基于神经网络的精密运动系统宏动平台辨识

Identification of Macro-Moving Stage in Precision Positioning System Based on Neural Networks

针对精密运动定位系统中宏动平台具有死区与迟滞的复合特性,提出了一种特殊的神经网络结构,将通常用于逼近光滑系统的神经网络模型改进为可以描述非光滑非线性特性的模型,在模型结构中引入一种非光滑激励函数,并引入广义梯度改进麦夸特算法,以用其对精密运动系统的含有非光滑非线性的运动特性进行建模。在所设计的神经网络中,同时也采用了扩展辩识空间方法,首先将迟滞特性的多值映射变为一一映射,而且还证明了采用完备化的算子基对辩识逼近的必要性及其扩展辩识空间的途径。实际辨识结果表明,所提出的建模方法取得了令人满意的结果。

A special neural network with non-smooth activation function is proposed in this paper to approximate the non-smooth nonlinearities such as dead zone coupled with hysteresis in macro-moving stage of precision positioning systems.In order to train the nonsmooth neural network,the generalized gradient is introduced into the Levenberg-Marquardt algorithm to model the behavior of the macro -stage.The expanded input space method is used to transform the multi-value mapping of hysteresis into the one-to-one mappi...