Sharif Digital Repository

The nonlinear model predictive control (MPC) approach is used to control the coupled translational-rotational motion of an all-thruster spacecraft when one of the actuators fails. In order to model the dynamical response of the spacecraft in MPC, instead of direct integration, a neural network (NN) model is utilized. This model is built of a static NN, followed by a dynamic NN. The static NN is used to find the changes of the mapping of “the demanded forces to the thrusters” and “the real torques/forces produced by the remaining thrusters” after the failure occurrence through online training. In this manner, the effect of failed thruster on the dynamics can be found and the need for... 

The problem of controlling an all-thruster spacecraft in the coupled translational-rotational motion in presence of actuators fault and/or failure is investigated in this paper. the nonlinear model predictive control approach is used because of its ability to predict the future behavior of the system. The fault/failure of the thrusters changes the mapping between the commanded forces to the thrusters and actual force/torque generated by the thruster system. Thus, the basic six degree-of-freedom kinetic equations are separated from this mapping and a set of neural networks are trained off-line to learn the kinetic equations. Then, two neural networks are attached to these trained networks in...