基于Backstepping的欠驱动船舶神经滑模航迹控制

Neural Sliding Mode Control for Tracking of Underactuated Ships Based on Backstepping

在外界干扰和参数不确定的情况下,设计一种基于Backstepping的自适应神经滑模控制器对欠驱动船舶神经滑模航迹进行控制。采用趋近律的滑模控制,抑制常规滑模的固定切换增益系数带来的抖振现象;利用神经网络辨识对象,减少趋近律滑模控制对对象模型参数的依赖。以某实习船为例进行仿真,结果表明:在标称参数下,所设计的控制器能够有效跟踪设定的航迹并抑制常规滑模控制器的抖振现象;在外部环境扰动以及参数摄动的情况下,仍然能够实现较好的控制,表现出强鲁棒性。

As for uncertain external interference and parameters, a self-adaptive neural sliding mode controller based on backstepping is designed for underactuated ships powered by wind, wave and flow. The chattering problem brought on by fixed switching coefficient of traditional sliding mode control （SMC） is restrained by using approaching-law SMC. And the neural network is utilized to reduce the dependence on plant model＇ s parameters in approaching-law SMC. Results of a ship simulation indicate that the controller can effectively keep the given track and avoid the chattering phenomenon occurring in traditional sliding mode control and it can also control well under the external disturbance and parameter perturbation, which shows the controller strong robust.