Moving-mass attitude control law based on genetic algorithm

The ability of the moving-mass attitude control system was investigated to control a vehicle with three-axial stabilization in intra-atmospheric space. And the general nonlinear motion equations with three internal moving masses were provided. For the fast self-learning and adaptive capacities of radial basis function (RBF) networks, used it as the controller to calculate the moving masses′ position. With the coordination of the control, the masses were positioned independently to generate the modest attitude corrections for the vehicle. At the same time, the weights of the networks were optimized by genetic algorithm (GA).At last, the influence to the system performance caused by the moving masses′ motion was discussed and the system stability was analyzed. Simulation results show that the system stabilization can be satisfied to realize efficiently attitude adjustment for the vehicle, and the dynamic performances of the system are improved.