神经网络原理用于控制机动伺服平台

Applied of Neural Network Theory on the Mobile Servo Platform Control

随着雷达技术的日益发展,要求伺服系统控制和管理的对象和控制步骤也日益增多,伺服系统测量的内容和测量精度也日益增多和提高。随之而来的难题是程序控制的关联性和判定条件的辨证性日趋复杂,而且测量对象的动态特性受外界随机性突发干扰的情况时有发生。分析和控制机动伺服平台,一般采用人工神经网络原理。原因是利用神经网络具有处理多模及复杂状态、自适应、联想、容错、记忆、自学习等优点,可以达到优化控制程序、提高装备的使用性、提高可靠性、提高自动化操作的程度。

With the development of the radar technology, there are more and more the control / measurement items and the control procedures of the servo system. And the measurement accuracy requirement becomes higher. This results in some diffieuh problems. For instance, the relevancy of the procedure control and the overall consideration of the discrimination conditions become more and more sophisticated. Moreover, sudden and random disturbance from the environment to the dynamic characteristic of the measurement object often occurs. This paper analyzes and controls the mobile servo platform with the neural network theory. Due to the advantages such as processing of the muhimode complicated states, adaptive, association of thought, tolerance, memory, self-study etc. the neural network can optimize the control procedures, improve the reliability and utility and automatic operation.