The Optimization of Spacecraft Attitude Control Parameters Based on Improved Particle Swarm Algorithm

By analyzing the defects of the traditional flexible spacecraft attitude Proportion Integration Differentiation (PID) control parameter adjustment, this paper presents an improved particle swarm algorithm for PID parameter optimization, in which the particle diversity is maintained by crossover and mutation, and the local research accuracy is enhanced by adjusting learning factors dynamically. The algorithm is applied to the flexible spacecraft attitude control parameter optimization. Through the digital simulation of spacecraft control parameter between the initial and the optimized algorithm verifies that the optimized controller has a dynamic property in a short time, a small amount of overshoot and higher steady accuracy, which has a significance for further engineering application.