基于BP神经网络的三轴增稳云台自抗扰控制  

Active Disturbance Rejection Control of Three-Axis Stabilized Platform Based on BP Neural Network

作　　者：刘欣 罗晓曙[1] 赵书林 LIU Xin;LUO Xiaoshu;ZHAO Shulin(College of Electronic Engineering,Guangxi Normal University,Guilin Guangxi 541004,China;School of Chemistry and Pharmaceutical Sciences,Guangxi Normal University,Guilin Guangxi 541004,China)

机构地区：[1]广西师范大学电子工程学院,广西桂林541004 [2]广西师范大学化学与药学学院,广西桂林541004

出　　处：《广西师范大学学报（自然科学版）》2020年第2期115-120,共6页Journal of Guangxi Normal University:Natural Science Edition

基　　金：广西科技重大专项(AA18118004)。

摘　　要：针对三轴增稳云台伺服系统非线性特性,以及PD控制抗扰能力差,自抗扰控制器由于参数众多而导致整定过程耗时且费力的缺陷,本文利用BP神经网络的全局逼近能力和自我学习能力,将其与自抗扰控制器组成复合控制器,对自抗扰控制器的所有关键参数进行自整定寻优,应用于含Stribeck摩擦模型的三轴增稳云台伺服系统。仿真结果表明:该方法用于自动整定参数可行有效,与PD控制和参数固定的常规自抗扰控制器相比,具有更高的控制精度和更强的抗扰能力,对提高增稳云台的性能具有较好的应用价值。In view of the non-linear characteristics of the three-axis stabilized pan-tilt servo system, the anti-disturbance ability of PD control is poor, and the setting process of the active disturbance rejection control is time-consuming and laborious due to the large number of parameters. By using the global approximation ability and self-learning ability of BP neural network, a composite controller is composed of BP neural network and active disturbance rejection control. All the key parameters of active disturbance rejection control are self-tuned and optimized, which is applied to the three-axis stabilized pan-tilt servo system with Stribeck friction model. The simulation results show that the method is feasible and effective for parameter auto-tuning. Compared with the conventional ADRC with fixed parameters and PD control, it has higher control accuracy and stronger anti-disturbance ability, and has better application value for improving the performance of the stabilized platform.

关 键 词：增稳云台 伺服系统 PD控制 自抗扰控制 BP神经网络 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]