基于自适应模糊神经网络辨识的电液伺服系统L2增益设计

为克服电液伺服系统不确定性、非线性、估计误差和干扰等因素对系统稳定性和精度的影响，提出了基于自适应模糊神经网络辨识的电液伺服系统L2增益设计方法。用自适应模糊神经网络在线估计包括系统不确定性和非线性在内的未知动态特性，同时用增益自适应变结构补偿自适应模糊神经网络的估计误差，用系统L2增益设计方法抑制干扰对系统的影响，以期使系统对不确定性和非线性具有鲁棒性，而且从干扰到描述系统跟踪误差的评价函数的L2增益小于指定值。

L2 Gain Design for Hydraulic Servo System Based on Identification by Adaptive Fuzzy Neural Networks

In order to attenuate the effects on stability and accuracy of the system caused by uncertainty,non-linearity,evaluation errors and disturbances in an electro-hydraulic servo system,L2 gain design method of system based on the identification by adaptive fuzzy neural networks(AFNN) was proposed.AFNN was employed to evaluate the unknown dynamic characteristics including uncertainty and non-linearity in system,and GAVSC was used to compensate the evaluation errors at the same time,and L2 gain design method of system was introduced to suppress the effects on system by disturbances.So the system controlled by the proposed algorithm is robust to the system uncertainty and non-linearity,and the system's L2 gain from the disturbance to the evaluation function describing the system track error is smaller than the specified value.