基于迭代学习算法的控制力矩陀螺框架扰动抑制策略研究

Disturbance Torque Minimization Strategy for Control Moment Gyroscopes Based on Iterative Learning Algorithm

卫星的快速机动以及高精度姿态控制,越来越依赖高精度的控制力矩陀螺(CMG)力矩输出,这就需要CMG低速框架能够有效抑制扰动力矩,降低转速波动.对由高速转子动不平衡引起的扰动力矩进行了分析和建模.为了抑制该扰动力矩对低速框架转速稳定性的影响,在传统的低速框架PID双环控制系统上嵌入了迭代学习的控制算法.通过仿真实验验证该控制方法能够有效抑制扰振力矩,大幅度的提高CMG的输出力矩精度.

For high-agility and high-precision attitude maneuver of remote sensing satellites,the control moment gyroscope（ CMG） is expected to supply more accurate torque,which means the disturbance torque acting on its gimbal axis should be minimized to reduce the fluctuation of the gimbal speed. The torque generated by the wheel imbalance is analyzed,and the gimbal system model is built. To improve the gimbal speed stability,an iterative learning control（ ILC） module is inserted into the PID gimbal control system. The proposed control system is evaluated via system simulation,and the results demonstrate that the ILC algorithm can minimize the fluctuating torque effectively,which significantly improves the precision of the CMG output torque.