Neural Network based Fault Tolerant LQR Control for Orbital Maneuvering in LEO Satellites using Hall Effect Thrusters

This paper develops a neural network-based fault tolerant LQR control for orbital maneuvering of satellites in low altitude orbit that are subjected to disturbances like earth gravity, atmospheric drag, third body, and solar radiation. The controller is also developed such that it tolerates the effects of additive and effective loss of actuator faults. A neural network is used in the controller to estimate disturbances and faults and decrease their effects. Due to the high efficiency of electric thrusters, they are used widely in LEO (Low Earth Orbit) missions. Therefore, in this paper Hall effect thrusters are used as the actuator. For the maneuvering purpose, the reference orbit parameters are derived from a reference orbital dynamics which is the Kepler dynamics subjected to only the disturbance of gravity. To avoid singularity, the orbital dynamics of six modified elements are used in the control design besides the six classical elements. Then, by the desired orbital parameters from the reference orbit, the relative motion elements are calculated to apply in control laws. By Lyapunov analysis, the updating laws of the weights of the control and neural network are derived and also ultimately boundedness of the error between the nominal orbital elements and the faulty orbital elements is proved. To show the effectiveness of the proposed control, it is applied in the nonlinear Kepler dynamics which is affected by an accurate model of natural disturbances.