Moving-mass attitude control system based on RBF networks

The ability of moving-mass attitude control system to control a vehicle with three-axial stabilization in intra-atmospheric space is investigated.And general nonlinear motion equations with three internal moving masses are provided.Utilizing on the fast self-learning and adaptive capacities of RBF(radial basis function) networks,use them as the identification of the object under control to tune the PID(proportional integral differential) controller's parameters.The masses are positioned independently to generate modest attitude corrections for the vehicle.Finally,the influence of moving masses' motion on the system performance is discussed and the system stability is analyzed.Simulation results show that the system stabilization can be ensured while effective attitude adjustment for the vehicle is effected and at the same time the system's dynamic performances are improved.