Hybrid electric vehicle BSG system control method based on neural network self-adaptation inversion

The invention discloses a hybrid electric vehicle BSG system control method based on neural network self-adaptation inversion. The method specifically comprises the following steps: (1) using a transducer and a hybrid electric vehicle BSG system as a whole to form a composite controlled object, (2) adopting a neural network to establish an identification model and an inverse control model of the composite controlled object, (3) using the inverse model as an inverse controller which is in series connection in front of the composite controlled object to conduct open-loop control over system dynamic characteristics, (4) adjusting the weight coefficient of the identification model and the weight coefficient of the inverse control model in an on-line mode, and (5) combining the inverse controller and the identification model to form a neural network self-adaptation inverse controller to control the composite controlled object. The hybrid electric vehicle BSG system control method based on the neural network self-adaptation inversion can effectively solve the problem that due to the fact that feedback control is introduced, system instability of a conventional control method can be caused and can achieve control over the hybrid electric vehicle BSG system dynamic characteristics and external disturbance rejection in a separated and independent mode, and the self adaptive ability and the robustness of the system are strengthened.