CPC B64G 1/242 (2013.01) [B64G 1/247 (2023.08); G06N 3/08 (2013.01)] | 12 Claims |
1. A low-orbit satellite deorbit control method based on a particle swarm algorithm, comprising:
constructing an objective function according to a position parameter and a perturbation acceleration of each of particles in a particle swarm as well as a preset Lagrangian multiplier and a preset penalty factor;
calculating an objective function value of each particle in the particle swarm based on the objective function, determining an objective fitness of each particle and a population fitness of the particle swarm according to a calculation result, updating the position parameter and velocity of each particle, and obtaining a population optimal fitness at a maximum number of iterations and the position parameters of particles corresponding to the population optimal fitness; and
updating the Lagrangian multiplier and the penalty factor according to the population optimal fitness, obtaining a current number of iterations and an objective deviation value based on the population optimal fitness, comparing the current number of iterations and the objective deviation value with a preset iteration condition and a preset convergence condition, and determining an objective population fitness and an objective position parameter according to a comparison result to obtain an objective deorbit mode,
wherein the step of constructing an objective function according to a position parameter and a perturbation acceleration of each of particles in a particle swarm as well as a preset Lagrangian multiplier and a preset penalty factor further comprises:
obtaining a rate of change of each orbital element over time under the thrust applied by a thruster, and determining a control rate containing current costate variables according to the rate of change;
calculating a thrust vector, a fuel consumption rate, and a perturbation acceleration of the thrust applied by the thruster;
determining a particle position vector according to the current costate variables, and using the particle position vector as the position parameter of each of the particles in the particle swarm; and
performing orbit integration of the control rate based on the position parameter and the perturbation acceleration, and constructing the objective function according to a integration result, the preset Lagrangian multiplier and the preset penalty factor.
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