Neural Network-Based Adaptive Backstepping Control for Hypersonic Flight Vehicles with Prescribed Tracking Performance

الملخص EN

An adaptive neural control scheme is proposed for a class of generic hypersonic flight vehicles.

The main advantages of the proposed scheme include the following: (1) a new constraint variable is defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries; (2) RBF NNs are employed to compensate for complex and uncertain terms to solve the problem of controller complexity; (3) only one parameter needs to be updated online at each design step, which significantly reduces the computational burden.

It is proved that all signals of the closed-loop system are uniformly ultimately bounded.

Simulation results are presented to illustrate the effectiveness of the proposed scheme.