基于归一化神经网络的航天器自适应姿态跟踪控制

针对以变速控制力矩陀螺（VSCMGs）为姿态控制执行机构的航天器在同时考虑惯性参数和执行机构不确定性情况下的姿态跟踪控制问题，提出了一种基于归一化神经网络的自适应姿态跟踪控制方法。设计一个非线性反馈控制器作为航天器姿态控制的基本控制器，利用归一化神经网络设计补偿控制器，用以在线估计和消除包含系统不确定参数的未知不确定函数的影响，避免了标准自适应控制方法需要进行大量不确定参数估计的缺陷。采用神经网络输入归一化技术，简化了闭环系统复杂的稳定性分析过程。理论分析证明了闭环系统的稳定性和姿态跟踪误差的收敛性。仿真结果表明，所提出的控制方法能满足航天器在惯性参数和执行机构不确定性及外干扰存在情况下的高精度姿态跟踪控制要求。

Adaptive Attitude Tracking Control for Spacecraft Based on Input Normalized Neural Network

To deal with the attitude tracking control problem of spacecraft using a variable speed control moment gyro(VSCMG) cluster as the actuator in the presence of inertia and actuator uncertainties,an adaptive attitude tracking controller is developed based on input normalized neural networks.A nonlinear feedback controller is designed as a baseline attitude controller,and an adaptive input normalized neural network,used to estimate and eliminate the unknown uncertainties is added as a compensator,thus a large n...