基于Lyapunov稳定性定理和Backstepping方法，针对非线性船舶航向运动数学模型提出一种考虑输入饱和的直接自适应神经网络控制方法.采用Backstepping方法对系统进行递归式设计并借助一种饱和内补偿辅助系统处理系统中的输入饱和限制问题，同时运用最少学习参数（MLP）技术，以减少控制器的计算负担，便于工程实现和应用.本文设计的控制器保证了闭环系统信号一致最终有界，而且使系统输出能收敛到零的一个较小领域.数值仿真验证了该算法的有效性.
Abstract
Based on the Lyapunov stability theory and the backstepping technique, a direct adaptive neural network controller was proposed for ship coursekeeping control in the presence of input saturation. The scheme proposed was constructed by combining backstepping technique and “minimum learning parameter” technique, so the computational burden of the algorithm could be reduced drastically and the algorithm is convenient to be implemented in applications. A stability analysis which is subject to the effect of input saturation constrains is conducted with the help of an auxiliary design system. The proposed NN based controller guarantees that all the closeloop signals are uniform ultimate bounded (UUB) and the output of system converges to a small neighborhood of the desired trajectory. Numerical simulations illustrate the effectiveness of the proposed algorithm.
关键词
船舶 /
航向控制 /
输入饱和 /
Backstepping /
神经网络 /
最少学习参数
Key words
ship /
course control /
input saturation /
Backstepping /
neural network /
minimum learning parameter
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基金
浙江省教育厅科研项目（Y201329826）
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