基于RBF神经网络的水面船舶轨迹跟踪控制

针对速度矢量不可测、动态参数不确定以及具有未知扰动和磁滞特性的水面船舶系统,提出一种基于径向基函数神经网络的自适应反馈轨迹跟踪控制方案。根据船舶的状态矢量,利用高增益观测器估计水面船舶系统的不可测速度矢量,并通过一个函数描述间隙类磁滞对系统的影响。利用径向基函数神经网络的逼近能力和反步法设计控制器,基于李雅普诺夫稳定性理论,验证所设计控制器的稳定性,证明系统所有的闭环信号都是半全局一致有界的。通过仿真验证了控制器的有效性。

Surface Vessel Tracking Control based on RBF Neural Network

An adaptive feedback trajectory tracking control scheme based on radial basis function (RBF) neural network (NN) is proposed for a surface vessel system with unmeasurable velocity vector, uncertain dynamic parameters, unknown disturbance and hysteresis. Firstly, according to the state vector of surface vessel, the unmeasured velocity vector is estimated by the high-gain observer, and the effect of the Backlash-like hysteresis is described by a function. Secondly, the controller is designed by using RBF NN approximation in combination with backstepping. Finally, based on the Lyapunov stability theory, the stability of the designed controller is verified, and it is proved that all closed-loop signals are semi-globally uniformly bounded. Simulation studies are performed to demonstrate the effectiveness of the proposed method.