Particle Swarm Deorbit Control for Low-Orbit Satellites

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Solution Overview

Problem

Current methods for low-orbit satellite deorbit control, particularly for large constellations like Starlink, face challenges in accurately and efficiently managing the deorbit process due to limited algorithm implementation and sensitivity to initial values, which can lead to prolonged deorbit durations and increased collision risks in congested orbital spaces.

Innovation Solution

A low-orbit satellite deorbit control method and system based on a particle swarm algorithm, which constructs an objective function using position parameters, perturbation accelerations, Lagrangian multipliers, and penalty factors to optimize thruster control, reducing the likelihood of local optimal solutions and enhancing global convergence, thereby minimizing deorbit duration and fuel consumption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If traditional deorbit control methods are used, then the deorbit process can be implemented, but the deorbit duration is prolonged and the control precision is insufficient

Engineering Contradiction:

Improvedeorbit control precisionVSAvoiddeorbit duration

Core Design Contradiction:

Manufacturing precisionVSDuration of action of moving object

Solution Approach 1:

The patent applies particle swarm optimization algorithm to dynamically adjust control parameters (thrust magnitude and direction) during the deorbit process. The algorithm iteratively optimizes parameters based on fitness functions that evaluate deorbit performance, enabling precise control of the satellite's trajectory and minimizing deorbit duration while ensuring accurate arrival at the target orbit.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces traditional mechanical control methods with an intelligent algorithm-based control system. The particle swarm optimization algorithm substitutes conventional feedback control mechanisms, using computational optimization to determine optimal thrust commands, thereby improving control precision and reducing deorbit time through adaptive parameter adjustment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If particle swarm algorithm is applied to deorbit control, then the search ability and convergence speed are improved, but the computational complexity increases

Engineering Contradiction:

Improveconvergence speedVSAvoidalgorithm complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the deorbit control problem into discrete optimization steps suitable for particle swarm algorithm implementation. The control horizon is divided into multiple time intervals, with the algorithm optimizing thrust parameters for each segment independently while considering overall mission objectives, thereby managing computational complexity through problem decomposition.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements a simplified fitness function that evaluates only the most critical aspects of deorbit performance (primary orbital elements and key constraints) rather than considering all possible parameters. This partial action approach maintains the high convergence speed of the particle swarm algorithm while reducing computational burden by focusing on essential control objectives.

Inventive Principle:

Principle #16Partial or excessive action

3Reliability

If augmented Lagrangian function is used to handle constraints, then the constraint satisfaction is improved, but the calculation amount increases

Engineering Contradiction:

Improveconstraint satisfactionVSAvoidcomputational power

Core Design Contradiction:

ReliabilityVSPower

Solution Approach 1:

The patent employs the augmented Lagrangian method to continuously enforce constraints throughout the optimization process. The algorithm maintains constraint satisfaction by iteratively adjusting Lagrange multipliers and penalty parameters, ensuring that thrust magnitude, direction, and orbital constraints are met at every optimization step while achieving reliable constraint compliance.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The patent dynamically adjusts the penalty parameter in the augmented Lagrangian function during the optimization process. The penalty parameter is increased progressively to strengthen constraint enforcement as the optimization converges, balancing computational efficiency with reliable constraint satisfaction by adapting the computational burden to the optimization stage.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11787567B2Low-orbit satellite deorbit control method and system based on particle swarm algorithm

Publication Date: 2023.10.17 SPACE ENG UNIV

AI summary

The disclosure describes a low-orbit satellite deorbit control method and system based on a particle swarm algorithm. The method includes: 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 penalty factor; determining an objective fitness of each particle and a population fitness of the particle swarm based on the objective function, updating the position parameter and the velocity of each particle, and obtaining a population optimal fitness at a maximum number of iterations; updating the Lagrangian multiplier and the penalty factor according to the population optimal fitness, comparing the objective deviation value with 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.