Stacked recurrent neural network based high precision pointing coupled control of the spacecraft and telescopes

In some space missions especially in the field of space gravitational wave detection, the telescope needs to point to a certain target through attitude movement and pointing control. In several mainstream gravitational wave detection missions, the detector usually consists of a cluster of three identical satellites, flying in a quasi-equilateral triangular formation with a big edge length, so every satellite needs two telescopes to point each other and constitute three giant Michelson-Type interferometers. Therefore, a satellite platform system with two telescopes is researched in this paper. This research helps to characterize the attitude motion of a telescope for space astronomical observation or space gravitational wave detection, provides new method on the telescope's high-precision pointing control. For this purpose, we derive a satellite-telescope coupling attitude model, design the sliding mode controller for satellite and the stacked recurrent neural network adaptive controller for telescope. In the stacked recurrent neural network adaptive controller design, a sliding mode control technology is adopted. In addition, we propose a combinatorial optimization method for network weights in the stacked recurrent neural network training process, that is, the output layer is corrected by the adaptive law, and the correction of other layers adopt the error backpropagation method. Finally, a numerical simulation method verifies the effectiveness of the controller design.