Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances
针对欠驱动水面船只受未知周期性扰动和未知水动力参数的问题，提出一种学习与近最优控制方法，通过辅助系统在线学习并逼近最优控制律，仿真验证了有效性。
In this article, we propose a novel learning and near-optimal control approach for underactuated surface (USV) vessels with unknown mismatched periodic external disturbances and unknown hydrodynamic parameters. Given a prior knowledge of the periods of the disturbances, an analytical near-optimal control law is derived through the approximation of the integral-type quadratic performance index with respect to the tracking error, where the equivalent unknown parameters are generated online by an auxiliary system that can learn the dynamics of the controlled system. It is proved that the state differences between the auxiliary system and the corresponding controlled USV vessel are globally asymptotically convergent to zero. Besides, the approach theoretically guarantees asymptotic optimality of the performance index. The efficacy of the method is demonstrated via simulations based on the real parameters of an USV vessel.