Abstract:

Aiming at the attitude control problem of reusable launch vehicle in complex re-entry environment， and considering the unknown disturbance of atmosphere， the uncertainty of aerodynamic parameter modeling and the possible partial faults of actuator， the controllers of attitude angle loop and angular velocity loop are designed based on incremental backstepping and Radial Basis Function （REF） neural network. Because the neural network has good unknown approximation ability， RBF neural network is used to estimate the high-order term of Taylor expansion and the influence of the above unknown disturbances in the incremental backstepping design process， and compensate them in the control law. The simulation results show that the designed control system can effectively improve the tracking accuracy of instructions under the influence of unknown disturbances， and has less dependence on the modeling of aircraft information， so it has good robust fault tolerance.

Key words: reusable launch vehicle, unknown disturbance, partial failure of actuator, incremental backstepping control, neural network approximation, robust fault tolerant control