Monte-carlo based optimal control strategy through state estimator in autonomous space systems

Abstract

What we research here deals with the dynamics and its kinematics concerning the autonomous space systems through a new control strategy, while a band of parameters uncertainties in connection with disturbances based upon the variations of the thrust vector, center of mass, engine misalignment, moments of inertial and so on are all taken into real consideration. To present the investigated outcomes in such a real situation, the process noise that is related to a set of thrusters and the measurement noise that also is related to a set of sensors are considered to be dealt with through optimal state estimator scheme. There are the double control loops including the inner loop and the outer loop, which are organized based upon a combination of low and high thrusts levels to handle three-axis rotational angles and their rates. The aforementioned thrusts levels in connection with the uncertainties and disturbances are handled through the Monte-Carlo based method to consider the performance of the proposed approach, in a series of experiments. The investigated results show that the performance of the proposed strategy is verified in which the well-known state-dependent Riccati equation based on the three-axis rotational angles and the corresponding angular rates as well as a number of potential benchmarks are considered to be compared in the same conditions as much as possible.