Moving-mass attitude control system for spinning vehicles based on neural networks

The ability of a moving-mass attitude control system to control a spinning vehicle was investigated. The general nonlinear equations of motion with n internal moving elements were provided. The coupling influence to the system caused by the relative movement of the moving masses to the vehicle shell was described. For the self-learning and adaptive capacities of the neural network control system, the attitude control scheme based on the neural networks was produced to improve the dynamic performances, which calculated the desired center position of the system mass. In order to generate the modest attitude corrections of the vehicle, the mass position algorithm was presented by using the optimal principle to get the commanded position of each mass to realize the desired change of the system mass center. A nonlinear simulation of the spinning vehicle with two internal moving mass actuators demonstrated the ability of the controller to effectively control the vehicle attitude.