基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应控制

针对三自由度全驱动船舶存在模型不确定和未知外部环境扰动的情况，设计出一种基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应神经网络控制方法.该方法综合考虑船舶位置和速度误差之间关系设计递归滑模面，引入神经网络对船舶模型不确定部分进行逼近，设计带σ-修正泄露项的自适应律对神经网络逼近误差与外界环境扰动总和的界进行估计，并应用一种非线性增益函数构造动态面控制律，选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后，基于一艘供给船进行仿真验证，结果表明，船舶轨迹跟踪响应速度快、精度高，所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.

Recursive Sliding-mode Dynamic Surface Adaptive Control for Ship Trajectory Tracking With Nonlinear Gains

The trajectory tracking problem of three degrees of freedom fully actuated ship with model uncertainty and unknown external environmental disturbances is analyzed, and a recursive sliding-mode dynamic surface adaptive robust control method with nonlinear gains for ship trajectory tracking is proposed. The recursive sliding-mode surface which considers the relationship between position and velocity errors is designed. Neural networks are constructed to provide estimation of model uncertainty and feedforward compensation for control amount. The adaptive laws based on leakage terms of σ modification are used to estimate bounds of neural network errors and unknown external environmental disturbances. A new function with nonlinear gains is used to construct the dynamic surface control law. With a new Lyapunov function, all signals in the ship's closed-loop trajectory tracking system can be guaranteed to have the uniformly ultimate boundedness by using the proposed control law. Simulation results show that the tracking speed is fast and the accuracy is high, and the proposed controller is strongly robust to model uncertainty and unknown external environmental disturbances.