滤波器。此外，与常规比例积分微分控制器的比较结果证明了所提出的基于扩展状态卡尔曼滤波器的模型预测控制器在设定点跟踪和干扰抑制方面的可行性和有效性。