Adaptive Fault-Tolerant Control of Rigid Body Using RBF Neural Networks

In this paper, an adaptive fault-tolerant attitude control problem is presented of rigid body using radial basis function neural network (RBF NN). The faults we considered are that the thrusters of the rigid might partially or totally lose power. The uncertainty of the system produced by the external disturbances, unknown inertia matrix and thrusters failures are approximated by RBF NN. It is proved that the control method can guarantee that all the signals of the closed-loop system are bounded. Simulation results are presented to demonstrate that the controller is available in achieving high attitude control with external disturbances, inertia uncertainty and thrusters failures.