基于神经网络的导弹制导控制一体化反演设计

doi:10.7527/S1000-6893.2014.0346

Abstract

Abstract:

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.

Key words: integrated guidance and control, uncertainty, high order sliding mode differentiator, neural network, back-stepping control

CLC Number:

V448.2

ZHOU Jin, LEI Humin, LI Jiong, SHAO Lei. Integrated missile guidance and control design based on neural network and back-stepping control theory[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(5): 1661-1672.

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Integrated missile guidance and control design based on neural network and back-stepping control theory

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