陀螺稳定平台神经网络滑模变结构控制  

Neural Network Sliding Mode Variable Structure Control for Gyro Stabilized Platform

作　　者：鲁延娟 李明秋[1] LU Yah-juan;LI Ming-qiu(Changchun University of Science and Technology,Changchun 130022,China)

机构地区：[1]长春理工大学,长春130022

出　　处：《电光与控制》2018年第10期39-42,共4页Electronics Optics & Control

摘　　要：主要针对陀螺稳定平台的单轴控制系统的快速性和鲁棒性进行研究。采用滑模变结构与神经网络结合的方法对陀螺稳定平台进行控制。由于传统滑模变结构方法的模型估计不准确,会导致抖振问题。提出了采用RBF神经网络逼近模型未知部分的神经网络滑模变结构控制方法。实验证明,采用该方法可以有效提高系统的快速性和鲁棒性。针对陀螺稳定平台单轴控制系统,该方法对角位置跟踪的时间为0.3 s,角速度跟踪时间为0.6 s。其中,角位置跟踪的最大误差为0.007 3,且在1 s时,误差收敛到零的一个邻域内。This paper mainly studies the speed and robustness of the single-axis control system of the gyro stabilized platform. The sliding-mode variable structure is combined with neural network for controlling the gyro stabilized platform. Aiming at the chattering problem of traditional sliding mode variable structure method due to inaccurate model estimation, a neural network sliding mode variable structure control method is proposed using RBF neural network to approximate the unknown part of the model. Experiments show that using this method can effectively improve the system＇s speed and robustness. For the uniaxial control system of the gyro stabilized platform, the method can implement the angle tracking in O. 3 s, and the speed tracking in 0.6 s ; the maximum error of angle tracking is 0. 007 3, and the error converges to a neighbourhood of zero in 1 s.

关 键 词：陀螺稳定平台 速度稳定环 滑模变结构 RBF神经网络 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]