Moving-Mass Trim Control System Design for Spinning Vehicles

The ability of a moving-mass control system to control the attitudes of a spinning vehicle is investigated. For the self-learning and adaptive abilities of neural networks, the hybrid attitude control scheme is produced to improve the dynamic performances of the complicated nonlinear system. An analysis of the torque disturbances caused by the relative movements of the masses to the vehicle's body reveals that the total movements of the masses are inclined to be minimized to mitigate these coupling effects. And based on the optimal theory, the mass position algorithm, which determines the mass location to realize the offset of the center of mass of system, is designed. A nonlinear eight degree-of-freedom simulation of a spinning vehicle with two internal moving elements demonstrates the abilities of the hybrid attitude control scheme and the mass position algorithm to effectively control the attitudes of spinning vehicles.