Design of the missile attitude control system of the model under a high uncertainty

DOI:	10.11809/bqzbgcxb2023.03.027
Keywords:	RBF neural network; intellisense; sliding mode theory; adaptive control; stability
Abstract:	Aiming at the problem that the control system model is largely uncertain due to the perturbation of aerodynamic parameters and external disturbances during the reentry of hypersonic missiles, this paper proposes a robust adaptive attitude control system design method based on RBF neural network. Firstly, a missile attitude control system model based on sliding mode control theory is established. Secondly, the RBF neural network is used to perform intelligent perception and real time prediction on the unknown items of the system model, and the control error is introduced into the weight updating law of the neural network, which improves the dynamic characteristics of the system and realizes the intelligent adaptive control. Then, the stability of the system is proved by Lyapunov stability theory.Finally, the mathematical simulation of the control method of the two models under uncertainty cases is carried out. The simulation results show that the designed control method has good control performance and strong robustness under a high uncertainty of the model, which verifies the effectiveness of the proposed method.
Published:	2023-03-28
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