为了提高自适应光学系统中压电倾斜镜(TTM)的控制精度，提出一种基于神经网络建模对TTM的磁滞非线性进行补偿的方法。实验得到TTM磁滞响应数据后，选用反向传播(BP)神经网络对磁滞特性建模，并通过软件编程模拟磁滞响应过程，进而实时计算控制量，实现对TTM的前馈补偿控制。为了满足自适应光学系统中实时控制的要求，根据BP网络内部运算机理得到BP网络运算的函数表达形式，以函数运算代替耗时的网络仿真运算。仿真结果显示这种替代在保证运算精度的前提下，提高了运算速度。实验结果表明，通过补偿，TTM的磁滞非线性减小约70%，提高了TTM的整体线性度和控制精度。

A method to compensate the hysteresis nonlinearity of piezoelectric steering mirror (tip/tilt mirror, TTM) based on neural network models is presented, which may improve the steering mirror control accuracy in adaptive optics system. The hysteresis of TTM is modeled by using back propagation (BP) neural network via the response data of TTM. We simulate the process of hysteresis response and compute the voltage applied to TTM in real time, so as to achieve the forward compensation control of TTM. In order to meet the real time intention, we use the function form of BP neural network instead of time-consuming simulation operation, which increases the computing speed and insures the operation precision. The results show that by using this compensation approach the hysteresis nonlinearity is reduced by about 70%. Moreover, the global linearity and the controlling accuracy are obviously improved.