Satellite Attitude Tracking Controller Optimization Based on Particle Swarm Optimization

Abstract Parametric design of satellite tracking controller is an essential for almost modern satellites. To avoid selecting parameters by traditional experience, particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of satellite attitude tracking controller, which is a radial basis function neural network based sliding mode controller. The mechanism of sharing information among particles is introduced to obtain solution. Numerical simulation results show that PSO can reach the optimal solution within 20 iterations. By updating the position and velocity of particles to seek solutions, PSO provides strong global search ability and convergent performance.

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