Feed-forward Control Based on a Neural Network Technology for an Optimal Slewing of the Flexible Spacecraft

Zakrzhevskii A

doi:10.35840/2631-5009/7518

Abstract

The problem of construction of optimal program slewing of spacecraft with attached flexible elements in the class of slewing around Eulerian axis is considered. The mathematical model is developed, allowing considering a finite number of natural modes of flexible vibrations. The problem of optimal reorientation of spacecraft with nontraditional objective of control, reducing to a minimum dynamic overload of flexible elements in a relative motion is formulated. The algorithm of numerical solving of the corresponding nonlinear boundary problem, realized in the form of a package of applied programs in FORTRAN is developed. The network whose training can be made during of preflight preparation is created in space of the slewing parameters. Simulation of fast calculation of the parameters of the optimal slewing program in flight is made on the basis of use of known radial basic functions.

Highlights

Interest to motion control of flexible spacecraft (SC) round the mass center, in particular, to a mode of their optimal reorientation, has been increased
Approximation of the vectorial fields determining program laws of control based on a method of neural networks demands essentially less of mathematical operations and can be realized on more simple on-board computers
Using the Principle of maximum, we reduce the problem of the optimal control to the twopoint boundary problem for a non-linear system of 2M = 4(N + 1) ordinary differential equations (ODE) of the first order

Summary

Introduction

Interest to motion control of flexible spacecraft (SC) round the mass center, in particular, to a mode of their optimal reorientation, has been increased. We introduce the frame of reference Oξ1ξ2ξ3 , fixed in the Citation: Zakrzhevskii A (2018) Feed-forward Control Based on a Neural Network Technology for an Optimal Slewing of the Flexible Spacecraft.

Results

Conclusion