基于BP网络的智能控制器在AFM中的应用研究

Application Research of Intelligent Controller Based on BP Network in AFM

原子力显微镜(AFM)作为纳米领域重要的研究工具之一,始终起着举足轻重的作用。目前商用的AFM控制系统一般采用PID控制算法实现对压电陶瓷驱动器的精密控制。但是压电陶瓷具有复杂的非线性特性,传统的PID控制器难以实现精密控制,不仅影响AFM的扫描速度,也影响其测试精度。基于目前AFM控制系统的分析研究,将常规的PID算法通过引入BP网络进行优化,应用于原子力显微镜控制系统中可以使AFM获得自学习的能力,从而增强其系统的实时性以及精确性。用Simulink结合系统函数(S-function)实现了AFM在接触以及轻敲两种工作模式下的过程仿真,为其研究提供方便快捷的第一手资料,并基于此构建了AFM系统的仿真平台,并在该平台上对改进后的PID算法进行了验证,最后通过自制单探针系统进行扫描实验。

Atomic force microscope(AFM)had been playing an important role in the nano-meter field and be one of the important research tools.Currently,commercial AFM control systems used PID control algorithm to control of piezoelectric ceramic actuators precisely.However,the piezoelectric ceramics was difficult to be controlled by traditional PID controller because of the complex nonlinear characteristics,which not only affected the scanning speed of AFM,but also affected its test accuracy.Based on the analysis of the current AFM control system,this research use the con-ventional PI algorithm was introduced into BP network to enable the AFM acquire the ability of self-learning,thus to enhance real-time capability and accuracy.It is achieved by using Simulink combined with S function that the conve-nient first-hand information for the research was provided under contact and tapping working mode simulation;and the AFM system simulation platform was constructed;improved PID algorithm were verified by being combined BP neural network with the platform.At last,homemade single scanning probe system experiment were carried out.