Because the problem of machining non-circle pin-hole, a giant magnetostrictive smart component is proposed. The intrinsic hysteresis observed in giant magnetostrictive material has impaired the motion accuracy. The GMM(Giant Magnetostrictive Material) smart component hysteresis has the nature that its relationship between output and input of smart component is one-to-two mapping and its output varies from the different frequency input. A new kind of architecture of neural network is proposed to approximate the smart components dynamic hysteretic characteristics. The smart component precision control is realized by combining the neural network created inverse model and a Proportional Derivative (PD) feedback controller. Simulation shows that this control strategy can eliminate the hysteretic nonlinear impact and achieve the precision control of the smart component.%由于超磁致伸缩材料(GMM)内在的迟滞特性会引起智能构件的定位误差,并且其迟滞现象具有输入和输出一对多,输出随输入频率变化的特点,提出一种基于神经网络实现GMM智能构件动态迟滞建模方法.通过所建立神经网络实现GMM智能构件逆迟滞模型,结合PD反馈控制器,实现智能构件的实时精密位移控制.在Matlab平台上进行仿真,结果表明所建立控制策略能消除GMM智能构件迟滞非线性的影响,实现了GMM智能构件的精密位移控制目的.
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