基于神经网络的导弹变结构制导律

导弹的打击精度容易受干扰的影响.针对这些未知干扰的存在,利用RBF神经网络具有自学习的能力,并结合变结构控制方法的鲁棒性,提出了一种基于RBF神经网络的滑模变结构控制的导弹制导律.利用RBF神经网络对干扰进行在线估计,克服了未知干扰对制导精度的不利影响,并且分析了闭环系统的稳定性.仿真结果表明,RBF神经网络能够很好地估计出干扰,所设计的制导律能够不受干扰的影响,从而快速精确地打击到目标,验证了该制导律的有效性和鲁棒性.

A Variable Structure Guidance Law for Missile Based on Neural Networks

Missile's attacking precision is liable to the influence of unknown disturbance.Aiming at the existence of the unknown disturbance,a sliding mode variable structure guidance law for missile was presented based on neural networks,which combined the self-learning ability of RBF neural networks and the robustness of variable structure control technology.The RBF neural networks were used to estimate the disturbance on line,which overcame the adverse influence of the unknown disturbance.The closed loop system st...