论文部分内容阅读
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。
In order to improve the control accuracy of piezoelectric TTM in adaptive optics system, a method of compensating hysteresis nonlinearity of TTM based on neural network modeling is proposed. After obtaining the TTM hysteresis response data, the backpropagation (BP) neural network was chosen to model the hysteresis characteristics. The hysteresis response process was simulated by software programming, and then the control amount was calculated in real time to realize the feedforward compensation control of TTM. In order to meet the requirements of real-time control in adaptive optics system, the function expression of BP network operation is obtained according to the internal computing mechanism of BP network, and the time-consuming network simulation operation is replaced by the function operation. The simulation results show that this kind of replacement can improve the computing speed under the premise of ensuring the precision of operation. Experimental results show that the hysteresis nonlinearity of TTM is reduced by about 70% by compensation, which improves the overall linearity and control accuracy of TTM.