Parameters online identification-based data-driven backstepping control of hypersonic vehicles

The control problem of the hypersonic vehicles is studied in this article. A new control approach is presented. This approach consists of a data-driven dynamic model established by multiple neural networks, an online identification method for system parameters, and a basic backstepping controller. The implementation of this approach requires a dynamic model and system parameters including the moment of inertia and aerodynamic parameters of the hypersonic vehicles. The parameter identification problem is regarded as a dynamic optimization process. The loss function is designed by the Lagrange criterion, and its constraints are determined by the physical and the numerical values. In the case of model mutation, the system parameters identified online are used as the nominal values of the output of the neural network in the data-driven model to adjust the controller through its gradient descent. Simulation comparisons are given to show the effectiveness of the proposed data-driven approach.