Optimal control method adopting solid micro-thrusters for orbit keeping

Abstract

The invention belongs to the field of optimal control, and discloses an optimal control method adopting solid micro-thrusters for orbit keeping. The method comprises the following steps: initializinga particle swarm algorithm population and setting parameters; for a solid micro-thruster array arranged on a certain micro-nano satellite, according to a control mechanism in which the solid micro-thrusters cannot be repeatedly used, establishing a fitness function targeted at the minimum energy consumption of all the solid micro-thrusters within a certain period; with minimization of the fitnessfunction as an optimization target of the particle swarm algorithm, obtaining the optimal weighted arrays Q and R through optimization models; and substituting the optimal weighted arrays Q and R intothe linear-quadratic control algorithm to calculate and obtain the optimal orbit keeping controller, and substituting the satellite orbit state X(t) at each moment to obtain the corresponding optimalfeedback control force U(t), so that the purpose of orbit keeping is achieved. The method disclosed by the invention has the advantages that blindness and uncertainty in selection of the parameters Qand R can be greatly reduced, and the optimal values of the parameters Q and R matched with given initial satellite orbit conditions can be automatically provided, so that the efficiency is higher, and the control performance is better.

Description

technical field [0001] The invention belongs to the field of optimization control, relates to the orbit keeping control of spacecraft, in particular to an optimization control method for orbit keeping by using a solid micro thruster. Background technique [0002] The role of the satellite propulsion system is to provide the required thrust for satellite orbit transfer and position maintenance, and provide the required torque for attitude control. It is one of the most important subsystems of the spacecraft. Different energy sources and conversion methods determine different propulsion methods, mainly including chemical propulsion, cold air propulsion, electric propulsion, nuclear propulsion, momentum conversion propulsion and non-working medium propulsion. The propellant carrying capacity and effective specific impulse of the propulsion system restrict the life of the spacecraft, and directly determine the success or failure of the mission in deep space exploration. [0003...

AI Technical Summary

Problems solved by technology

In the current LQR control theory, there is no clear theoretical relationship between the selection of the weighting matrix Q and R and the control performance. At present, most of the Q and R matrices are determined by trial and error calculations, which is very dependent on the work experience of the designer. , when the initial conditions are replaced, it is often necessary to find or try new Q and R arrays, which is very cumbersome and complicated to operate

[0008] For the optimal selection method of Q and R arrays in the LQR algorithm, the existing methods use particle swarm and other intelligent search technologies to solve problems outside the field of micro-nano satellite orbit control, see literature [2]: Zhang Yufen, Lu Jiaxuan The journal paper "Research on Optimal Control of Active Suspension Based on Particle Swarm Optimization" published in October 2017 did not take into account the characteristics of the special pulse-type actuator such as the MEMS solid thruster, so it cannot be used for Orbit control design of micro-nano-satellite with solid as thruster as actuator

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