基于神经网络算法的单框架控制力矩陀螺系统复合控制

Compound Control of Single Gimbal Control Moment Gyro Based on Neural Network

摘要:

提出一种基于双神经网络的前馈?反馈控制系统，采集单框架控制力矩陀螺系统在不同条件下的运行数据并使用卡尔曼滤波处理，基于神经网络构建了涵盖控制通道与干扰通道信息的虚拟广义被控对象模型，由此设计了神经网络前馈补偿器。实现了前馈补偿器?原反馈控制器的复合控制系统，减少了传统的前馈?反馈复合控制需要精确地获取干扰通道信息的约束，能够克服单框架控制力矩陀螺系统存在的复杂干扰对控制性能的影响。仿真结果表明了方案能够提升系统稳态精度，减小稳态误差，改善控制动态性能。

Abstract:

This paper presents a feedforward-feedback control system based on the double neural network. The operation data of the single gimbal control moment gyro（SGCMG） system under different conditions is collected and processed by Kalman filter. A virtual generalized controlled object model covering the information of the control channel and the disturbance channel is constructed based on neural network， and a neural network feedforward compensator is designed. The composite control system of feedforward compensator and original feedback controller is realized， which reduces the constraint that the traditional feedforward-feedback composite control needs to obtain the disturbance channel information accurately， and can overcome the influence of the complex disturbance existing in the SGCMG system on the control performance. The simulation results show that the scheme can improve the steady-state accuracy of the system， reduce the steady-state error and improve the control dynamic performance.