Abstract:

The control of reusable launch vehicle (RLV) is challenging due to the changes in the dynamics as the vehicle flies through large flight envelopes. There are uncertainties and disturbances in the reentry phase of RLV, influencing attitude control performance. Based on radical basis function neural network (RBFNN), an adaptive attitude controller design scheme was presented. Firstly, an RLV control model was developed. The fast and slow loops control system was designed based on time-scale separate theory. Then an RBFNN was implemented to generate the estimation of model uncertainty and disturbance. The adaptive controller based on RBFNN was used to compensate for the effect of the modeling error and disturbance torque. Results show that the control scheme meets the attitude tracking performance requirements. Simulation demonstrates that the RBFNN can estimate the modeling uncertainty and disturbance torque effectively.

Key words:

reusable launch vehicle (RLV), reentry, adaptive control, radical basis function neuralnetwork (RBFNN), disturbance-rejection, uncertainty