本文研究了深空环境下三星库仑编队构型重构控制问题.首先考虑外界环境干扰作用(主要以太阳光压为主)和德拜效应影响,推导出精确的三星库仑编队动力学方程.针对库仑编队动力学特性和太阳光压对于编队任务控制精度的影响,设计基于BP神经网络的PID控制方法.PID控制结构简单,稳定性好,BP神经网络具有超强的自主学习和非线性逼近干扰能力,二者有机结合,通过BP神经网络输出最优的PID控制参数组合,改变卫星所带电荷从而改变卫星之间库仑力大小,使编队渐近稳定并按期望距离和构型飞行.仿真结果表明基于BP神经网络PID控制性能明显优于传统PID控制,大大提高了编队控制精度和系统对于外界干扰的鲁棒性.%The problem of reconfiguration control for a three-craft coulomb formation in the deep space is investigated in this paper. First of all, considering external disturbance and Debye effect, the accurate dynamic models of three-craft coulomb formation are established. According to the characteristics of the coulomb formation dynamic model and the effect of solar radiation pressure on control precision, a PID charge feedback controller based on BP neural network is designed. PID control has simple structure and good stability, while BP neural network has abilities of autonomic learning and nonlinear approximation. Connecting PID control with BP neural network, the optimal PID control parameters are outputted to change the charge of satellites in order to change coulomb forces between satellites, which eventually make the formation converge to equilibrium. The simulations show that the controller is accurate and simple which is better than traditional PID control, and exhibits good control robustness.
展开▼
