基于神经网络的导弹制导控制一体化反演设计
收稿日期: 2014-05-07
修回日期: 2014-12-15
网络出版日期: 2014-12-17
基金资助
航空科学基金 (20130196004)
Integrated missile guidance and control design based on neural network and back-stepping control theory
Received date: 2014-05-07
Revised date: 2014-12-15
Online published: 2014-12-17
Supported by
Aeronautical Science Foundation of China (20130196004)
针对制导控制一体化(IGC)模型中的不确定性难以进行估计补偿的问题,提出了基于神经网络的IGC反演设计方法。首先,根据弹目相对运动关系以及导弹自动驾驶仪模型建立了三维空间中的IGC模型。其次,针对由目标机动引起的模型不确定性,提出应用高阶滑模微分器(SMD)对导弹导引头获得的弹目相对运动信息进行微分,从而估计出目标加速度的方法,然后考虑导弹自身由于参数摄动以及未建模动态引起的模型不确定性,应用SMD和神经网络模型进行在线逼近补偿,基于反演控制理论设计了带有SMD和神经网络模型的IGC算法,应用李雅普诺夫稳定性理论对所设计的控制算法进行了稳定性证明。最后,进行了导弹六自由度仿真,验证了所设计控制算法的有效性。
周觐 , 雷虎民 , 李炯 , 邵雷 . 基于神经网络的导弹制导控制一体化反演设计[J]. 航空学报, 2015 , 36(5) : 1661 -1672 . DOI: 10.7527/S1000-6893.2014.0346
In allusion to the problem that the uncertainty in the integrated guidance and control (IGC) model is difficult to estimate and compensate, a novel IGC algorithm is designed based on neural network and back-stepping control theory. Firstly, the three space dimensional IGC model is constructed with the relative motion of the missile and target as well as the missile autopilot model. Secondly, aimed at the model uncertainty caused by the target maneuvers, a novel method is proposed featuring the estimation of target acceleration by introducing the high order sliding mode differentiator (SMD) which differentiates the target and missile relative motion information obtained from the missile active homing seeker. Then, the uncertainties caused by possible parameter perturbations and unmodelled dynamics are considered. With the SMD and neural network performing an online estimation and compensation, a novel IGC algorithm with SMD and neural network is designed based on the back-stepping control. The stability of the algorithm is precisely derived and analyzed. Finally, the missile's six degree of freedom simulation is carried out to verify the effectiveness of the algorithm.
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