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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2018, Vol. 39 ›› Issue (8): 321953-321953.doi: 10.7527/S1000-6893.2018.21953

• Electronics and Electrical Engineering and Control • Previous Articles     Next Articles

Attitude control of near space kinetic kill vehicle based on neural network backtepping control

ZHANG Tao1,2, LI Jiong2, Wang Huaji2, LEI Humin2, YE Jikun2   

  1. 1. Graduate College, Air Force Engineering University, Xi'an 710051, China;
    2. Air and Missile Defense college, Air Force Engineering University, Xi'an 710051, China
  • Received:2017-12-20 Revised:2018-05-28 Online:2018-08-15 Published:2018-05-28
  • Supported by:
    National Natural Science Foundation of China (61773398,61703421)

Abstract: To meet the requirement for fast, accurate and robust attitude control of near space kinetic kill vehicles, an adaptive neural network backstepping attitude controller is designed. First, a model for lateral jet interaction is established, and a three-channel strong coupling model including centroid drift, parameter perturbation and external disturbance is developed. Second, an adaptive neural backstepping attitude controller is designed. Then, to improve control precision, the RBF neural network is adopted to estimate and compensate the uncertainties in each channel, and the neural network learning parameter is fitted as a parameter based on the theory of minimum learning parameters to improve Radial Basis Function (RBF) calculation efficiency and estimation instantaneity. Finally, a PSeudo Rate (PSR) modulator is designed to convert the continuous control law into the pulse control law, so as to realize control of KKV digital variable thrust. The simulation shows that the controller has fast convergence speed, high control precision and robustness to strong disturbance.

Key words: Radial Basis Function (RBF) neural network, lateral jet interaction model, near space kinetic kill vehicle, PSR modulator, backstepping control

CLC Number: