未知时变扰动及模型参数动态不确定下船舶自动靠泊控制

Automatic berthing control of ships with unknown time-varying disturbance and dynamic uncertainty of model parameters

针对未知时变扰动及模型参数动态不确定下的船舶自动靠泊控制问题,提出一种新的间接神经自适应的靠泊控制策略。采用径向基函数神经网络逼近船舶模型参数中动态不确定部分,采用带有唯一虚拟参数的线性化参数形式表示模型参数动态不确定性和未知时变扰动构成的复合不确定项,使计算简单,易于工程实现。利用Lyapunov理论证明提出的自动靠泊闭环控制系统的稳定性和信号的一致有界性。仿真实验结果证明了该控制律可以使船舶达到期望的位置和艏向角,实现船舶自动靠泊。

Aiming at the ship automatic berthing control problem under the unknown time-varying disturbance and the dynamic uncertainty of model parameters,a new indirect neural adaptive berthing control strategy is proposed.The radial basis function neural network is used to approximate the dynamic uncertain part of the ship model parameters,and the linearized parameter form with a unique virtual parameter is used to express the compound uncertainty from the dynamic uncertainty of model parameters and the unknown time-varying disturbance,making the calculation simple and the project realization easy.The stability of the proposed automatic berthing closed-loop control system and the consistent boundedness of the signal are proved by Lyapunov theory.Simulation experiment results prove that the control law can control a ship to the desired position and heading angle,and realize ship automatic berthing.