针对船舶模型不确定和控制增益未知的非线性船舶航向控制问题，基于RBF神经网络自适应控制，提出一种新的非线性航向保持控制器.首先，在理论上证明存在连续的控制律；然后，通过RBF神经网络对其逼近；最后，借助Lyapunov稳定性理论分析证明船舶航向保持闭环系统的所有误差信号一致最终有界.仿真研究验证了该控制器的有效性.
Abstract
For ship model uncertainty and the unknown control gain nonlinear ship course control problem, this paper proposes a new nonlinear course keeping controller based on RBF neural network adaptive control. The paper proves theoretically the existence of a continuous control law, then approximate it by using RBF neural network. Finally the paper analyzes and illustrates that the consistency of all error signals of the closedloop system for ship course keeping is ultimately bounded via Lyapunov stability theory. Simulations verify the effectiveness of the controller.
关键词
船舶 /
航向保持 /
RBF神经网络 /
Lyapunov直接法 /
自适应控制
Key words
ship /
course keeping /
RBF neural network /
Lyapunov stability theory /
adaptive control
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参考文献
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基金
国家自然科学基金资助项目( 61074053);交通运输部交通应用基础研究基金资助项目( 2011-329-225-390).
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