Attitude Blended Control for Aerospace Vehicle with Lateral Thrusters and Aerodynamic Fins

Abstract

A finite-time blended control strategy is proposed for the reentry phase attitude control of the aerospace vehicle (ASV) based on the neural network, sliding mode control theory and control allocation. Firstly, a finite-time neural networks sliding mode controller is designed based on the attitude model of the ASV in the reentry phase to obtain the virtual control moments which can make the attitude error converge to the equilibrium point in finite time. Secondly, the desired control moments are mapped into the control commands on the aerodynamic deflectors and the reaction control system (RCS) by using the control allocation. Finally, simulation results are provided to demonstrate the effectiveness of the attitude blended control strategy proposed.

Keywords

• neural network
• finite-time control
• control allocation
• reaction control system
• blended control
• aerospace vehicle
• sliding mode control
• simulation